The document provides an outline for a lecture on massively parallel computing. It discusses how modeling and simulation problems require high-performance computing and are driving the development of new computing architectures. It mentions some of the world's most powerful supercomputers like Roadrunner and Tianhe-1A. It also discusses how cloud computing, data processing needs, and gaming are contributing to growth in parallel computing. The document outlines how data from science experiments and the web are exploding in size and driving the need for new parallel and distributed solutions.
The Challenges facing Libraries and Imperative Languages from Massively Paral...Jason Hearne-McGuiness
The document discusses challenges related to parallel processing and massive parallel architectures. It covers topics like pipeline processors, multiprocessors, processing in memory architectures like Cyclops and picoChip, and cellular architectures. It also discusses code generation issues that arise from massive parallelism and possible solutions using compilers or libraries.
This document discusses massively parallel architectures and processing in memory (PIM) as ways to overcome the memory wall problem. It describes several PIM and cellular architectures including Cyclops, Gilgamesh, Shamrock, picoChip and DIMES. DIMES is an FPGA implementation of a simplified cellular architecture that was used by Jason McGuiness to test programming approaches. The talk concludes with an invitation for questions.
- IBM's Tivoli Storage Manager (TSM) provides data protection, backup and recovery for both physical and virtual environments.
- TSM 6.4 includes enhancements like incremental 'forever' VMware backups, application-aware Microsoft backups, and SAP HANA support.
- The presentation discusses IBM's strategy to optimize storage infrastructure through virtualization, data reduction, analytics and automation.
Greenplum is the first open source Massively Parallel Processing (MPP) data warehouse, built with over two million lines of code. MPP allows a program to run across multiple processors that each use their own memory and operating system. Greenplum was released under Apache software and differs functionally and architecturally from other open source data systems through its use of MPP to execute complex SQL analytics over large datasets at high speeds. As an open source system, Greenplum assures customers that their software needs will be met long-term.
Massively Parallel Processing with Procedural Python - Pivotal HAWQInMobi Technology
The document discusses massively parallel processing using procedural Python. It describes EMC Corporation and its subsidiaries which provide data storage, virtualization, security, and other software solutions. It also discusses Pivotal's open source contributions and the architecture of its HAWQ database which allows Python user-defined functions to perform parallel operations across clusters.
Real-Time Distributed and Reactive Systems with Apache Kafka and Apache AccumuloJoe Stein
In this talk we will walk through how Apache Kafka and Apache Accumulo can be used together to orchestrate a de-coupled, real-time distributed and reactive request/response system at massive scale. Multiple data pipelines can perform complex operations for each message in parallel at high volumes with low latencies. The final result will be inline with the initiating call. The architecture gains are immense. They allow for the requesting system to receive a response without the need for direct integration with the data pipeline(s) that messages must go through. By utilizing Apache Kafka and Apache Accumulo, these gains sustain at scale and allow for complex operations of different messages to be applied to each response in real-time.
La programmation parallèle est désormais une incontournable solution aux problèmes de performance. Ce n'est pas la seule, mais elle ne peut être ignorée. Les nombreux coeurs et CPUs qui peuplent nos serveurs en sont la preuve.
Elle peut aussi s'utiliser plus souvent qu'on pourrait le penser. Que ce soit pour diminuer les temps de réponse ou augmenter le débit.
Nous vous proposons un état des lieux. Quels sont les usages? Quel est le degré de facilité? Comment se prémunir de la complexité? CPU ou GPU?
À l'aide d'exemples de code, tout ce qui est nécessaire de mettre dans le cartable du développeur vivant dans l'air du temps.
The Challenges facing Libraries and Imperative Languages from Massively Paral...Jason Hearne-McGuiness
The document discusses challenges related to parallel processing and massive parallel architectures. It covers topics like pipeline processors, multiprocessors, processing in memory architectures like Cyclops and picoChip, and cellular architectures. It also discusses code generation issues that arise from massive parallelism and possible solutions using compilers or libraries.
This document discusses massively parallel architectures and processing in memory (PIM) as ways to overcome the memory wall problem. It describes several PIM and cellular architectures including Cyclops, Gilgamesh, Shamrock, picoChip and DIMES. DIMES is an FPGA implementation of a simplified cellular architecture that was used by Jason McGuiness to test programming approaches. The talk concludes with an invitation for questions.
- IBM's Tivoli Storage Manager (TSM) provides data protection, backup and recovery for both physical and virtual environments.
- TSM 6.4 includes enhancements like incremental 'forever' VMware backups, application-aware Microsoft backups, and SAP HANA support.
