This document provides an introduction to high performance computing (HPC) on Amazon Web Services (AWS). It discusses what HPC is and how grids and clusters are commonly used to enable parallel processing. It also outlines a wide range of HPC applications and how they map to AWS features. Key factors that make AWS compelling for HPC are its scalability, agility, ability to enable global collaboration, and cost optimization options. Sample HPC architectures that can be implemented on AWS including grid and cluster computing are also presented.
This document provides an overview of Amazon Web Services (AWS) and its portfolio of cloud computing services. It discusses the breadth and depth of the AWS platform, which includes over 100 services across computing, storage, databases, analytics, mobile, developer tools, management tools, IoT, and enterprise applications. The document also summarizes several core AWS services like Amazon EC2 for virtual servers, Amazon S3 for object storage, Amazon DynamoDB for NoSQL databases, and Amazon RDS for SQL databases. It highlights how AWS services provide scalability, reliability, security, and low costs compared to on-premises infrastructure.
This document discusses architecting genomic data security and compliance when using Amazon Web Services (AWS). It reviews guidelines from agencies like NIH for genomic privacy and security. It describes AWS's shared security responsibilities model where AWS manages the underlying cloud infrastructure but customers are responsible for their own data and access controls in their AWS accounts. The document provides recommendations for how to use AWS services to meet requirements for controlled access genomic datasets from dbGaP and other repositories.
AWS Big Data and Analytics Services Speed Innovation | AWS Public Sector Summ...Amazon Web Services
Data-driven agencies face extreme data integration and analytics challenges. Decades of point solutions have solved specific mission problems while creating valuable data stores. However, these data stores are not integrated and are stored in information silos. AWS's powerful data ingestion and integration services now allow agencies to rapidly store more in data lakes for deeper analytics. Join this discussion on how FAA and other agencies have leveraged AWS data integration and analytic services to optimize and innovate with their previously untapped information silos. Learn More: https://aws.amazon.com/government-education/
Architectures for HPC and HTC Workloads on AWS | AWS Public Sector Summit 2017Amazon Web Services
Researchers and IT professionals using High Performance Computing (HPC) and High Throughput Computing (HTC) need large scale infrastructure in order to move their research forward. Neuroimaging employs a variety of computationally demanding techniques with which to interrogate the structure and function of the living brain. Tara Madhyastha with the University of Washington, Department of Radiology, is demonstrating these methods at scale. This session will provide reference architectures for running your workloads on AWS, enabling you to achieve scale on demand, and reduce your time to science. We will also debunk myths about HPC in the cloud and show techniques for running common on-premises workloads in the cloud. Learn More: https://aws.amazon.com/government-education/
Fortinet Automates Migration onto Layered Secure WorkloadsAmazon Web Services
A primary concern many of today’s organizations is how to securely migrate their data and workloads to the cloud. To mitigate these challenges, multi-layered protection needs to be in place at all points along the path of data: entering, exiting, and within the cloud. Join Fortinet and AWS to learn how you can enable robust and effective security for your AWS Cloud-based applications and services. Fortinet provides a comprehensive security solution for your hybrid workloads, allowing you to effectively secure your workloads with simplified, automated migration.
Join us to learn:
- The best practices for enabling visibility and control against advanced threats
- Identify and enable the right security architecture for your applications and services
- How to protect your data along each step of the migration process
Who should attend: CTOs, CIOs, CISOs, IT Administers, IT Architects and IT Security Engineers
The document provides tips for optimizing costs when using AWS. It recommends replacing upfront capital expenses with low variable costs on AWS and describes how AWS is able to continually lower costs through economies of scale. It then provides 10 specific tips for lowering AWS costs, such as choosing the right instance types, using auto scaling, turning off unused instances, using reserved and spot instances, using appropriate storage classes, offloading from your architecture, using AWS services instead of reinventing capabilities, using consolidated billing, and taking advantage of AWS tools like Trusted Advisor and Cost Explorer.
Businesses are generating more data than ever before.
Doing real time data analytics requires IT infrastructure that often needs to be scaled up quickly and running an on-premise environment in this setting has its limitations.
Organisations often require a massive amount of IT resources to analyse their data and the upfront capital cost can deter them from embarking on these projects.
What’s needed is scalable, agile and secure cloud-based infrastructure at the lowest possible cost so they can spin up servers that support their data analysis projects exactly when they are required. This infrastructure must enable them to create proof-of-concepts quickly and cheaply – to fail fast and move on.
The document discusses Amazon Relational Database Service (Amazon RDS), which allows users to set up and manage relational databases in the cloud. Some key points:
- Amazon RDS provides a managed database service running MySQL, Oracle, SQL Server, PostgreSQL, MariaDB, or Aurora databases. It handles time-consuming administration tasks.
- Databases can be easily launched and scaled up or down as needed. Additional storage, compute power, and throughput can also be provisioned.
- Automated backups provide point-in-time recovery. Multi-AZ deployments provide high availability and durability. Security features include encryption and IAM access control.
This document provides an overview of Amazon Web Services (AWS) and its portfolio of cloud computing services. It discusses the breadth and depth of the AWS platform, which includes over 100 services across computing, storage, databases, analytics, mobile, developer tools, management tools, IoT, and enterprise applications. The document also summarizes several core AWS services like Amazon EC2 for virtual servers, Amazon S3 for object storage, Amazon DynamoDB for NoSQL databases, and Amazon RDS for SQL databases. It highlights how AWS services provide scalability, reliability, security, and low costs compared to on-premises infrastructure.
This document discusses architecting genomic data security and compliance when using Amazon Web Services (AWS). It reviews guidelines from agencies like NIH for genomic privacy and security. It describes AWS's shared security responsibilities model where AWS manages the underlying cloud infrastructure but customers are responsible for their own data and access controls in their AWS accounts. The document provides recommendations for how to use AWS services to meet requirements for controlled access genomic datasets from dbGaP and other repositories.
AWS Big Data and Analytics Services Speed Innovation | AWS Public Sector Summ...Amazon Web Services
Data-driven agencies face extreme data integration and analytics challenges. Decades of point solutions have solved specific mission problems while creating valuable data stores. However, these data stores are not integrated and are stored in information silos. AWS's powerful data ingestion and integration services now allow agencies to rapidly store more in data lakes for deeper analytics. Join this discussion on how FAA and other agencies have leveraged AWS data integration and analytic services to optimize and innovate with their previously untapped information silos. Learn More: https://aws.amazon.com/government-education/
Architectures for HPC and HTC Workloads on AWS | AWS Public Sector Summit 2017Amazon Web Services
Researchers and IT professionals using High Performance Computing (HPC) and High Throughput Computing (HTC) need large scale infrastructure in order to move their research forward. Neuroimaging employs a variety of computationally demanding techniques with which to interrogate the structure and function of the living brain. Tara Madhyastha with the University of Washington, Department of Radiology, is demonstrating these methods at scale. This session will provide reference architectures for running your workloads on AWS, enabling you to achieve scale on demand, and reduce your time to science. We will also debunk myths about HPC in the cloud and show techniques for running common on-premises workloads in the cloud. Learn More: https://aws.amazon.com/government-education/
Fortinet Automates Migration onto Layered Secure WorkloadsAmazon Web Services
A primary concern many of today’s organizations is how to securely migrate their data and workloads to the cloud. To mitigate these challenges, multi-layered protection needs to be in place at all points along the path of data: entering, exiting, and within the cloud. Join Fortinet and AWS to learn how you can enable robust and effective security for your AWS Cloud-based applications and services. Fortinet provides a comprehensive security solution for your hybrid workloads, allowing you to effectively secure your workloads with simplified, automated migration.
Join us to learn:
- The best practices for enabling visibility and control against advanced threats
- Identify and enable the right security architecture for your applications and services
- How to protect your data along each step of the migration process
Who should attend: CTOs, CIOs, CISOs, IT Administers, IT Architects and IT Security Engineers
The document provides tips for optimizing costs when using AWS. It recommends replacing upfront capital expenses with low variable costs on AWS and describes how AWS is able to continually lower costs through economies of scale. It then provides 10 specific tips for lowering AWS costs, such as choosing the right instance types, using auto scaling, turning off unused instances, using reserved and spot instances, using appropriate storage classes, offloading from your architecture, using AWS services instead of reinventing capabilities, using consolidated billing, and taking advantage of AWS tools like Trusted Advisor and Cost Explorer.
Businesses are generating more data than ever before.
Doing real time data analytics requires IT infrastructure that often needs to be scaled up quickly and running an on-premise environment in this setting has its limitations.
Organisations often require a massive amount of IT resources to analyse their data and the upfront capital cost can deter them from embarking on these projects.
