This document discusses patterns for parallel computing. It outlines key concepts like Amdahl's law and types of parallelism like data and task parallelism. Examples are provided of how major tech companies like Microsoft, Google, Amazon implement parallelism at different levels of their infrastructure and applications to scale efficiently. Design principles are discussed for converting sequential programs to parallel programs while maintaining performance.
Please contact me to download this pres.A comprehensive presentation on the field of Parallel Computing.It's applications are only growing exponentially day by days.A useful seminar covering basics,its classification and implementation thoroughly.
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Research Scope in Parallel Computing And Parallel ProgrammingShitalkumar Sukhdeve
Research Scope in Parallel Programming and Parallel computing,Different forms of parallel computing,bit-level,
instruction level, data, and task parallelism,multi-core and multi-processor computers having multiple processing elements within a single machine, while clusters, MPPs, and grids ,Concurrent programming languages, libraries, APIs, and parallel programming models (such as Algorithmic Skeletons) ,shared memory, distributed memory, or shared distributed memory.POSIX Threads and OpenMP are two of most widely used shared memory APIs, whereas Message Passing Interface (MPI) is the most widely used message-passing system API.The ”future concept” is also useful while implementing parallel programming.Automatic parallelization,parallel programming languages exist—
SISAL,
Parallel Haskell,
System C (for FPGAs),
Mitrion-C,
VHDL, and
Verilog.
Application checkpointing
Parallel computing and its applicationsBurhan Ahmed
Parallel computing is a type of computing architecture in which several processors execute or process an application or computation simultaneously. Parallel computing helps in performing large computations by dividing the workload between more than one processor, all of which work through the computation at the same time. Most supercomputers employ parallel computing principles to operate. Parallel computing is also known as parallel processing.
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Please contact me to download this pres.A comprehensive presentation on the field of Parallel Computing.It's applications are only growing exponentially day by days.A useful seminar covering basics,its classification and implementation thoroughly.
Visit www.ameyawaghmare.wordpress.com for more info
Research Scope in Parallel Computing And Parallel ProgrammingShitalkumar Sukhdeve
Research Scope in Parallel Programming and Parallel computing,Different forms of parallel computing,bit-level,
instruction level, data, and task parallelism,multi-core and multi-processor computers having multiple processing elements within a single machine, while clusters, MPPs, and grids ,Concurrent programming languages, libraries, APIs, and parallel programming models (such as Algorithmic Skeletons) ,shared memory, distributed memory, or shared distributed memory.POSIX Threads and OpenMP are two of most widely used shared memory APIs, whereas Message Passing Interface (MPI) is the most widely used message-passing system API.The ”future concept” is also useful while implementing parallel programming.Automatic parallelization,parallel programming languages exist—
SISAL,
Parallel Haskell,
System C (for FPGAs),
Mitrion-C,
VHDL, and
Verilog.
Application checkpointing
Parallel computing and its applicationsBurhan Ahmed
Parallel computing is a type of computing architecture in which several processors execute or process an application or computation simultaneously. Parallel computing helps in performing large computations by dividing the workload between more than one processor, all of which work through the computation at the same time. Most supercomputers employ parallel computing principles to operate. Parallel computing is also known as parallel processing.
↓↓↓↓ Read More:
Watch my videos on snack here: --> --> http://sck.io/x-B1f0Iy
@ Kindly Follow my Instagram Page to discuss about your mental health problems-
-----> https://instagram.com/mentality_streak?utm_medium=copy_link
@ Appreciate my work:
-----> behance.net/burhanahmed1
Thank-you !
The primary reasons for using parallel computing:
Save time - wall clock time
Solve larger problems
Provide concurrency (do multiple things at the same time)
Parallel computing is computing architecture paradigm ., in which processing required to solve a problem is done in more than one processor parallel way.
MySpace Chief Data Architect Christa Stelzmuller slides from her talk to the Silicon Valley SQL Server User Group in June 2009. Read about it on the Ginneblog: http://bit.ly/YLzle
Parallel Computing: Perspectives for more efficient hydrological modelingGrigoris Anagnostopoulos
A presentation that introduces the basic concepts of parallel computing and gives some details on General Purpose GPU computing using the CUDA architecture.
The primary reasons for using parallel computing:
Save time - wall clock time
Solve larger problems
Provide concurrency (do multiple things at the same time)
Parallel computing is computing architecture paradigm ., in which processing required to solve a problem is done in more than one processor parallel way.
MySpace Chief Data Architect Christa Stelzmuller slides from her talk to the Silicon Valley SQL Server User Group in June 2009. Read about it on the Ginneblog: http://bit.ly/YLzle
Parallel Computing: Perspectives for more efficient hydrological modelingGrigoris Anagnostopoulos
A presentation that introduces the basic concepts of parallel computing and gives some details on General Purpose GPU computing using the CUDA architecture.