- The presentation discusses IBM's strategy to optimize storage infrastructure through virtualization, data reduction, analytics and automation.
Greenplum is the first open source Massively Parallel Processing (MPP) data warehouse, built with over two million lines of code. MPP allows a program to run across multiple processors that each use their own memory and operating system. Greenplum was released under Apache software and differs functionally and architecturally from other open source data systems through its use of MPP to execute complex SQL analytics over large datasets at high speeds. As an open source system, Greenplum assures customers that their software needs will be met long-term.
Massively Parallel Processing with Procedural Python - Pivotal HAWQInMobi Technology
The document discusses massively parallel processing using procedural Python. It describes EMC Corporation and its subsidiaries which provide data storage, virtualization, security, and other software solutions. It also discusses Pivotal's open source contributions and the architecture of its HAWQ database which allows Python user-defined functions to perform parallel operations across clusters.
Real-Time Distributed and Reactive Systems with Apache Kafka and Apache AccumuloJoe Stein
In this talk we will walk through how Apache Kafka and Apache Accumulo can be used together to orchestrate a de-coupled, real-time distributed and reactive request/response system at massive scale. Multiple data pipelines can perform complex operations for each message in parallel at high volumes with low latencies. The final result will be inline with the initiating call. The architecture gains are immense. They allow for the requesting system to receive a response without the need for direct integration with the data pipeline(s) that messages must go through. By utilizing Apache Kafka and Apache Accumulo, these gains sustain at scale and allow for complex operations of different messages to be applied to each response in real-time.
La programmation parallèle est désormais une incontournable solution aux problèmes de performance. Ce n'est pas la seule, mais elle ne peut être ignorée. Les nombreux coeurs et CPUs qui peuplent nos serveurs en sont la preuve.
Elle peut aussi s'utiliser plus souvent qu'on pourrait le penser. Que ce soit pour diminuer les temps de réponse ou augmenter le débit.
Nous vous proposons un état des lieux. Quels sont les usages? Quel est le degré de facilité? Comment se prémunir de la complexité? CPU ou GPU?
À l'aide d'exemples de code, tout ce qui est nécessaire de mettre dans le cartable du développeur vivant dans l'air du temps.
"AI" for Blockchain Security (Case Study: Cosmos)npinto
This document discusses preliminary work using machine learning techniques to help improve blockchain security. It outlines initial experiments using a Cosmos SDK simulator to generate test data and identify "bug correlates" that could help predict vulnerabilities. Several bugs were already found in the simulator itself. The goal is to focus compute resources on more interesting test runs likely to produce bugs. This is an encouraging first step in exploring how AI may augment blockchain security testing.
High-Performance Computing Needs Machine Learning... And Vice Versa (NIPS 201...npinto
This document discusses using high-performance computing for machine learning tasks like analyzing large convolutional neural networks for visual object recognition. It proposes running hundreds of thousands of large neural network models in parallel on GPUs to more efficiently search the parameter space, beyond what is normally possible with a single graduate student and model. This high-throughput screening approach aims to identify better performing network architectures through exploring a vast number of possible combinations in the available parameter space.
[Harvard CS264] 16 - Managing Dynamic Parallelism on GPUs: A Case Study of Hi...npinto
The document discusses challenges with parallel programming on GPUs including tasks with statically known data dependences, SIMD divergence, lack of fine-grained synchronization and writeable coherent caches. It also presents performance results for sorting algorithms on different GPU and CPU architectures, with GPUs providing much higher sorting throughput than CPUs. Parallel prefix sum is proposed as a method for allocating work in parallel tasks that require dynamic scheduling or allocation.
[Harvard CS264] 15a - The Onset of Parallelism, Changes in Computer Architect...npinto
The document discusses changes in computer architecture and Microsoft's role in the transition to parallel computing. It notes that computer cores are increasing rapidly and that Microsoft aims to make parallelism accessible to all developers through tools like Visual Studio. It also outlines Microsoft's involvement in GPU computing through technologies like DirectX and efforts to support GPU programming across its software stack.
The document discusses dynamic compilation for massively parallel processors. It describes how execution models provide an interface between programming languages and hardware architectures. Emerging execution models like bulk-synchronous parallel and PTX aim to abstract parallelism on heterogeneous multi-core and many-core processors. The document outlines how dynamic compilers can translate between execution models and target instructions to different core architectures through techniques like thread fusion, vectorization, and subkernel extraction. This bridging of models and architectures through just-in-time compilation helps program entire processors rather than individual cores.