What’s needed is scalable, agile and secure cloud-based infrastructure at the lowest possible cost so they can spin up servers that support their data analysis projects exactly when they are required. This infrastructure must enable them to create proof-of-concepts quickly and cheaply – to fail fast and move on.
The document discusses Amazon Relational Database Service (Amazon RDS), which allows users to set up and manage relational databases in the cloud. Some key points:
- Amazon RDS provides a managed database service running MySQL, Oracle, SQL Server, PostgreSQL, MariaDB, or Aurora databases. It handles time-consuming administration tasks.
- Databases can be easily launched and scaled up or down as needed. Additional storage, compute power, and throughput can also be provisioned.
- Automated backups provide point-in-time recovery. Multi-AZ deployments provide high availability and durability. Security features include encryption and IAM access control.
Bursting on-premise analytic workloads to Amazon EMR using AlluxioAlluxio, Inc.
Data Orchestration Summit 2020 organized by Alluxio
https://www.alluxio.io/data-orchestration-summit-2020/
Bursting on-premise analytic workloads to Amazon EMR using Alluxio
Roy Hasson, AWS
About Alluxio: alluxio.io
Engage with the open source community on slack: alluxio.io/slack
This document provides an overview of Amazon Web Services (AWS) and its capabilities. It describes AWS's global consumer and seller businesses as well as its cloud infrastructure business. It then discusses why researchers love using AWS due to benefits like time to science, global accessibility, low costs, security, and elasticity. Popular high-performance computing workloads on AWS are also listed.
Join us for a series of introductory and technical sessions on AWS Big Data solutions. Gain a thorough understanding of what Amazon Web Services offers across the big data lifecycle and learn architectural best practices for applying those solutions to your projects.
We will kick off this technical seminar in the morning with an introduction to the AWS Big Data platform, including a discussion of popular use cases and reference architectures. In the afternoon, we will deep dive into Machine Learning and Streaming Analytics. We will then walk everyone through building your first Big Data application with AWS.
Drug discovery at 2x speed. Faster, more comprehensive testing approval processes. Identifying gene targets in massive sequencing data sets. These goals are ambitious yet attainable, but not without increasing the computational capabilities of today's researchers. While everyone agrees that simply deploying more infrastructure is not the answer, running that work in the cloud is not without challenges. In this talk we will discuss and illustrate elements of those workloads that Cycle Computing's customers have run on AWS, generating vastly better results than would have been attained on traditional infrastructure. We will cover some common problems they encountered, and how they resolved them using Amazon EC2, S3, Glacier, and Cycle's software.
Presenters: Dougal Ballantyne, Business Development, AWS; Rob Futrick, CTO, Cycle Computing
This slide deck talks about Elasticsearch and its features.
When you talk about ELK stack it just means you are talking
about Elasticsearch, Logstash, and Kibana. But when you talk
about Elastic stack, other components such as Beats, X-Pack
are also included with it.
what is the ELK Stack?
ELK vs Elastic stack
What is Elasticsearch used for?
How does Elasticsearch work?
What is an Elasticsearch index?
Shards
Replicas
Nodes
Clusters
What programming languages does Elasticsearch support?
Amazon Elasticsearch, its use cases and benefits
This document discusses using AWS for high performance computing and risk modeling in financial services. It notes the challenges of limited on-premises capacity and inflexible hardware. AWS offers scalable compute resources, different instance types, storage options, and security tools to meet the needs of risk modeling applications. Example compute scenarios for a 1 petaflop cluster on AWS using a mix of reserved and spot instances are provided, with estimated total compute costs of $0.025 and $0.02 per core per hour respectively.
Session Sponsored by Tableau: Transforming Data Into Valuable InsightsAmazon Web Services
Session Sponsored by Tableau: Transforming Data Into Valuable Insights
Want to transform your data into valuable insights that can help make your business more productive, profitable and secure? Come learn about Splunk Cloud which delivers Operational Intelligence as a cloud service, enabling you to gain critical insights from your machine data without the need to manage any infrastructure.
Speaker: Jason Oakes, Sales Consultant, Tableau
This document provides an overview of database scaling strategies on AWS. It begins with a single EC2 instance hosting a full stack application and database. It then progresses through separating components, adding redundancy, implementing sharding and database federation to handle increasing user loads from 1 to over 1 million users. Key strategies discussed include moving to managed database services like RDS, adding read replicas, distributing load with services like S3, CloudFront, DynamoDB and SQS, and splitting databases by function or key using sharding or federation.
AWS re:Invent 2016| HLC301 | Data Science and Healthcare: Running Large Scale...Amazon Web Services
Working with Amazon Web Services “AWS” and 1Strategy, an Advance AWS Consulting partner; the Cambia Health Data Sciences teams have been able to deploy HIPAA compliant and secured AWS Elastic Map Reduce (EMR) data pipelines on the cloud. In this session, we will dive deep into the architectural components of this solution and you will learn how utilizing AWS services has helped Cambia decrease processing time for analytics, increase application flexibility and accelerate speed to production. The second part of the session is going to cover machine learning and its role in reducing cost and improving quality of care. The healthcare community must rely on advanced analytics and machine learning to analyze multiple facets of healthcare data and process it at scale to gain insights on things that matter. You will learn why AWS is a well suited platform for machine learning. We will take you through the steps of building a machine learning model using Amazon ML for a real world problem of predicting patient readmissions.
Visit http:aws.amazon.com/hpc for more information about HPC on AWS.
High Performance Computing (HPC) allows scientists and engineers to solve complex science, engineering, and business problems using applications that require high bandwidth, low latency networking, and very high compute capabilities. AWS allows you to increase the speed of research by running high performance computing in the cloud and to reduce costs by providing Cluster Compute or Cluster GPU servers on-demand without large capital investments. You have access to a full-bisection, high bandwidth network for tightly-coupled, IO-intensive workloads, which enables you to scale out across thousands of cores for throughput-oriented applications.
February 2016 Webinar Series - Introduction to AWS Database Migration ServiceAmazon Web Services
AWS Database Migration Service helps you migrate databases to AWS easily and securely with minimal downtime to the source database. AWS Database Migration Service can be used for both homogeneous and heterogeneous database migrations from on-premise to RDS or EC2 as well as EC2 to RDS.
In this webinar, we will provide an introduction to AWS Database Migration Service and go through the details of how you can use it today for your database migration projects. We will also discuss AWS Schema Conversion Tool that help you convert your database schema and code for cross database (heterogeneous) migrations.
Learning Objectives:
Understand what is AWS Database Migration Service
Learn how to start using AWS Database Migration Service
Understand homogenous and heterogeneous migrations
Learn about AWS Schema Conversions Tool
Who Should Attend:
IT Managers, DBAs, Solution Architects, Engineers and Developers
AWS Summit 2013 | Singapore - Understanding the Total Cost of (Non) Ownership...Amazon Web Services
Explore the financial considerations of owning and operating a traditional data center versus utilizing cloud infrastructure. The session will consider many cost factors which can be overlooked when comparing models, such as provisioning, procurement, training, support contracts and software licensing. Learn how to further reduce your current costs on AWS and improve your spend predictability.
Amazon EC2 Instances, Featuring Performance Optimisation Best PracticesAmazon Web Services
This document provides an overview of Amazon EC2. It discusses the different types of EC2 instances optimized for various workloads like compute, memory, storage and graphics. It also covers key EC2 services like Elastic Block Store, Virtual Private Cloud, Placement Groups, Elastic Load Balancing and Auto Scaling. The document reviews EC2 purchasing options including On-Demand, Reserved and Spot instances. It emphasizes optimizing costs by combining these options based on workload requirements.
Getting Started with Big Data and HPC in the Cloud - August 2015Amazon Web Services
How can you use Big Data to grow your business and discover new opportunities? When organizations effectively capture, analyze, visualize and apply big data insights to their business goals, they differentiate themselves from their competitors and outperform them in terms of operational efficiency and the bottom line. With Amazon Web Services, businesses and researchers can easily fulfill their high performance computing (HPC) requirements with the added benefit of ad-hoc provisioning, pay-as-you-go pricing and faster time-to-results. Join this session to understand how to run HPC applications in AWS cloud, and about different AWS Big Data and Analytics services such as Amazon Elastic MapReduce (Hadoop), Amazon Redshift (Data Warehouse) and Amazon Kinesis (Streaming), when to use them and how they work together.
大數據運算媒體業案例分享 (Big Data Compute Case Sharing for Media Industry)Amazon Web Services
This document discusses big data and analytics on AWS. It defines big data as large, diverse, and growing volumes of data that are difficult to capture, curate, manage and process with traditional database systems. It notes that the majority of data is now unstructured and that data volumes are growing exponentially. The document outlines the AWS big data platform, which supports batch processing, real-time analytics and machine learning. It provides recommendations on which AWS data stores and analytics services to use depending on data type, access patterns, volume and other attributes.