A quick review and demonstration on how to get started on parallel computing with R. Includes an example of SNOW cluster set up in the departmental lab.
Iterative computations are at the core of the vast majority of data-intensive scientific computations. Recent advancements in data intensive computational fields are fueling a dramatic growth in number as well as usage of such data intensive iterative computations. The utility computing model introduced by cloud computing combined with the rich set of cloud infrastructure services offers a very viable environment for the scientists to perform data intensive computations. However, clouds by nature offer unique reliability and sustained performance challenges to large scale distributed computations necessitating computation frameworks specifically tailored for cloud characteristics to harness the power of clouds easily and effectively. My research focuses on identifying and developing user-friendly distributed parallel computation frameworks to facilitate the optimized efficient execution of iterative as well as non-iterative data-intensive computations in cloud environments, alongside the evaluation of heterogeneous cloud resources offering GPGPU resources in addition to CPU resources, for data-intensive iterative computations.
Yura Nikonovich is the first to set up a tradition with his research “CAP theorem and distributed systems”.
In the first part of the report, find his insights into what distributed systems are and what challenges he faced in the course of his work with them.
In the second part of the report Yura tells us about CAP theorem, it's varieties and the future of distributed databases.
High Performance Parallel Computing with Clouds and Cloud Technologiesjaliyae
Infrastructure services (Infrastructure-as-a-service), provided by cloud vendors, allow any user to provision a large number of compute instances fairly easily. Whether leased from public clouds or allocated from private clouds, utilizing these virtual resources to perform data/compute intensive analyses requires employing different parallel runtimes to implement such applications. Among many parallelizable problems, most “pleasingly parallel” applications can be performed using MapReduce technologies such as Hadoop, CGL-MapReduce, and Dryad, in a fairly easy manner. However, many scientific applications, which have complex communication patterns, still require low latency communication mechanisms and rich set of communication constructs offered by runtimes such as MPI. In this paper, we first discuss large scale data analysis using different MapReduce implementations and then, we present a performance analysis of high performance parallel applications on virtualized resources.
NoSQL databases, the CAP theorem, and the theory of relativityLars Marius Garshol
A presentation showing how the CAP theorem causes NoSQL databases to have BASE semantics. That is, they don't support ACID consistency. Then shows how CAP is related to Einstein's theory of relativity. And finally shows how Google Spanner and F1 provide ACID that scales.
This presentation, by big data guru Bernard Marr, outlines in simple terms what Big Data is and how it is used today. It covers the 5 V's of Big Data as well as a number of high value use cases.
This is the course that was presented by James Liddle and Adam Vile for Waters in September 2008.
The book of this course can be found at: http://www.lulu.com/content/4334860
Data-driven companies have a need to make their data easily accessible to those who analyze it. Many organizations have adopted the Looker application, LookML on AWS, a centralized analytical database with a user-friendly interface that allows employees to ask and answer their own questions to make informed business decisions.
Join our webinar to learn how our customer, Casper, an online mattress retailer, made the switch from a transactional database to Looker’s data analytics program on Amazon Redshift. Looker on Amazon Redshift can help you greatly reduce your analytics lifecycle with a simplified infrastructure and rapid cloud scaling.
Join us to learn:
• How to utilize LookML to build reusable definitions and logic for your data
• Best practices for architecting a centralized analytical database
• How Casper leveraged Looker and Amazon Redshift to provide all their employees access to their data and metrics
Who should attend: Heads of Analytics, Heads of BI, Analytics Managers, BI Teams, Senior Analysts
Big Data Serving with Vespa - Jon Bratseth, Distinguished Architect, OathYahoo Developer Network
Offline and stream processing of big data sets can be done with tools such as Hadoop, Spark, and Storm, but what if you need to process big data at the time a user is making a request? Vespa (http://www.vespa.ai) allows you to search, organize and evaluate machine-learned models from e.g TensorFlow over large, evolving data sets with latencies in the tens of milliseconds. Vespa is behind the recommendation, ad targeting, and search at Yahoo where it handles billions of daily queries over billions of documents.
Graph Data: a New Data Management FrontierDemai Ni
Graph Data: a New Data Management Frontier -- Huawei’s view and Call for Collaboration by Demai Ni:
Huawei provides Enterprise Databases, and are actively exploring the latest technology to provide end-to-end Data Management Solution on Cloud. We are looking at to bridge classic RDMS to Graph Database on a distributed platform.
Presentazione durante il Cloud Community Day del 22 luglio 2013 presso il Politecnico di Milano.
http://www.eurocloud.it/index.php/component/content/article/190-cloud-communities-day
Maginatics @ SDC 2013: Architecting An Enterprise Storage Platform Using Obje...Maginatics
How did Maginatics build a strongly consistent and secure distributed file system? Niraj Tolia, Chief Architect at Maginatics, gave this presentation on the design of MagFS at the Storage Developer Conference on September 16, 2013.