[Harvard CS264] 13 - The R-Stream High-Level Program Transformation Tool / Pr...npinto
The document describes the R-Stream high-level program transformation tool. It provides an overview of R-Stream, walks through the compilation process, and discusses performance results. R-Stream uses the polyhedral model to perform program transformations like loop transformations, fusion, distribution and tiling to optimize for parallelism and locality. It models the target machine and uses this to inform the mapping of operations to resources like GPUs.
[Harvard CS264] 12 - Irregular Parallelism on the GPU: Algorithms and Data St...npinto
The document discusses irregular parallelism on GPUs and presents several algorithms and data structures for handling irregular workloads efficiently in parallel. It covers sparse matrix-vector multiplication using different sparse matrix formats. It also discusses compositing of fragments in parallel and presents a nested data parallel approach. The document describes challenges with parallel hashing and presents a two-level hashing scheme. It analyzes parallel task queues and work stealing techniques for load balancing irregular work. Throughout, it focuses on managing communication in addition to computation for optimal parallel performance.
[Harvard CS264] 11b - Analysis-Driven Performance Optimization with CUDA (Cli...npinto
This document discusses performance optimization of GPU kernels. It outlines analyzing kernels to determine if they are limited by memory bandwidth, instruction throughput, or latency. The profiler can identify limiting factors by comparing memory transactions and instructions issued. Source code modifications for memory-only and math-only versions help analyze memory vs computation balance and latency hiding. The goal is to optimize kernels by addressing their most significant performance limiters.
[Harvard CS264] 11a - Programming the Memory Hierarchy with Sequoia (Mike Bau...npinto
This document discusses performance optimization of GPU kernels. It outlines analyzing kernels to determine if they are limited by memory bandwidth, instruction throughput, or latency. The profiler can identify limiting factors by comparing memory transactions and instructions issued. Source code modifications for memory-only and math-only versions help analyze memory vs computation balance and latency hiding. The goal is to optimize kernels by addressing their most significant performance limiters.
[Harvard CS264] 10b - cl.oquence: High-Level Language Abstractions for Low-Le...npinto
This document summarizes a paper about using high-level programming languages for low-level systems programming. It discusses the needs of scientists and engineers for software that is reliable, high-performance, and customizable. The paper aims to address these needs by exploring features of high-level languages that could enable low-level programming tasks typically done in C/C++, like developing device drivers, operating systems, and embedded systems.
This document outlines Andreas Klockner's presentation on GPU programming in Python using PyOpenCL and PyCUDA. The presentation covers an introduction to OpenCL, programming with PyOpenCL, run-time code generation, and perspectives on GPU programming in Python. OpenCL provides a common programming framework for heterogeneous parallel programming across CPUs, GPUs, and other processors. PyOpenCL and PyCUDA allow GPU programming from Python.
[Harvard CS264] 09 - Machine Learning on Big Data: Lessons Learned from Googl...npinto
Abstract:
Machine learning researchers and practitioners develop computer
algorithms that "improve performance automatically through
experience". At Google, machine learning is applied to solve many
problems, such as prioritizing emails in Gmail, recommending tags for
YouTube videos, and identifying different aspects from online user
reviews. Machine learning on big data, however, is challenging. Some
"simple" machine learning algorithms with quadratic time complexity,
while running fine with hundreds of records, are almost impractical to
use on billions of records.
In this talk, I will describe lessons drawn from various Google
projects on developing large scale machine learning systems. These
systems build on top of Google's computing infrastructure such as GFS
and MapReduce, and attack the scalability problem through massively
parallel algorithms. I will present the design decisions made in
these systems, strategies of scaling and speeding up machine learning
systems on web scale data.
Speaker biography:
Max Lin is a software engineer with Google Research in New York City
office. He is the tech lead of the Google Prediction API, a machine
learning web service in the cloud. Prior to Google, he published
research work on video content analysis, sentiment analysis, machine
learning, and cross-lingual information retrieval. He had a PhD in
Computer Science from Carnegie Mellon University.
Creating cluster 'mycluster' with the following settings:
- Master node: m1.small using ami-fce3c696
- Number of nodes: 1
- Node type: m1.small
- Node AMI: ami-fce3c696
- Storage: EBS volume of size 10 GB
- Security group: mycluster-sg allowing SSH from anywhere
Launching instances...
This may take a few minutes. You can check progress with 'starcluster list'.
When instances have started, SSH will be automatically configured.
You can now ssh to the master with:
starcluster ssh mycluster
Have fun and please let us know if you have
[Harvard CS264] 08b - MapReduce and Hadoop (Zak Stone, Harvard)npinto
This document provides an introduction and overview of Hadoop, an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It outlines what Hadoop is, how its core components MapReduce and HDFS work, advantages like scalability and fault tolerance, disadvantages like complexity, and resources for getting started with Hadoop installations and programming.