Workload-Aware: Auto-Scaling A new paradigm for Big Data WorkloadsVasu S
Learn more about Workload-Aware-Auto-Scaling-- an alternative architectural approach to Auto-Scaling that is better suited for the Cloud and applications like Hadoop, Spark, and Presto.
qubole.com/resources/white-papers/workload-aware-auto-scaling-qubole
ENT305 Migrating Your Databases to AWS: Deep Dive on Amazon Relational Databa...Amazon Web Services
Amazon RDS allows you to launch an optimally configured, secure and highly available database with just a few clicks. It provides cost-efficient and resizable capacity, automates time-consuming database administration tasks, and provides you with six familiar database engines to choose from: Amazon Aurora, Oracle, Microsoft SQL Server, PostgreSQL, MySQL and MariaDB. In this session, we will take a close look at the capabilities of Amazon RDS and explain how it works. We’ll also discuss the AWS Database Migration Service and AWS Schema Conversion Tool, which help you migrate databases and data warehouses with minimal downtime from on-premises and cloud environments to Amazon RDS and other Amazon services. Gain your freedom from expensive, proprietary databases while providing your applications with the fast performance, scalability, high availability, and compatibility they need.
- AWS SciCo team brings HPC capabilities to researchers through AWS cloud services like EC2, enabling extreme scale and agility at low cost.
- Tools like cfnCluster allow researchers to easily provision HPC clusters in the cloud through infrastructure as code, deploying clusters in minutes that can be customized and scaled on demand.
- Researchers are able to perform large scale computations and process vast amounts of data more cost effectively using AWS spot instances and other pricing models, allowing them to reinvest savings in their core research.
Emind’s Architecture for Enterprise with AWS IntegrationLahav Savir
This document outlines Emind's architecture for integrating enterprise systems with AWS. The key goals are to reduce wait times, enable easy scaling of computational pipelines, and provide access to cloud services. The architecture covers integrating billing, identity management, networking, security, applications, monitoring, and automation between on-premise systems and AWS. It also describes managed services and self-service options.
This document provides instructions for building an HPC cluster on AWS using the cfnCluster tool in 10 minutes. It discusses establishing an AWS account, pulling the cfnCluster source code from GitHub, generating SSH keys, configuring cfnCluster, and other configuration options to consider like using low-cost t2.micro instance types when first experimenting with cfnCluster functionality. The overall process demonstrated allows provisioning an HPC cluster within AWS that includes a head node and auto-scaling compute nodes connected over a 10G network, using CloudFormation templates managed by cfnCluster.
Bursting on-premise analytic workloads to Amazon EMR using AlluxioAlluxio, Inc.
Data Orchestration Summit 2020 organized by Alluxio
https://www.alluxio.io/data-orchestration-summit-2020/
Bursting on-premise analytic workloads to Amazon EMR using Alluxio
Roy Hasson, AWS
About Alluxio: alluxio.io
Engage with the open source community on slack: alluxio.io/slack
This document provides an overview of Amazon Web Services (AWS) and its capabilities. It describes AWS's global consumer and seller businesses as well as its cloud infrastructure business. It then discusses why researchers love using AWS due to benefits like time to science, global accessibility, low costs, security, and elasticity. Popular high-performance computing workloads on AWS are also listed.
Join us for a series of introductory and technical sessions on AWS Big Data solutions. Gain a thorough understanding of what Amazon Web Services offers across the big data lifecycle and learn architectural best practices for applying those solutions to your projects.
We will kick off this technical seminar in the morning with an introduction to the AWS Big Data platform, including a discussion of popular use cases and reference architectures. In the afternoon, we will deep dive into Machine Learning and Streaming Analytics. We will then walk everyone through building your first Big Data application with AWS.
Drug discovery at 2x speed. Faster, more comprehensive testing approval processes. Identifying gene targets in massive sequencing data sets. These goals are ambitious yet attainable, but not without increasing the computational capabilities of today's researchers. While everyone agrees that simply deploying more infrastructure is not the answer, running that work in the cloud is not without challenges. In this talk we will discuss and illustrate elements of those workloads that Cycle Computing's customers have run on AWS, generating vastly better results than would have been attained on traditional infrastructure. We will cover some common problems they encountered, and how they resolved them using Amazon EC2, S3, Glacier, and Cycle's software.
Presenters: Dougal Ballantyne, Business Development, AWS; Rob Futrick, CTO, Cycle Computing
This slide deck talks about Elasticsearch and its features.
When you talk about ELK stack it just means you are talking
about Elasticsearch, Logstash, and Kibana. But when you talk
about Elastic stack, other components such as Beats, X-Pack
are also included with it.
what is the ELK Stack?
ELK vs Elastic stack
What is Elasticsearch used for?
How does Elasticsearch work?
What is an Elasticsearch index?
Shards
Replicas
Nodes
Clusters
What programming languages does Elasticsearch support?
Amazon Elasticsearch, its use cases and benefits
This document discusses using AWS for high performance computing and risk modeling in financial services. It notes the challenges of limited on-premises capacity and inflexible hardware. AWS offers scalable compute resources, different instance types, storage options, and security tools to meet the needs of risk modeling applications. Example compute scenarios for a 1 petaflop cluster on AWS using a mix of reserved and spot instances are provided, with estimated total compute costs of $0.025 and $0.02 per core per hour respectively.
Session Sponsored by Tableau: Transforming Data Into Valuable InsightsAmazon Web Services
Session Sponsored by Tableau: Transforming Data Into Valuable Insights
Want to transform your data into valuable insights that can help make your business more productive, profitable and secure? Come learn about Splunk Cloud which delivers Operational Intelligence as a cloud service, enabling you to gain critical insights from your machine data without the need to manage any infrastructure.
Speaker: Jason Oakes, Sales Consultant, Tableau
This document provides an overview of database scaling strategies on AWS. It begins with a single EC2 instance hosting a full stack application and database. It then progresses through separating components, adding redundancy, implementing sharding and database federation to handle increasing user loads from 1 to over 1 million users. Key strategies discussed include moving to managed database services like RDS, adding read replicas, distributing load with services like S3, CloudFront, DynamoDB and SQS, and splitting databases by function or key using sharding or federation.
AWS re:Invent 2016| HLC301 | Data Science and Healthcare: Running Large Scale...Amazon Web Services
Working with Amazon Web Services “AWS” and 1Strategy, an Advance AWS Consulting partner; the Cambia Health Data Sciences teams have been able to deploy HIPAA compliant and secured AWS Elastic Map Reduce (EMR) data pipelines on the cloud. In this session, we will dive deep into the architectural components of this solution and you will learn how utilizing AWS services has helped Cambia decrease processing time for analytics, increase application flexibility and accelerate speed to production. The second part of the session is going to cover machine learning and its role in reducing cost and improving quality of care. The healthcare community must rely on advanced analytics and machine learning to analyze multiple facets of healthcare data and process it at scale to gain insights on things that matter. You will learn why AWS is a well suited platform for machine learning. We will take you through the steps of building a machine learning model using Amazon ML for a real world problem of predicting patient readmissions.
Visit http:aws.amazon.com/hpc for more information about HPC on AWS.
High Performance Computing (HPC) allows scientists and engineers to solve complex science, engineering, and business problems using applications that require high bandwidth, low latency networking, and very high compute capabilities. AWS allows you to increase the speed of research by running high performance computing in the cloud and to reduce costs by providing Cluster Compute or Cluster GPU servers on-demand without large capital investments. You have access to a full-bisection, high bandwidth network for tightly-coupled, IO-intensive workloads, which enables you to scale out across thousands of cores for throughput-oriented applications.
February 2016 Webinar Series - Introduction to AWS Database Migration ServiceAmazon Web Services
AWS Database Migration Service helps you migrate databases to AWS easily and securely with minimal downtime to the source database. AWS Database Migration Service can be used for both homogeneous and heterogeneous database migrations from on-premise to RDS or EC2 as well as EC2 to RDS.
In this webinar, we will provide an introduction to AWS Database Migration Service and go through the details of how you can use it today for your database migration projects. We will also discuss AWS Schema Conversion Tool that help you convert your database schema and code for cross database (heterogeneous) migrations.