For more information about MagFS—The File System for the Cloud, visit maginatics.com or contact us directly at info@maginatics.com.
Big Data raises challenges about how to process such vast pool of raw data and how to aggregate value to our lives. For addressing these demands an ecosystem of tools named Hadoop was conceived.
Maximizing Data Lake ROI with Data Virtualization: A Technical DemonstrationDenodo
Watch full webinar here: https://bit.ly/3ohtRqm
Companies with corporate data lakes also need a strategy for how to best integrate them with their overall data fabric. To take full advantage of a data lake, data architects must determine what data belongs in the Lake vs. other sources, how end users are going to find and connect to the data they need as well as the best way to leverage the processing power of the data lake. This webinar will provide you with a deep dive look at how the Denodo Platform for data virtualization enables companies to maximize their investment in their corporate data lake.
Watch on-demand this webinar to learn:
- How to create a logical data fabric with Denodo
- How to leverage the a data lake for MPP Acceleration and Summary Views
- How to leverage Presto with Denodo for file based data lakes (ie. S3, ADLS, HDFS, etc.)
Choosing technologies for a big data solution in the cloudJames Serra
Has your company been building data warehouses for years using SQL Server? And are you now tasked with creating or moving your data warehouse to the cloud and modernizing it to support “Big Data”? What technologies and tools should use? That is what this presentation will help you answer. First we will cover what questions to ask concerning data (type, size, frequency), reporting, performance needs, on-prem vs cloud, staff technology skills, OSS requirements, cost, and MDM needs. Then we will show you common big data architecture solutions and help you to answer questions such as: Where do I store the data? Should I use a data lake? Do I still need a cube? What about Hadoop/NoSQL? Do I need the power of MPP? Should I build a "logical data warehouse"? What is this lambda architecture? Can I use Hadoop for my DW? Finally, we’ll show some architectures of real-world customer big data solutions. Come to this session to get started down the path to making the proper technology choices in moving to the cloud.
Organize & manage master meta data centrally, built upon kong, cassandra, neo4j & elasticsearch. Managing master & meta data is a very common problem with no good opensource alternative as far as I know, so initiating this project – MasterMetaData.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
10. >Patterns > Multi-threading Multi-threading Typically, functional decomposition into individual threads But, explicit concurrent programming brings complexities Managing threads, semaphores, monitors, dead-locks, race conditions, mutual exclusion, synchronization, etc. Moving towards implicit parallelism Integrating concurrency & coordination into mainstream programming languages Developing tools to ease development Encapsulating parallelism in reusable components Raising the semantic level: new approaches
11. >Patterns > Multi-threading > Example Photobucket Web Browser 2007 stats: +30M searches processed / day 25M UU/month in US, +46M worldwide +7B images uploaded +300K unique websites link to content #31 top 50 sites in US #41 top 100 sites worldwide 18th largest ad supported site in US Thumbs Images Albums Groups Content Pods Content Pods Content Pods Content Pods API Content Pods Content Pods Content Pods Content Pods Content Pods Content Pods Content Pods Content Pods Content Pods Content Pods Content Pods Content Pods PIC Scaling the performance: Browser handles concurrency Centralized lookup Horizontal partitioning of distributed content Metadata Membership
12. >Patterns > Data Parallelism Data Parallelism Loop-level parallelism Focuses on distributing the data across different parallel computing nodes Denormalization, sharding, horizontal partitioning, etc. Each processor performs the same task on different pieces of distributed data Emphasizes the distributed (parallelized) nature of the data Ideal for data that is read more than written (scale vs. consistency)
13. >Patterns > Data Parallelism Parallelizing Data in Distributed Architecture Browser Browser Browser Web/App Server Web/App Server Web/App Server Web/App Server Web/App Server A-Z A-M N-Z H-M N-S A-G T-Z Index
14. >Patterns > Data Parallelism > Example Flickr 2007 stats: Serve 40,000 photos / second Handle 100,000 cache operations / second Process 130,000 database queries / second Scaling the “read” data: Data denormalization Database replication and federation Vertical partitioning Central cluster for index lookups Large data sets horizontally partitioned as shards Grow by binary hashing of user buckets
15. >Patterns > Data Parallelism > Example MySpace 2007 stats: 115B pageviews/month 5M concurrent users @ peak +3B images, mp3, videos +10M new images/day 160 Gbit/sec peak bandwidth Scaling the “write” data: MyCache: distributed dynamic memory cache MyRelay: inter-node messaging transport handling +100K req/sec, directs reads/writes to any node MySpace Distributed File System: geographically redundant distributed storage providing massive concurrent access to images, mp3, videos, etc. MySpace Distributed Transaction Manager: broker for all non-transient writes to databases/SAN, multi-phase commit across data centers