This document summarizes an MIT lecture on GPU cluster programming using MPI. It provides administrative details such as homework due dates and project information. It also announces various donations of computing resources for the class, including Amazon AWS credits and a Tesla graphics card for the best project. The lecture outline covers the problem of computations too large for a single CPU, an introduction to MPI, MPI basics, using MPI with CUDA, and other parallel programming approaches.
This document summarizes a lecture on CUDA Ninja Tricks given on March 1st, 2011. The lecture covered scripting GPUs with PyCUDA, meta-programming and RTCG, and a case study in brain-inspired AI. It included sections on why scripting is useful for GPUs, an introduction to GPU scripting with PyCUDA, and a hands-on example of a simple PyCUDA program that defines and runs a CUDA kernel to double the values in a GPU memory array.
[Harvard CS264] 05 - Advanced-level CUDA Programmingnpinto
The document discusses optimizations for memory and communication in massively parallel computing. It recommends caching data in faster shared memory to reduce loads and stores to global device memory. This can improve performance by avoiding non-coalesced global memory accesses. The document provides an example of coalescing writes for a matrix transpose by first loading data into shared memory and then writing columns of the tile to global memory in contiguous addresses.
[Harvard CS264] 04 - Intermediate-level CUDA Programmingnpinto
This document provides an overview and summary of key points from a lecture on massively parallel computing using CUDA. The lecture covers CUDA language and APIs, threading and execution models, memory and communication, tools, and libraries. It discusses the CUDA programming model including host and device code, threads and blocks, and memory allocation and transfers between the host and device. It also summarizes the CUDA runtime and driver APIs for launching kernels and managing devices at different levels of abstraction.
[Harvard CS264] 03 - Introduction to GPU Computing, CUDA Basicsnpinto
1. GPUs have many more cores than CPUs and are very good at processing large blocks of data in parallel.
2. GPUs can provide a significant speedup over CPUs for applications that map well to a data-parallel programming model by harnessing the power of many cores.
3. The throughput-oriented nature of GPUs makes them well-suited for algorithms where the same operation can be performed on many data elements independently.
"AI" for Blockchain Security (Case Study: Cosmos)npinto
This document discusses preliminary work using machine learning techniques to help improve blockchain security. It outlines initial experiments using a Cosmos SDK simulator to generate test data and identify "bug correlates" that could help predict vulnerabilities. Several bugs were already found in the simulator itself. The goal is to focus compute resources on more interesting test runs likely to produce bugs. This is an encouraging first step in exploring how AI may augment blockchain security testing.
High-Performance Computing Needs Machine Learning... And Vice Versa (NIPS 201...npinto
This document discusses using high-performance computing for machine learning tasks like analyzing large convolutional neural networks for visual object recognition. It proposes running hundreds of thousands of large neural network models in parallel on GPUs to more efficiently search the parameter space, beyond what is normally possible with a single graduate student and model. This high-throughput screening approach aims to identify better performing network architectures through exploring a vast number of possible combinations in the available parameter space.
[Harvard CS264] 16 - Managing Dynamic Parallelism on GPUs: A Case Study of Hi...npinto
The document discusses challenges with parallel programming on GPUs including tasks with statically known data dependences, SIMD divergence, lack of fine-grained synchronization and writeable coherent caches. It also presents performance results for sorting algorithms on different GPU and CPU architectures, with GPUs providing much higher sorting throughput than CPUs. Parallel prefix sum is proposed as a method for allocating work in parallel tasks that require dynamic scheduling or allocation.
[Harvard CS264] 15a - The Onset of Parallelism, Changes in Computer Architect...npinto
The document discusses changes in computer architecture and Microsoft's role in the transition to parallel computing. It notes that computer cores are increasing rapidly and that Microsoft aims to make parallelism accessible to all developers through tools like Visual Studio. It also outlines Microsoft's involvement in GPU computing through technologies like DirectX and efforts to support GPU programming across its software stack.
The document discusses dynamic compilation for massively parallel processors. It describes how execution models provide an interface between programming languages and hardware architectures. Emerging execution models like bulk-synchronous parallel and PTX aim to abstract parallelism on heterogeneous multi-core and many-core processors. The document outlines how dynamic compilers can translate between execution models and target instructions to different core architectures through techniques like thread fusion, vectorization, and subkernel extraction. This bridging of models and architectures through just-in-time compilation helps program entire processors rather than individual cores.