Learning Objectives:
Understand what is AWS Database Migration Service
Learn how to start using AWS Database Migration Service
Understand homogenous and heterogeneous migrations
Learn about AWS Schema Conversions Tool
Who Should Attend:
IT Managers, DBAs, Solution Architects, Engineers and Developers
AWS Summit 2013 | Singapore - Understanding the Total Cost of (Non) Ownership...Amazon Web Services
Explore the financial considerations of owning and operating a traditional data center versus utilizing cloud infrastructure. The session will consider many cost factors which can be overlooked when comparing models, such as provisioning, procurement, training, support contracts and software licensing. Learn how to further reduce your current costs on AWS and improve your spend predictability.
Amazon EC2 Instances, Featuring Performance Optimisation Best PracticesAmazon Web Services
This document provides an overview of Amazon EC2. It discusses the different types of EC2 instances optimized for various workloads like compute, memory, storage and graphics. It also covers key EC2 services like Elastic Block Store, Virtual Private Cloud, Placement Groups, Elastic Load Balancing and Auto Scaling. The document reviews EC2 purchasing options including On-Demand, Reserved and Spot instances. It emphasizes optimizing costs by combining these options based on workload requirements.
Getting Started with Big Data and HPC in the Cloud - August 2015Amazon Web Services
How can you use Big Data to grow your business and discover new opportunities? When organizations effectively capture, analyze, visualize and apply big data insights to their business goals, they differentiate themselves from their competitors and outperform them in terms of operational efficiency and the bottom line. With Amazon Web Services, businesses and researchers can easily fulfill their high performance computing (HPC) requirements with the added benefit of ad-hoc provisioning, pay-as-you-go pricing and faster time-to-results. Join this session to understand how to run HPC applications in AWS cloud, and about different AWS Big Data and Analytics services such as Amazon Elastic MapReduce (Hadoop), Amazon Redshift (Data Warehouse) and Amazon Kinesis (Streaming), when to use them and how they work together.
大數據運算媒體業案例分享 (Big Data Compute Case Sharing for Media Industry)Amazon Web Services
This document discusses big data and analytics on AWS. It defines big data as large, diverse, and growing volumes of data that are difficult to capture, curate, manage and process with traditional database systems. It notes that the majority of data is now unstructured and that data volumes are growing exponentially. The document outlines the AWS big data platform, which supports batch processing, real-time analytics and machine learning. It provides recommendations on which AWS data stores and analytics services to use depending on data type, access patterns, volume and other attributes.
Workload-Aware: Auto-Scaling A new paradigm for Big Data WorkloadsVasu S
Learn more about Workload-Aware-Auto-Scaling-- an alternative architectural approach to Auto-Scaling that is better suited for the Cloud and applications like Hadoop, Spark, and Presto.
qubole.com/resources/white-papers/workload-aware-auto-scaling-qubole
ENT305 Migrating Your Databases to AWS: Deep Dive on Amazon Relational Databa...Amazon Web Services
Amazon RDS allows you to launch an optimally configured, secure and highly available database with just a few clicks. It provides cost-efficient and resizable capacity, automates time-consuming database administration tasks, and provides you with six familiar database engines to choose from: Amazon Aurora, Oracle, Microsoft SQL Server, PostgreSQL, MySQL and MariaDB. In this session, we will take a close look at the capabilities of Amazon RDS and explain how it works. We’ll also discuss the AWS Database Migration Service and AWS Schema Conversion Tool, which help you migrate databases and data warehouses with minimal downtime from on-premises and cloud environments to Amazon RDS and other Amazon services. Gain your freedom from expensive, proprietary databases while providing your applications with the fast performance, scalability, high availability, and compatibility they need.
- AWS SciCo team brings HPC capabilities to researchers through AWS cloud services like EC2, enabling extreme scale and agility at low cost.
- Tools like cfnCluster allow researchers to easily provision HPC clusters in the cloud through infrastructure as code, deploying clusters in minutes that can be customized and scaled on demand.
- Researchers are able to perform large scale computations and process vast amounts of data more cost effectively using AWS spot instances and other pricing models, allowing them to reinvest savings in their core research.
Emind’s Architecture for Enterprise with AWS IntegrationLahav Savir
This document outlines Emind's architecture for integrating enterprise systems with AWS. The key goals are to reduce wait times, enable easy scaling of computational pipelines, and provide access to cloud services. The architecture covers integrating billing, identity management, networking, security, applications, monitoring, and automation between on-premise systems and AWS. It also describes managed services and self-service options.
This document provides instructions for building an HPC cluster on AWS using the cfnCluster tool in 10 minutes. It discusses establishing an AWS account, pulling the cfnCluster source code from GitHub, generating SSH keys, configuring cfnCluster, and other configuration options to consider like using low-cost t2.micro instance types when first experimenting with cfnCluster functionality. The overall process demonstrated allows provisioning an HPC cluster within AWS that includes a head node and auto-scaling compute nodes connected over a 10G network, using CloudFormation templates managed by cfnCluster.
This document provides an overview of how Amazon Web Services (AWS) can be used for high-performance computing (HPC). It discusses how AWS's large scale and flexibility allows users to provision resources on demand and pay only for what they use. Tools like AWS CloudFormation and cfnCluster make it easy to provision HPC clusters in AWS that utilize services like EC2, EBS, and VPC.
AWS Educate is Amazon's global initiative to accelerate cloud learning for educational institutions, educators, and students. It provides four pillars of support: labs and open course content on AWS products and cloud topics, grants for free AWS services, educator collaboration tools, and communities for sharing best practices. The program is self-service, automated, and available globally in English. Over 1,300 educators and 380 institutions from over 50 countries have already joined, along with over 9,400 students. The initiative aims to graduate students ready for cloud-enabled careers.
El ensayo analiza la definición de constitución según Ferdinand Lassalle. Lassalle argumenta que una constitución es la suma de los factores reales de poder que rigen un estado, aunque estas fuerzas a menudo se omiten en las constituciones escritas. Explica que una constitución debe reflejar los intereses de todos los miembros de una sociedad al establecer los poderes del estado y los derechos de los ciudadanos. Concluye que una constitución es más que una simple ley escrita, sino la ley fundamental que debe organizarse con
Este documento describe los programas de mantenimiento predictivo, incluyendo su definición, ventajas, técnicas, desventajas y pasos para implementar un programa efectivo. Explica que el mantenimiento predictivo se basa en determinar el estado de la maquinaria en operación para detectar síntomas de fallas incipientes. Algunas técnicas incluyen análisis de aceite, vibraciones y temperaturas. Implementar un programa de mantenimiento predictivo puede incrementar la confiabilidad de la maquinaria y reducir costos.
IEEE Paper - A Study Of Cloud Computing Environments For High Performance App...Angela Williams
This document discusses cloud computing environments for high performance applications. It begins with an introduction to high performance computing and how cloud computing can provide scalable resources for HPC applications at a lower cost compared to traditional on-premise HPC systems. It then discusses different types of HPC applications and their requirements in more detail. The document also examines cluster-based HPC systems and Google's architecture for HPC in the cloud. It provides a performance analysis of several HPC cloud vendors and concludes with case studies of running HPC applications in the cloud.
This document provides an overview of cloud computing from researchers at UC Berkeley. It defines cloud computing as both software delivered as a service over the internet (SaaS) and the hardware/software in datacenters providing those services (clouds). The researchers argue that large, low-cost datacenters enabled cloud computing by lowering costs through economies of scale and statistical multiplexing. They classify current cloud offerings and discuss when utility computing is preferable to private clouds. The document identifies top obstacles to cloud computing growth and opportunities to overcome them.
Unit 3 covers cloud services and providers, including compute, database, storage, networking, security, identity, and compliance services. It also compares major cloud providers like Amazon Web Services, Google Cloud, and Microsoft Azure. Choosing a cloud provider involves considering cost, capabilities, trust, security, and open ecosystem factors. Compute resources in the cloud include CPU, memory, storage, and networking that can be dynamically provisioned on demand.
This document provides an overview of cloud computing from researchers at UC Berkeley. It defines cloud computing as both software delivered as a service over the internet (SaaS) and the hardware/software in data centers that provide those services. When data center resources are provided on a pay-as-you-go basis to the public, it is considered utility computing or a public cloud. Private clouds refer to internal company data centers not available publicly. The researchers argue that large-scale commodity data centers offering resources at low costs have enabled cloud computing to provide services cheaper than medium-sized private data centers. They also discuss technical and business challenges and opportunities related to cloud computing.