16. >Patterns > Data Parallelism > Example Facebook 2009 stats: +200B pageviews/month >3.9T feed actions/day +300M active users >1B chat mesgs/day 100M search queries/day >6B minutes spent/day (ranked #2 on Internet) +20B photos, +2B/month growth 600,000 photos served / sec 25TB log data / day processed thru Scribe 120M queries /sec on memcache Scaling the “relational” data: Keeps data normalized, randomly distributed, accessed at high volumes Uses “shared nothing” architecture
17. >Patterns > Task Parallelism Task Parallelism Functional parallelism Focuses on distributing execution processes (threads) across different parallel computing nodes Each processor executes a different thread (or process) on the same or different data Communication takes place usually to pass data from one thread to the next as part of a workflow Emphasizes the distributed (parallelized) nature of the processing (i.e. threads) Need to design how to compose partial output from concurrent processes
18. >Patterns > Task Parallelism > Example Google 2007 stats: +20 petabytes of data processed / day by +100K MapReduce jobs 1 petabyte sort took ~6 hours on ~4K servers replicated onto ~48K disks +200 GFS clusters, each at 1-5K nodes, handling +5 petabytes of storage ~40 GB/sec aggregate read/write throughput across the cluster +500 servers for each search query < 500ms Scaling the process: MapReduce: parallel processing framework BigTable: structured hash database Google File System: massively scalable distributed storage
20. > Design Principles Parallelism for Scale-out Sequential Parallel Convert sequential and/or single-machine program into a form in which it can be executed in a concurrent, potentially distributed environment Over-decompose for scaling Structured multi-threading with a data focus Relax sequential order to gain more parallelism Ensure atomicity of unordered interactions Consider data as well as control flow Careful data structure & locking choices to manage contention User parallel data structures Minimize shared data and synchronization Continuous optimization
21. >Design Principles > Example Amazon Principles for Scalable Service Design (Werner Vogels, CTO, Amazon) Autonomy Asynchrony Controlled concurrency Controlled parallelism Decentralize Decompose into small well-understood building blocks Failure tolerant Local responsibility Recovery built-in Simplicity Symmetry
22. > Microsoft Platform Parallel computing on the Microsoft platform Concurrent Programming (.NET 4.0 Parallel APIs) Distributed Computing (CCR & DSS Runtime, Dryad) Cloud Computing (Azure Services Platform) Grid Computing (Windows HPC Server 2008) Massive Data Processing (SQL Server “Madison”) Components spanning a spectrum of computing models
24. > Microsoft Platform > Distributed Computing CCR & DSS Toolkit Concurrency & Coordination Runtime Decentralized Software Services Supporting multi-core and concurrent applications by facilitating asynchronous operations Dealing with concurrency, exploiting parallel hardware and handling partial failure Supporting robust, distributed applications based on a light-weight state-driven service model Providing service composition, event notification, and data isolation
25. > Microsoft Platform > Distributed Computing Dryad General-purpose execution environment for distributed, data-parallel applications Automated management of resources, scheduling, distribution, monitoring, fault tolerance, accounting, etc. Concurrency and mutual exclusion semantics transparency Higher-level and domain-specific language support
26. > Microsoft Platform > Cloud Computing Azure Services Platform Internet-scale, highly available cloud fabric Auto-provisioning 64-bit compute nodes on Windows Server VMs Massively scalable distributed storage (table, blob, queue) Massively scalable and highly consistent relational database
27. > Microsoft Platform > Grid Computing Windows HPC Server #10 fastest supercomputer in the world (top500.org) 30,720 cores 180.6 teraflops 77.5% efficiency Image multicasting-based parallel deployment of cluster nodes Fault tolerance with failover clustering of head node Policy-driven, NUMA-aware, multicore-aware, job scheduler Inter-process distributed communication via MS-MPI
28. > Microsoft Platform > Massive Data Processing SQL Server “Madison” Massively parallel processing (MPP) architecture +500TB to PB’s databases “Ultra Shared Nothing” design IO and CPU affinity within symmetric multi-processing (SMP) nodes Multiple physical instances of tables w/ dynamic re-distribution Distribute / partition large tables across multiple nodes Replicate small tables Replicate + distribute medium tables
29. > Resources For More Information Architect Council Website (blogs.msdn.com/sac) This series (blogs.msdn.com/sac/pages/council-2009q2.aspx) .NET 4.0 Parallel APIs (msdn.com/concurrency) CCR & DSS Toolkit (microsoft.com/ccrdss) Dryad (research.microsoft.com/dryad) Azure Services Platform (azure.com) SQL Server “Madison” (microsoft.com/madison) Windows HPC Server 2008 (microsoft.com/hpc)