[Harvard CS264] 13 - The R-Stream High-Level Program Transformation Tool / Pr...npinto
The document describes the R-Stream high-level program transformation tool. It provides an overview of R-Stream, walks through the compilation process, and discusses performance results. R-Stream uses the polyhedral model to perform program transformations like loop transformations, fusion, distribution and tiling to optimize for parallelism and locality. It models the target machine and uses this to inform the mapping of operations to resources like GPUs.
[Harvard CS264] 12 - Irregular Parallelism on the GPU: Algorithms and Data St...npinto
The document discusses irregular parallelism on GPUs and presents several algorithms and data structures for handling irregular workloads efficiently in parallel. It covers sparse matrix-vector multiplication using different sparse matrix formats. It also discusses compositing of fragments in parallel and presents a nested data parallel approach. The document describes challenges with parallel hashing and presents a two-level hashing scheme. It analyzes parallel task queues and work stealing techniques for load balancing irregular work. Throughout, it focuses on managing communication in addition to computation for optimal parallel performance.
[Harvard CS264] 11b - Analysis-Driven Performance Optimization with CUDA (Cli...npinto
This document discusses performance optimization of GPU kernels. It outlines analyzing kernels to determine if they are limited by memory bandwidth, instruction throughput, or latency. The profiler can identify limiting factors by comparing memory transactions and instructions issued. Source code modifications for memory-only and math-only versions help analyze memory vs computation balance and latency hiding. The goal is to optimize kernels by addressing their most significant performance limiters.
[Harvard CS264] 11a - Programming the Memory Hierarchy with Sequoia (Mike Bau...npinto
This document discusses performance optimization of GPU kernels. It outlines analyzing kernels to determine if they are limited by memory bandwidth, instruction throughput, or latency. The profiler can identify limiting factors by comparing memory transactions and instructions issued. Source code modifications for memory-only and math-only versions help analyze memory vs computation balance and latency hiding. The goal is to optimize kernels by addressing their most significant performance limiters.
[Harvard CS264] 10b - cl.oquence: High-Level Language Abstractions for Low-Le...npinto
This document summarizes a paper about using high-level programming languages for low-level systems programming. It discusses the needs of scientists and engineers for software that is reliable, high-performance, and customizable. The paper aims to address these needs by exploring features of high-level languages that could enable low-level programming tasks typically done in C/C++, like developing device drivers, operating systems, and embedded systems.
This document outlines Andreas Klockner's presentation on GPU programming in Python using PyOpenCL and PyCUDA. The presentation covers an introduction to OpenCL, programming with PyOpenCL, run-time code generation, and perspectives on GPU programming in Python. OpenCL provides a common programming framework for heterogeneous parallel programming across CPUs, GPUs, and other processors. PyOpenCL and PyCUDA allow GPU programming from Python.
[Harvard CS264] 09 - Machine Learning on Big Data: Lessons Learned from Googl...npinto
Abstract:
Machine learning researchers and practitioners develop computer
algorithms that "improve performance automatically through
experience". At Google, machine learning is applied to solve many
problems, such as prioritizing emails in Gmail, recommending tags for
YouTube videos, and identifying different aspects from online user
reviews. Machine learning on big data, however, is challenging. Some
"simple" machine learning algorithms with quadratic time complexity,
while running fine with hundreds of records, are almost impractical to
use on billions of records.
In this talk, I will describe lessons drawn from various Google
projects on developing large scale machine learning systems. These
systems build on top of Google's computing infrastructure such as GFS
and MapReduce, and attack the scalability problem through massively
parallel algorithms. I will present the design decisions made in
these systems, strategies of scaling and speeding up machine learning
systems on web scale data.
Speaker biography:
Max Lin is a software engineer with Google Research in New York City
office. He is the tech lead of the Google Prediction API, a machine
learning web service in the cloud. Prior to Google, he published
research work on video content analysis, sentiment analysis, machine
learning, and cross-lingual information retrieval. He had a PhD in
Computer Science from Carnegie Mellon University.
Creating cluster 'mycluster' with the following settings:
- Master node: m1.small using ami-fce3c696
- Number of nodes: 1
- Node type: m1.small
- Node AMI: ami-fce3c696
- Storage: EBS volume of size 10 GB
- Security group: mycluster-sg allowing SSH from anywhere
Launching instances...
This may take a few minutes. You can check progress with 'starcluster list'.
When instances have started, SSH will be automatically configured.