BUILDING A PRIVATE HPC CLOUD FOR COMPUTE AND DATA-INTENSIVE APPLICATIONSijccsa
Traditional HPC (High Performance Computing) clusters are best suited for well-formed calculations. The
orderly batch-oriented HPC cluster offers maximal potential for performance per application, but limits
resource efficiency and user flexibility. An HPC cloud can host multiple virtual HPC clusters, giving the
scientists unprecedented flexibility for research and development. With the proper incentive model,
resource efficiency will be automatically maximized. In this context, there are three new challenges. The
first is the virtualization overheads. The second is the administrative complexity for scientists to manage
the virtual clusters. The third is the programming model. The existing HPC programming models were
designed for dedicated homogeneous parallel processors. The HPC cloud is typically heterogeneous and
shared. This paper reports on the practice and experiences in building a private HPC cloud using a subset
of a traditional HPC cluster. We report our evaluation criteria using Open Source software, and
performance studies for compute-intensive and data-intensive applications. We also report the design and
implementation of a Puppet-based virtual cluster administration tool called HPCFY. In addition, we show
that even if the overhead of virtualization is present, efficient scalability for virtual clusters can be achieved
by understanding the effects of virtualization overheads on various types of HPC and Big Data workloads.
We aim at providing a detailed experience report to the HPC community, to ease the process of building a
private HPC cloud using Open Source software.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Interventions for scientific and enterprise applications based on high perfor...eSAT Journals
Abstract High performance computing refers to the practice of aggregating computing power in a way that delivers much higher performance than one could get out of a typical desktop computer in order to solve large problems in science, engineering or business. While cloud computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. The scope of HPC is scientific research and engineering as well as the design of enterprise applications. As enterprise applications are data centric, user friendly, complex, scalable and often require software packages, decision support systems, warehouse while scientific applications have need of the availability of a huge number of computers for executing large scale experiments. These needs can be addressed by using high-performance and cloud computing. The goal of HPC is to reduce execution time and accommodate larger and more complicated problems. While cloud computing provides scientists with a completely new model of utilizing the computing infrastructure, computing resources, storage resources, as well as applications can be dynamically provisioned on a pay per use basis. These resources can be released when they are no more needed. This paper focuses on enabling and scaling computing systems to support the execution of scientific and business applications. Keywords: Scientific computing, enterprise applications, cloud computing, high performance computing
Cloud computing is a general term for network-based computing that takes place over the Internet. It provides on-demand access to shared pools of configurable computing resources like networks, servers, storage, applications, and services. Key characteristics include pay-as-you-go pricing, ubiquitous network access, resource pooling, rapid elasticity, and measured service. Common cloud service models are SaaS, PaaS, and IaaS. While cloud computing provides opportunities to reduce costs and access services from anywhere, challenges relate to security, control, and dependence on third parties.
The document discusses server virtualization and consolidation in enterprise data centers. It notes that many servers are underutilized but some become overloaded during peaks, and server consolidation aims to increase utilization while maintaining performance. Two main virtualization technologies are hypervisor-based (e.g. VMware, Xen) and operating system-level (e.g. OpenVZ, Linux VServer). The document evaluates the performance and scalability of a multi-tier application running on these virtualization platforms under different consolidation scenarios. It also examines the impact on underlying system metrics to understand virtualization overhead.
Comparison of Several IaaS Cloud Computing Platformsijsrd.com
Today, the question is less about whether or not to use Infrastructure as a Services (IaaS), but rather which providers to use. Cloud infrastructure services, known as Infrastructure as a Service (IaaS), are self-service models for accessing, monitoring, and managing remote data center infrastructures, such as compute, storage, networking, and networking services. Instead of having to purchase hardware outright, users can purchase Infrastructure as a Service (IaaS) based on consumption, similar to electricity or other utility billing. Most providers offer the core services of server instances, storage and load balancing. When choosing and evaluating a service, it is important to look at issues around location, resiliency and security as well as the features and cost. In order to evaluate which provider best suits requirements.
This document provides architectural guidance and best practices for building solutions on Amazon Web Services (AWS). It discusses key differences between traditional and cloud computing environments including flexible, scalable capacity, managed services, built-in security, and cost optimization options. The document outlines several design principles for AWS including scalability, using disposable resources instead of fixed servers, automation, loose coupling, leveraging services instead of managing servers directly, database strategies, and optimizing for cost and performance.
The document provides guidance on developing a phased strategy for migrating existing applications to the AWS cloud. It discusses performing an assessment of applications to identify good candidates for migration. This includes understanding dependencies, risks, and compliance needs to classify applications. The strategy involves multiple phases - assessment, proof of concept, migrating data, migrating applications, leveraging the cloud, and optimization. The goal is to identify applications that can benefit from cloud capabilities like scalability, flexibility and cost savings through a structured, phased approach.
The document provides a step-by-step guide for migrating existing applications to the AWS cloud in a phased approach. It discusses performing an assessment of applications to identify good candidates for migration. This includes analyzing dependencies, risks, and security requirements to classify applications. It also recommends building a proof of concept to validate the proposed cloud architecture before full migration. The guide outlines phases for moving data and applications to AWS services and leveraging cloud capabilities once migrated.
The document discusses cloud computing from the perspectives of application developers, quality assurance teams, and enterprises. It provides rationales for why cloud computing can reduce capital expenditures and operational expenditures compared to maintaining their own on-premise hardware and software. The document also summarizes the NIST definition of cloud computing and describes its essential characteristics, service models, and deployment models.
Cloud computing is a general term for network-based computing that takes place over the Internet. It provides on-demand access to shared pools of configurable computing resources like networks, servers, storage, applications, and services. Key characteristics include elasticity, ubiquitous network access, and pay-per-use pricing. Some advantages include lower costs, universal access, automatic updates, and unlimited storage. However, it also requires a constant Internet connection and raises security and data loss concerns.
This document discusses enterprise cloud analytics. It begins by defining cloud computing and its benefits for businesses, including cost savings from a pay-as-you-go model versus owning infrastructure. New cloud analytics services can help businesses better manage big data and applications. The document then describes the architecture of enterprise cloud analytics, including Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Software as a Service (SaaS), Business Process as a Service, and analytics platforms like Cloudera, Hortonworks, and MapR. It emphasizes that cloud analytics allows businesses to easily deploy analytics capabilities to gain insights from large amounts of internal and external data.
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
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Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. It allows users to access technology-based services from the network cloud without knowledge of, expertise with, or control over the underlying technology infrastructure that supports them. Key benefits of cloud computing include lower costs, better scalability and flexibility.
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
Dandelion Hashtable: beyond billion requests per second on a commodity serverAntonios Katsarakis
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/temporal-event-neural-networks-a-more-efficient-alternative-to-the-transformer-a-presentation-from-brainchip/
Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional streaming data, facilitating advancements in object detection, action recognition, speech enhancement and language model/sequence generation. Through the utilization of polynomial-based continuous convolutions, TENNs streamline models, expedite training processes and significantly diminish memory requirements, achieving notable reductions of up to 50x in parameters and 5,000x in energy consumption compared to prevailing methodologies like transformers.
Integration with BrainChip’s Akida neuromorphic hardware IP further enhances TENNs’ capabilities, enabling the realization of highly capable, portable and passively cooled edge devices. This presentation delves into the technical innovations underlying TENNs, presents real-world benchmarks, and elucidates how this cutting-edge approach is positioned to revolutionize edge AI across diverse applications.
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on integration of Salesforce with Bonterra Impact Management.
Interested in deploying an integration with Salesforce for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
Digital Banking in the Cloud: How Citizens Bank Unlocked Their MainframePrecisely
Inconsistent user experience and siloed data, high costs, and changing customer expectations – Citizens Bank was experiencing these challenges while it was attempting to deliver a superior digital banking experience for its clients. Its core banking applications run on the mainframe and Citizens was using legacy utilities to get the critical mainframe data to feed customer-facing channels, like call centers, web, and mobile. Ultimately, this led to higher operating costs (MIPS), delayed response times, and longer time to market.
Ever-changing customer expectations demand more modern digital experiences, and the bank needed to find a solution that could provide real-time data to its customer channels with low latency and operating costs. Join this session to learn how Citizens is leveraging Precisely to replicate mainframe data to its customer channels and deliver on their “modern digital bank” experiences.
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
Skybuffer AI: Advanced Conversational and Generative AI Solution on SAP Busin...Tatiana Kojar
Skybuffer AI, built on the robust SAP Business Technology Platform (SAP BTP), is the latest and most advanced version of our AI development, reaffirming our commitment to delivering top-tier AI solutions. Skybuffer AI harnesses all the innovative capabilities of the SAP BTP in the AI domain, from Conversational AI to cutting-edge Generative AI and Retrieval-Augmented Generation (RAG). It also helps SAP customers safeguard their investments into SAP Conversational AI and ensure a seamless, one-click transition to SAP Business AI.
With Skybuffer AI, various AI models can be integrated into a single communication channel such as Microsoft Teams. This integration empowers business users with insights drawn from SAP backend systems, enterprise documents, and the expansive knowledge of Generative AI. And the best part of it is that it is all managed through our intuitive no-code Action Server interface, requiring no extensive coding knowledge and making the advanced AI accessible to more users.
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-EfficiencyScyllaDB
Freshworks creates AI-boosted business software that helps employees work more efficiently and effectively. Managing data across multiple RDBMS and NoSQL databases was already a challenge at their current scale. To prepare for 10X growth, they knew it was time to rethink their database strategy. Learn how they architected a solution that would simplify scaling while keeping costs under control.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
3. Amazon Web Services – An Introduction to High Performance Computing on AWS August 2015
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Contents
Abstract 4
Introduction 4
What Is HPC? 5
Grids and Clusters 7
A Wide Spectrum of HPC Applications in the Cloud 8
Mapping HPC Applications to AWS Features 10
Loosely Coupled Grid Computing 10
Tightly Coupled HPC 10
Data-Intensive Computing 11
Factors that Make AWS Compelling for HPC 12
Scalability and Agility 12
Global Collaboration and Remote Visualization 13
Reducing or Eliminating Reliance on Job Queues 13
Faster Procurement and Provisioning 14
Sample Architectures 15
Grid Computing in the Cloud 15
Cluster Computing in the Cloud 16
Running Commercial HPC Applications on AWS 17
Security and Governance for HPC 17
World-Class Protection 18
Built-In Security Features 18
Conclusion 20
Contributors 20
Further Reading 21
Notes 22
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Abstract
This paper describes a range of high performance computing (HPC) applications
that are running today on Amazon Web Services (AWS). You will learn best
practices for cloud deployment, for cluster and job management, and for the
management of third-party software. This whitepaper covers HPC use cases that
include highly distributed, highly parallel grid computing applications, as well as
more traditional cluster computing applications that require a high level of node-
to-node communications. We also discuss HPC applications that require access to
various types of high performance data storage.
This whitepaper covers cost optimization. In particular, we describe how you can
leverage Amazon Elastic Compute Cloud (EC2) Spot Instances1 and storage
options such as Amazon Simple Storage Service (S3), Amazon Elastic Block Store
(EBS), and Amazon Glacier for increased performance and significant cost
savings when managing large, scalable HPC workloads.
Introduction
Amazon Web Services (AWS) provides on-demand scalability and elasticity for a
wide variety of computational and data-intensive workloads, including workloads
that represent many of the world’s most challenging computing problems:
engineering simulations, financial risk analyses, molecular dynamics, weather
prediction, and many more. Using the AWS Cloud for high performance
computing enables public and private organizations to make new discoveries,
create more reliable and efficient products, and gain new insights in an
increasingly data-intensive world.
Organizations of all sizes use AWS. Global enterprises use AWS to help manage
and scale their product development and manufacturing efforts, to evaluate
financial risks, and to develop new business insights. Research and academic
institutions use AWS to run calculations and simulations at scales that were
previously impractical, accelerating new discoveries. Innovative startups use
AWS to deploy traditional HPC applications in new and innovative ways,
especially those applications found in science and engineering. AWS also
5. Amazon Web Services – An Introduction to High Performance Computing on AWS August 2015
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provides unique benefits for entirely new categories of applications that take
advantage of the virtually limitless scalability that cloud has to offer.
Using AWS, you can focus on design, simulation, and discovery, instead of
spending time building and maintaining complex IT infrastructures. AWS
provides a range of services: from virtual servers and storage that you can access
on-demand, to higher level computing and data services such as managed
databases and software development and deployment tools. AWS also provides
services that enable cluster automation, monitoring, and governance.
What Is HPC?
One way to think of HPC is to compare HPC requirements to requirements for a
typical server. HPC applications require more processor cores–perhaps vastly
more–than the cores available in a typical single server, and HPC applications
also require larger amounts of memory or higher storage I/O than is found in a
typical server. Most HPC applications today need parallel processing—either by
deploying grids or clusters of standard servers and central processing units
(CPUs) in a scale-out manner, or by creating specialized servers and systems with
unusually high numbers of cores, large amounts of total memory, or high
throughput network connectivity between the servers, and from servers to high-
performance storage. These systems might also include non-traditional compute
processing, for example using graphical processing units (GPUs) or other
accelerators attached to the servers. These specialized HPC systems, when
deployed at large scale, are sometimes referred to as supercomputers.
HPC and supercomputers are often associated with large, government-funded
agencies or with academic institutions. However, most HPC today is in the
commercial sector, in fields such as aerospace, automotive, semiconductor
design, large equipment design and manufacturing, energy exploration, and
financial computing.
HPC is used in other domains in which very large computations—such as fluid
dynamics, electromagnetic simulations, and complex materials analysis—must be
performed to ensure a high level of accuracy and predictability, resulting in
higher quality, and safer, more efficient products. For example, HPC is used to
model the aerodynamics, thermal characteristics, and mechanical properties of
6. Amazon Web Services – An Introduction to High Performance Computing on AWS August 2015
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an automotive subassembly or components to find exactly the right design that
balances efficiency, reliability, cost, and safety, before spending millions of
dollars prototyping a real product.
HPC is also found in domains such as 2D and 3D rendering for media and
entertainment, genomics and proteomics analysis for life sciences and healthcare,
oil and gas reservoir simulation for energy exploration, and design verification
for the semiconductor industry. In the financial sector, HPC is used to perform
institutional liquidity simulations and to predict the future values and risks of
complex investments. In architectural design, HPC is used to model everything
from the structural properties of a building, to the efficiency of its cooling
systems under thousands of different input parameters, resulting in millions of
different simulation scenarios.
HPC platforms have evolved along with the applications they support. In the
early days of HPC, computing and data storage platforms were often purpose-
built and optimized for specific types of applications. For example, in
computational fluid dynamics (CFD) and molecular dynamics (MD), two
dimensional engineering applications are widely used that have very different
needs for CPU densities, amounts and configurations of memory, and node-to-
node interconnects.
Over time, the growing use of HPC in research and in the commercial sector,
particularly in manufacturing, finance, and energy exploration, coupled with a
growing catalog of HPC applications, created a trend toward HPC platforms built
to handle a wider variety of workloads, and these platforms are constructed using
more widely available components. This use of commodity hardware components
characterizes the cluster and grid era of HPC. Clusters and grids continue to be
the dominant methods of deploying HPC in both the commercial and
research/academic sectors. Economies of scale, and the need to centrally manage
HPC resources across large organizations with diverse requirements, have
resulted in the practical reality that widely divergent applications are often run
on the same, shared HPC infrastructure.
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Grids and Clusters
Grid computing and cluster computing are two distinct methods of supporting
HPC parallelism, which enables applications that require more than a single
server. Grid computing and cluster computing using widely available servers and
workstations has been common in HPC for at least two decades, and today they
represent the overwhelming majority of HPC workloads.
When two or more computers are connected and used together to support a
single application, or a workflow consisting of related applications, the connected
system is called a cluster. Cluster management software may be used to monitor
and manage the cluster (for example, to provide shared access to the cluster by
multiple users in different departments) or to manage a shared pool of software
licenses across that same set of users, in compliance with software vendor license
terms.
Clusters are most commonly assembled using the same type of computers and
CPUs, for example a rack of commodity dual or quad socket servers connected
using high-performance network interconnects. An HPC cluster assembled in this
way might be used and optimized for a single persistent application, or it might
be operated as a managed and scheduled resource, in support of a wide range of
HPC applications. A common characteristic of HPC clusters is that they benefit
from locality: HPC clusters are normally constructed to increase the throughput
and minimize the latency of data movement between computing nodes, to data
storage devices, or both.
Grid computing, which is sometimes called high throughput computing (HTC),
differs from cluster computing in at least two ways: locality is not a primary
requirement, and the size of the cluster can grow and shrink dynamically in
response to the cost and availability of resources. Grids can be assembled over a
wide area, perhaps using a heterogeneous collection of server and CPU types, or
by “borrowing” spare computing cycles from otherwise idle machines in an office
environment, or across the Internet.
An extreme example of grid computing is the UC Berkeley SETI@home2
experiment, which uses many thousands of Internet-connected computers in the
search for extraterrestrial intelligence (SETI). SETI@home volunteers participate
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by running a free program that downloads and analyzes radio telescope data as a
background process without interrupting the normal use of the volunteer’s
computer. A similar example of web-scale grid computing is the Stanford
Folding@home3 project, which also uses many thousands of volunteers’
computers to perform molecular-level proteomics simulations useful in cancer
research.
Similar grid computing methods can be used to distribute a computer-aided
design (CAD) 3D rendering job across underutilized computers in an
architectural office environment, thus reducing or eliminating the need to
purchase and deploy a dedicated CAD cluster.