You can now ssh to the master with:
starcluster ssh mycluster
Have fun and please let us know if you have
[Harvard CS264] 08b - MapReduce and Hadoop (Zak Stone, Harvard)npinto
This document provides an introduction and overview of Hadoop, an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It outlines what Hadoop is, how its core components MapReduce and HDFS work, advantages like scalability and fault tolerance, disadvantages like complexity, and resources for getting started with Hadoop installations and programming.
This document summarizes an MIT lecture on GPU cluster programming using MPI. It provides administrative details such as homework due dates and project information. It also announces various donations of computing resources for the class, including Amazon AWS credits and a Tesla graphics card for the best project. The lecture outline covers the problem of computations too large for a single CPU, an introduction to MPI, MPI basics, using MPI with CUDA, and other parallel programming approaches.
This document summarizes a lecture on CUDA Ninja Tricks given on March 1st, 2011. The lecture covered scripting GPUs with PyCUDA, meta-programming and RTCG, and a case study in brain-inspired AI. It included sections on why scripting is useful for GPUs, an introduction to GPU scripting with PyCUDA, and a hands-on example of a simple PyCUDA program that defines and runs a CUDA kernel to double the values in a GPU memory array.
[Harvard CS264] 05 - Advanced-level CUDA Programmingnpinto
The document discusses optimizations for memory and communication in massively parallel computing. It recommends caching data in faster shared memory to reduce loads and stores to global device memory. This can improve performance by avoiding non-coalesced global memory accesses. The document provides an example of coalescing writes for a matrix transpose by first loading data into shared memory and then writing columns of the tile to global memory in contiguous addresses.
[Harvard CS264] 04 - Intermediate-level CUDA Programmingnpinto
This document provides an overview and summary of key points from a lecture on massively parallel computing using CUDA. The lecture covers CUDA language and APIs, threading and execution models, memory and communication, tools, and libraries. It discusses the CUDA programming model including host and device code, threads and blocks, and memory allocation and transfers between the host and device. It also summarizes the CUDA runtime and driver APIs for launching kernels and managing devices at different levels of abstraction.
[Harvard CS264] 03 - Introduction to GPU Computing, CUDA Basicsnpinto
1. GPUs have many more cores than CPUs and are very good at processing large blocks of data in parallel.
2. GPUs can provide a significant speedup over CPUs for applications that map well to a data-parallel programming model by harnessing the power of many cores.
3. The throughput-oriented nature of GPUs makes them well-suited for algorithms where the same operation can be performed on many data elements independently.
Main Java[All of the Base Concepts}.docxadhitya5119
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
How to Manage Your Lost Opportunities in Odoo 17 CRMCeline George
Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
-------------------------------------------------------------------------------
Find out more about ISO training and certification services
Training: ISO/IEC 27001 Information Security Management System - EN | PECB
ISO/IEC 42001 Artificial Intelligence Management System - EN | PECB
General Data Protection Regulation (GDPR) - Training Courses - EN | PECB
Webinars: https://pecb.com/webinars
Article: https://pecb.com/article
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For more information about PECB:
Website: https://pecb.com/
LinkedIn: https://www.linkedin.com/company/pecb/
Facebook: https://www.facebook.com/PECBInternational/
Slideshare: http://www.slideshare.net/PECBCERTIFICATION
How to Make a Field Mandatory in Odoo 17Celine George
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
Walmart Business + (https://business.walmart.com/plus) is a new shopping experience for nonprofits, schools, and local business customers that connects an exclusive online shopping experience to stores. Benefits include free delivery and shipping, a 'Spend Analytics” feature, special discounts, deals and tax-exempt shopping.
Special TechSoup offer for a free 180 days membership, and up to $150 in discounts on eligible orders.
Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
16. Massively Parallel Computing
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17. Massively Parallel Computing
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19. Modeling & Simulation
• Physics, astronomy, molecular dynamics, finance, etc.
• Data and processing intensive
• Requires high-performance computing (HPC)
• Driving HPC architecture development
20. (20 09)
CS 264
Top Dog (2008)
• Roadrunner, LANL
• #1 on top500.org in 2008 (now #7)
• 1.105 petaflop/s
• 3000 nodes with dual-core AMD Opteron processors
• Each node connected via PCIe to two IBM Cell processors
• Nodes are connected via Infiniband 4x DDR
22. Tianhe-1A
at NSC Tianjin
2.507 Petaflop
7168 Tesla M2050 GPUs
1 Petaflop/s = ~1M high-end laptops = ~world population
with hand calculators 24/7/365 for ~16 years
Slide courtesy of Bill Dally (NVIDIA)
31. Massively Parallel Computing
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45. How much Data?