Due to the distributed nature of grid computing, applications deployed in this
manner must be designed for resilience. The unexpected loss of one or more
nodes in the grid must not result in the failure of the entire computing job. Grid
computing applications should also be horizontally scalable, so they can take
advantage of an arbitrary number of connected computers with near-linear
application acceleration.
A Wide Spectrum of HPC Applications in
the Cloud
Demand for HPC continues to grow, driven in large part by ever-increasing
demands for more accurate and faster simulations, for greater insights into ever-
larger datasets, and to meet new regulatory requirements, whether for increased
safety or for reduced financial risk.
The growing demand for HPC, and the time and expense required to deploy and
manage physical HPC infrastructures, has led many HPC users to consider using
AWS, either to augment their existing HPC infrastructure, or to entirely replace
it. There is growing awareness among HPC support organizations—public and
private—that cloud provides near-instant access to computing resources for a
new and broader community of HPC users, and for entirely new types of grid and
cluster applications.
HPC has existed on the cloud since the early days of AWS. Among the first users
of Amazon Elastic Compute Cloud (EC2) were researchers looking for scalable
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and cost-effective solutions to problems ranging from genome analysis in life
sciences, to simulations in high-energy physics, and other computing problems
requiring large numbers of CPU cores for short periods of time. Researchers
quickly discovered that the features and capabilities of AWS were well suited for
creating massively parallel grids of virtual CPUs, on demand. Stochastic
simulations and other “pleasingly parallel” applications, from molecular
modeling to Monte Carlo financial risk analysis, were particularly well suited to
using Amazon EC2 Spot Instances, which allow users to bid on unused EC2
instance capacity at cost savings of up to 90 percent off the normal hourly on-
demand price.
As the capabilities and performance of AWS have continued to advance, the types
of HPC applications that are running on AWS have also evolved, with open
source and commercial software applications being successfully deployed on
AWS across industries, and across application categories.
In addition to the many public sector users of cloud for scalable HPC, commercial
enterprises have also been increasing their use of cloud for HPC, augmenting or
in some cases replacing, their legacy HPC infrastructures.
Pharmaceutical companies, for example, are taking advantage of scalability in the
cloud to accelerate drug discovery by running large-scale computational
chemistry applications. In the manufacturing domain, firms around the world are
successfully deploying third-party and in-house developed applications for
computer aided design (CAD), electronic design automation (EDA), 3D
rendering, and parallel materials simulations. These firms routinely launch
simulation clusters consisting of many thousands of CPU cores, for example to
run thousands or even millions of parallel parametric sweeps.
In the financial services sector, organizations ranging from hedge funds, to global
banks, to independent auditing agencies such as FINRA are using AWS to run
complex financial simulations, to predict future outcomes, and to back-test
proprietary trading algorithms.
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Mapping HPC Applications to AWS
Features
Amazon EC2 provides a wide selection of instance types optimized to fit different
use cases. Instance types comprise varying combinations of CPU, memory,
storage, and networking capacity and give you the flexibility to choose the
appropriate mix of resources for specific HPC applications. AWS also offers a
wide variety of data storage options, and higher-level capabilities for deployment,
cluster automation, and workflow management. To better understand how these
capabilities are used for HPC, we’ll first discuss the broad categories of HPC
applications.
Loosely Coupled Grid Computing
This category of HPC applications is sometimes characterized as high throughput
computing (HTC). Examples include Monte Carlo simulations for financial risk
analysis, materials science for proteomics, and a wide range of applications that
can be distributed across very large numbers of CPU cores or nodes in a grid,
with little dependence on high performance node-to-node interconnect, or on
high performance storage.
These applications are often designed for fault-tolerance, meaning the
application is tolerant of individual nodes being added or removed during the
course of a run. Such applications are ideally suited to Amazon EC2 Spot
Instances, and benefit as well from automation using Auto Scaling4. Customers
with highly scalable applications can choose from many EC2 instance types5.
They can optimize the choice of instance types for the specific compute tasks they
plan to execute or for controlling total costs of completing a large set of batch
tasks over time. Many applications in this category are able to take advantage of
GPU acceleration, using Amazon EC2 G2 instances in combination with
programming methods such as NVIDIA’s CUDA parallel computing platform, or
with OpenCL.
Tightly Coupled HPC
Applications in this category include many of the largest, most established HPC
workloads: example workloads include weather modeling, electromagnetic
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simulations, and computational fluid dynamics. These applications are often
written using the messaging passing interface (MPI) or shared memory
programming models, using libraries such as MPITCH, OpenMP, or other
methods for managing high levels of inter-node communications.
Tightly coupled applications can be deployed effectively on the cloud at small to
medium scale, with a maximum number of cores per job being dependent on the
application and its unique set of requirements, for example to meet the
constraints of packet size, frequency, and latency sensitivity of node-to-node
communications. A significant benefit of running such workloads on AWS is the
ability to scale out to achieve a higher quality of results. For example, an engineer
running electromagnetic simulations could run larger numbers of parametric
sweeps than would otherwise be practical, by using very large numbers of
Amazon EC2 On-Demand or Spot Instances, and using automation to launch
independent and parallel simulation jobs. A further benefit for such an engineer
is using Amazon Simple Storage Service (S3), Amazon DynamoDB, and other
AWS capabilities to aggregate, analyze, and visualize the results.
Amazon EC2 capabilities that help with applications in this category include EC2
placement groups and enhanced networking6, for reduced node-to-node latencies
and consistent network performance, and the availability of GPU instance types,
which can reduce the need to add more computing nodes by offloading highly
parallel computations to the GPU.
Data-Intensive Computing
When grid and cluster HPC workloads such as those described earlier are
combined with large amounts of data, the resulting applications require fast,
reliable access to various types of data storage. Representative HPC applications
in this category include genomics, high-resolution image processing, 3D
animation rendering, mix-signal circuit simulation, seismic processing, and
machine learning, among others.
Note that HPC in this category has similarities to “big data” but has different
goals: big data is used to answer questions you didn’t know to ask, or it is used to
discover correlations and patterns in large and diverse datasets. Examples of big
data include website log analysis, financial fraud detection, consumer sentiment
analysis, and ad placements.
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HPC may also generate or consume very large amounts of data, but HPC
applications most often operate on well-structured data models, for example a 3D
mesh representing a complex physical shape, or the individual frames of an
animated feature film. HPC applications use computing to calculate an answer to
a known question, or to simulate a scenario based on a predefined model, using
predefined sets of inputs. In the domain of semiconductor design, for example,
digital and mixed-signal simulations are often run on large computing clusters,
with many thousands of individual simulation tasks that all require access to
high-performance shared storage. This pattern is also found in life sciences, in
particular genomics workflows such as DNA and RNA sequence assembly and
alignment.
AWS services and features that help HPC users optimize for data-intensive
computing include Amazon S3, Amazon Elastic Block Store (EBS), and
AmazonEC2 instance types such as the I2 instance type, which includes locally
attached solid-state drive (SSD) storage. Solutions also exist for creating high
performance virtual network attached storage (NAS) and network file systems
(NFS) in the cloud, allowing applications running in Amazon EC2 to access high
performance, scalable, cloud-based shared storage resources.
Factors that Make AWS Compelling for
HPC
Scalability and Agility
AWS allows HPC users to scale applications horizontally and vertically to meet
computing demands, eliminating the need for job queues and decreasing the time
to results. Horizontal scalability is provided by the elasticity of Amazon EC2—
additional compute nodes can be added as needed and in an automated manner.
Vertical scalability is provided by the wide range of EC2 instance types, and
through Amazon EC2 features such as placement groups and enhanced
networking.
Automated methods of HPC deployment, including the CfnCluster framework7
developed at AWS help customers get started quickly and benefit from scalability
in the cloud.
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Global Collaboration and Remote Visualization
HPC users deploying on AWS quickly find that that running workloads on the
cloud is not simply a means to doing the same kinds of work as before, at lower
cost. Instead, these customers are seeing that cloud enables a new way for
globally distributed teams to securely collaborate on data, and to manage their
non-HPC needs even more efficiently, including desktop technical applications.
Such collaboration in manufacturing, for example, can include using the cloud as
a secure, globally accessible big data platform for production yield analysis, or
enabling design collaboration using remote 3D graphics. The use of the cloud for
collaboration and visualization allows a subcontractor or remote design team to
view and interact with a simulation model in near real time, without the need to
duplicate and proliferate sensitive design data.
Reducing or Eliminating Reliance on Job Queues
HPC users today are accustomed to using open source or commercial cluster and
job management tools, including job schedulers. In a typical HPC environment,
individual HPC users—researchers, engineers, and analysts who rely on HPC
applications—will submit their jobs to a shared resource using a job queue
submission system, using either the command line or an internal job submission
portal. The submitted job typically includes a script that specifies the applications
to be run and includes other information, such as whether and where data need
to be pre-staged, the number of cores or threads to be allocated to the job, and
possibly the maximum allowable runtime for the job. At this point, the cluster
management software takes over, and it schedules the various incoming jobs,
which may have different priorities, to the cluster resources.
Depending on the mix of jobs being submitted, their inter-dependencies and
priorities, and whether they are optimized for the shared resource, the HPC grid
or cluster may operate at very high or very low levels of effective utilization.
When workloads are highly variable (such as when there is a simultaneous high
demand for simulations from many different groups, or when there are
unexpected high-priority jobs being submitted), the queue wait times for a
centrally managed physical cluster can grow dramatically, resulting in job
completion times that are far in excess of the actual time needed to compete each
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job. Errors in the input scripts, mistakes in setting job parameters, or
unanticipated runtimes can result in additional scheduling complexities and
longer queue wait times due to queue contention.
When running HPC in the AWS Cloud, the problem of queue contention is
eliminated, because every job or every set of related, interdependent jobs can be
provided with its own purpose-built, on-demand cluster. In addition, the on-
demand cluster can be customized for the unique set of applications for which it
is being built. For example, you can configure a cluster with the right ratios of
CPU cores, memory, and local storage. Using the AWS Cloud for HPC
applications means there is less waste of resources and a more efficient use of
HPC spending.
Faster Procurement and Provisioning
Rapid deployment of cloud-based, scalable computing and data storage is
compelling for many organizations, in particular those seeking greater ability to
innovate. HPC in the cloud removes the burden of IT procurement and setup
from computational scientists and from commercial HPC users. The AWS Cloud
allows these HPC users to select and deploy an optimal set of services for their
unique applications, and to pay only for what they use.
The AWS Cloud can be deployed and managed by an individual HPC user, such
as a geophysicist needing to validate a new seismic algorithm at scale using on-
demand resources. Or the AWS Cloud can be deployed and managed by a
corporate IT department, using procedures similar to those used for managing
physical infrastructure. In both cases, a major benefit of using the AWS Cloud is
the speed at which new infrastructure can be brought up and be ready for use,
and the speed at which that same infrastructure can be reduced or eliminated to
save costs. In both cases—scale-up and scale-down—you can commission and
decommission HPC clusters in just minutes, rather than in days or weeks.
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Sample Architectures
Grid Computing in the Cloud
Figure 1: “Loosely-coupled” grid
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Cluster Computing in the Cloud
Figure 2: “Tightly coupled” cluster
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Running Commercial HPC Applications on
AWS
There are many independent software vendors (ISVs) providing innovative
solutions for HPC applications. These ISVs include providers of computer-aided
design (CAD), computer-aided engineering (CAE), electronic design automation
(EDA), and other compute-intensive applications, as well as providers of HPC
middleware, such as cluster management and job scheduling solutions. Providers
of HPC-oriented remote visualization and remote desktop tools are also part of
the HPC software ecosystem, as are providers of libraries and development
software for parallel computing.
In most cases, these third-party software products can run on AWS with little or
no change. By using the features of Amazon Virtual Private Cloud (VPC), HPC
users and HPC support teams can ensure that licensed ISV software is being
operated in a secure and auditable manner, including the use of license servers
and associated logs.
In some cases, it will be necessary to discuss your proposed use of technical
software with the ISV, to ensure compliance with license terms. AWS is available
to help with such discussions, including providing ISVs with deployment
assistance via the AWS Partner Network (APN).
In other cases, the ISV may have alternative distributions of software that are
optimized for use on AWS, or can provide a more fully managed software-as-a-
service (SaaS) alternative to customer-managed cloud deployments.
Security and Governance for HPC
The AWS Cloud infrastructure has been architected to be one of the most flexible
and secured cloud computing environments available today. For HPC
applications, AWS provides an extremely scalable, highly reliable, and secured
platform for the most sensitive applications and data.
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World-Class Protection
With the AWS Cloud, not only are infrastructure headaches removed, but so are
many of the allied security issues.
The AWS virtual infrastructure has been designed to provide optimum
availability while designed for customer privacy and segregation.
For a complete list of all the security measures built into the core AWS Cloud
infrastructure, platforms, and services, please read our “Overview of Security
Processes” whitepaper8.
Built-In Security Features
Not only are your applications and data protected by highly secured facilities and
infrastructure, they’re also protected by extensive network and security
monitoring systems. These systems provide basic but important security
measures such as distributed denial of service (DDoS) protection and password
brute-force detection on AWS Accounts. A discussion of additional security
measures follows.
Secure Access
Customer access points, also called API endpoints, allow secure HTTP access
(HTTPS) so that you can establish secure communication sessions with your
AWS services using Secure Sockets Layer (SSL).
Built-In Firewalls
You can control how accessible your instances are by configuring built-in firewall
rules—from totally public to completely private, or somewhere in between. When
your instances reside within an Amazon Virtual Private Cloud (VPC) subnet, you
can control egress as well as ingress.
Unique Users
AWS Identity and Access Management (IAM) allows you to control the level of
access your own users have to your AWS infrastructure services. With IAM, each
user can have unique security credentials, eliminating the need for shared
passwords, or keys, and allowing the security best practices of role separation and
least privilege.
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Multi-Factor Authentication
AWS provides built-in support for multi-factor authentication (MFA) for use with
AWS accounts as well as individual IAM user accounts.
Private Subnets
Amazon VPC allows you to add another layer of network security to your
instances by creating private subnets and even adding an IPsec VPN tunnel
between your home network and your VPC.
Encrypted Data Storage
Customers can have the data and objects they store in Amazon S3, Amazon
Glacier, Amazon Redshift, and Amazon Relational Database Service (RDS) for
Oracle encrypted automatically using Advanced Encryption Standard (AES) 256,
a secure symmetric-key encryption standard using 256-bit encryption keys.
Direct Connection Option
The AWS Direct Connect service allows you to establish a dedicated network
connection from your premises to AWS. Using industry standard 802.1q VLANs,
this dedicated connection can be partitioned into multiple logical connections to
enable you to access both public and private IP environments within your AWS
Cloud.
Security Logs
AWS CloudTrail provides logs of all user activity within your AWS account. You
can see who performed what actions on each of your AWS resources.
Isolated GovCloud
For customers who require additional measures in order to comply with US ITAR
regulations, AWS provides an entirely separate region called AWS GovCloud (US)
that provides an environment where customers can run ITAR-compliant
applications, and provides special endpoints that utilize only FIPS 140-2
encryption.
AWS Cloud HSM
For customers who must use Hardware Security Module (HSM) appliances for
cryptographic key storage, AWS CloudHSM provides a highly secure and
convenient way to store and manage keys.
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Trusted Advisor
Provided automatically when you sign up for AWS Premium Support, the AWS
Trusted Advisor service is a convenient way for you to see where you could use a
little more security. It monitors AWS resources and alerts you to security
configuration gaps, such as overly permissive access to certain EC2 instance ports
and Amazon S3 storage buckets, minimal use of role segregation using IAM, and
weak password policies.
Because the AWS Cloud infrastructure provides so many built-in security
features, you can simply focus on the security of your guest operating system
(OS) and applications. AWS security engineers and solutions architects have
developed whitepapers and operational checklists to help you select the best
options for your needs, and they recommend security best practices, such as
storing secret keys and passwords in a secure manner and rotating them
frequently.
Conclusion
Cloud computing helps research and academic organizations, government
agencies, and commercial HPC users gain fast access to grid and cluster
computing resources, to achieve results faster and with higher quality, at a
reduced cost relative to traditional HPC infrastructure. The AWS Cloud
transforms previously complex and static HPC infrastructures into highly flexible
and adaptable resources for on-demand or long-term use.
Contributors
The following individuals contributed to this document:
• David Pellerin, principal BDM (HPC), AWS Business Development
• Dougal Ballantyne, solutions architect, Amazon Web Services
• Adam Boeglin, solutions architect, Amazon Web Services
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Further Reading
Get started with HPC in the cloud today by going to aws.amazon.com/hpc.
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Notes
1 http://aws.amazon.com/ec2/purchasing-options/spot-instances/
2 http://setiathome.ssl.berkeley.edu/
3 http://folding.stanford.edu/
4 http://aws.amazon.com/documentation/autoscaling/
5 http://aws.amazon.com/ec2/instance-types/
6 http://docs.aws.amazon.com/AWSEC2/latest/UserGuide/enhanced-
networking.html
7 http://aws.amazon.com/hpc/cfncluster
8 http://d0.awsstatic.com/whitepapers/Security/AWS Security Whitepaper.pdf