• Google processes 24 PB / day, 8 EB / year (’10)
• Wayback Machine has 3 PB,100 TB/month (’09)
• Facebook user data: 2.5 PB, 15 TB/day (’09)
• Facebook photos: 15 B, 3 TB/day (’09) - 90 B (now)
• eBay user data: 6.5 PB, 50 TB/day (’09)
• “all words ever spoken by human beings”~ 42 ZB
Adapted from http://www.umiacs.umd.edu/~jimmylin/cloud-2010-Spring/
46. “640k ought to be enough for anybody.”
- Bill Gates just a rumor (1981)
47. Disk Throughput
• Average Google job size: 180 GB
• 1 SATA HDD = 75 MB / sec
• Time to read 180 GB off disk: 45 mins
• Solution: parallel reads
• 1000 HDDs = 75 GB / sec
• Google’s solutions: BigTable, MapReduce, etc.
48. Cloud Computing
• Clear trend: centralization of computing
resources in large data centers
• Q: What do Oregon, Iceland, and
abandoned mines have in common?
• A: Fiber, juice, and space
• Utility computing!
49. Massively Parallel Computing
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50. Instrument Data
Explosion
Sloan Digital Sky Survey
ATLUM / Connectome Project
61. Slide courtesy of Hanspeter Pfister
Diesel Powered HPC
Life Support…
Murchison Widefield Array
62. How much Data?
• NOAA has ~1 PB climate data (‘07)
• MWA radio telescope: 8 GB/sec of data
• Connectome: 1 PB / mm3 of brain tissue
(1 EB for 1 cm3)
• CERN’s LHC will generate 15 PB a year (‘08)
64. Massively Parallel Computing
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65. Computer Games
• PC gaming business:
• $15B / year market (2010)
• $22B / year in 2015 ?
• WOW: $1B / year
• NVIDIA Shipped 1B GPUs since 1993:
• 10 years to ship 200M GPUs (1993-2003)
• 1/3 of all PCs have more than one GPU
• High-end GPUs sell for around $300
• Now used for science application
76. Massively Parallel Computing
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77. Massively Parallel Computing
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78. Massively Parallel Human
Computing ???
• “Crowdsourcing”
• Amazon Mechanical Turk
(artificial artificial intelligence)
• Wikipedia
• Stackoverflow
• etc.
80. What is this course about?
Massively parallel processors
• GPU computing with CUDA
Cloud computing
• Amazon’s EC2 as an example of utility
computing
• MapReduce, the “back-end” of cloud
computing
93. Good News
• Moore’s Law marches on
• Chip real-estate is essentially free
• Many-core architectures are commodities
• Space for new innovations
94. Bad News
• Power limits improvements in clock speed
• Parallelism is the only route to improve
performance
• Computation / communication ratio will get
worse
• More frequent hardware failures?
96. A “Simple” Matter of
Software
• We have to use all the cores efficiently
• Careful data and memory management
• Must rethink software design
• Must rethink algorithms
• Must learn new skills!
• Must learn new strategies!
• Must learn new tools...
107. “ If you want to have good ideas
you must have many ideas. ”
“ Most of them will be wrong,
and what you have to learn is
which ones to throw away. ”
Linus Pauling
(double Nobel Prize Winner)
110. The curse of speed
...and the blessing of massively parallel computing
thousands of big models
large amounts of unsupervised
learning experience
111. The curse of speed
...and the blessing of massively parallel computing
No off-the-shelf solution? DIY!
Engineering (Hardware/SysAdmin/Software) Science
Leverage non-scientific high-tech
markets and their $billions of R&D...
Gaming: Graphics Cards (GPUs), PlayStation 3
Web 2.0: Cloud Computing (Amazon, Google)
114. The blessing of GPUs
DIY GPU pr0n (since 2006) Sony Playstation 3s (since 2007)
115. speed
(in billion floating point operations per second)
Q9450 (Matlab/C) [2008] 0.3
Q9450 (C/SSE) [2008] 9.0
7900GTX (OpenGL/Cg) [2006] 68.2
PS3/Cell (C/ASM) [2007] 111.4
8800GTX (CUDA1.x) [2007] 192.7
GTX280 (CUDA2.x) [2008] 339.3
cha n ging...
e
GTX480 (CUDA3.x) [2010]
pe edu p is g a m 974.3
(Fermi)
>1 000X s
Pinto, Doukhan, DiCarlo, Cox PLoS 2009
Pinto, Cox GPU Comp. Gems 2011
116. Tired Of Waiting For Your
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136. CS 264 Goals
• Have fun!
• Learn basic principles of parallel computing
• Learn programming with CUDA
• Learn to program a cluster of GPUs (e.g. MPI)
• Learn basics of EC2 and MapReduce
• Learn new learning strategies, tools, etc.
• Implement a final project
139. Lectures “Format”
• 2x ~ 45min regular “lectures”
• ~ 15min “Clinic”
• we’ll be here to fix your problems
• ~ 5 min: Life and Code “Hacking”:
• GTD Zen
• Presentation Zen
• Ninja Programming Tricks & Tools, etc.
• Interested? email staff+spotlight@cs264.org
140. Act I: GPU Computing
• Introduction to GPU Computing
• CUDA Basics
• CUDA Advanced
• CUDA Ninja Tricks !
141. l u t i on
n k Convo
Fi lterba
Performance / Effort 3D
Performance (g ops) Development Time (hours)
0.3
Matlab
0.5
9.0
C/SSE
10.0
111.4
PS3
30.0
339.3
GT200
10.0
142. Empirical results...
Performance (g ops)
Q9450 (Matlab/C) [2008] 0.3
Q9450 (C/SSE) [2008] 9.0
7900GTX (Cg) [2006] 68.2
PS3/Cell (C/ASM) [2007] 111.4
8800GTX (CUDA1.x) [2007] 192.7
GTX280 (CUDA2.x) [2008] 339.3
.
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143. Act II: Cloud Computing
• Introduction to utility computing
• EC2 & starcluster (Justin Riley, MIT OEIT)
• Hadoop (Zak Stone, SEAS)
• MapReduce with GPU Jobs on EC2
144. Amazon’s Web Services
• Elastic Compute Cloud (EC2)
• Rent computing resources by the hour
• Basic unit of accounting = instance-hour
• Additional costs for bandwidth
• You’ll be getting free AWS credits for course
assignments
145. MapReduce
• Functional programming meets distributed
processing
• Processing of lists with <key, value> pairs
• Batch data processing infrastructure
• Move the computation where the data is
146. Act III: Guest Lectures
• Andreas Knockler (NYU): OpenCL & PyOpenCL
• John Owens (UC Davis): fundamental algorithms/
data structures and irregular parallelism
• Nathan Bell (NVIDIA): Thrust
• Duane Merrill* (Virginia Tech): Ninja Tricks
• Mike Bauer* (Stanford): Sequoia
• Greg Diamos (Georgia Tech): Ocelot
• Other lecturers* from Google,Yahoo, Sun, Intel,
NCSA, AMD, Cloudera, etc.
147. Labs
• Lead by TF(s)
• Work on an interesting small problem
• From skeleton code to solution
• Hands-on
156. What do you
need to know?
• Programming (ideally in C / C++)
• See HW 0
• Basics of computer systems
• CS 61 or similar
157. Homeworks
• Programming assignments
• “Issue Spotter” (code debug & review, Q&A)
• Contribution to the community
(OSS, Wikipedia, Stackoverflow, etc.)
• Due: Fridays at 11 pm EST
• Hard deadline - 2 “bonus” days
158. Office Hours
• Lead by a TF
• 104 @ 53 Church St
(check website and news feed)
159. Participation
• HW0 (this week)
• Mandatory attendance for guest lectures
• forum.cs264.org
• Answer questions, help others
• Post relevant links and discussions (!)
160. Final Project
• Implement a substantial project
• Pick from a list of suggested projects or design
your own
• Milestones along the way (idea, proposal, etc.)
• In-class final presentations
• $500+ price for the best project
161. Grading
• On a 0-100 scale
• Participation: 10%
• Homework: 50%
• Final project: 40%
162. www.cs264.org
• Detailed schedule (soon)
• News blog w/ RSS feed
• Video feeds
• Forum (forum.cs264.org)
• Academic honesty policy
• HW0 (due Fri 2/4)
167. This course is not for you...
• If you’re not genuinely interested in the topic
• If you can’t cope with uncertainly,
unpredictability, poor documentation, and
immature software
• If you’re not ready to do a lot of programming
• If you’re not open to thinking about computing in
new ways
• If you can’t put in the time
Slide after Jimmy Lin, iSchool, Maryland
175. Acknowledgements
• Hanspeter Pfister & Henry Leitner, DCE
• TFs
• Rob Parrott & IT Team, SEAS
• Gabe Russell & Video Team, DCE
• NVIDIA, esp. David Luebke
• Amazon
177. Next?
• Fill out the survey: http://bit.ly/enrb1r
• Get ready for HW0 (Lab 1 & 2)
• Subscribe to http://forum.cs264.org
• Subscribe to RSS feed: http://bit.ly/eFIsqR