The document summarizes a meetup group for the application of high-performance and GPU supercomputing technology to business problems. The group started in 2011 with locations in several US cities and Tokyo, and has reached over 1000 members. It is non-profit and hosted on Meetup.com. The group provides a forum for professionals to discuss challenges and solutions for applying advanced computing technologies in business.
Squeezing Deep Learning Into Mobile PhonesAnirudh Koul
A practical talk by Anirudh Koul aimed at how to run Deep Neural Networks to run on memory and energy constrained devices like smart phones. Highlights some frameworks and best practices.
This presentation focuses on Deep Learning (DL) concepts, such as neural networks, backprop, activation functions, and Convolutional Neural Networks. You'll also learn how to incorporate Deep Learning in Android applications. Basic knowledge of matrices is helpful for this session, which is targeted primarily to beginners.
Presentation of few recent papers on Deep Learning ... in particular Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding, Song Han, Huizi Mao, William J. Dally International Conference on Learning Representations ICLR2016
Deep Learning Frameworks Using Spark on YARN by Vartika SinghData Con LA
Abstract:- Traditional machine learning and feature engineering algorithms are not efficient enough to extract complex and nonlinear patterns hallmarks of big data. Deep learning, on the other hand, helps translate the scale and complexity of the data into solutions like molecular interaction in drug design, the search for subatomic particles and automatic parsing of microscopic images. Co-locating a data processing pipeline with a deep learning framework makes data exploration/algorithm and model evolution much simpler, while streamlining data governance and lineage tracking into a more focused effort. In this talk, we will discuss and compare the different deep learning frameworks on Spark in a distributed mode, ease of integration with the Hadoop ecosystem, and relative comparisons in terms of feature parity.
Improving Hardware Efficiency for DNN ApplicationsChester Chen
Speaker: Dr. Hai (Helen) Li is the Clare Boothe Luce Associate Professor of Electrical and Computer Engineering and Co-director of the Duke Center for Evolutionary Intelligence at Duke University
In this talk, I will introduce a few recent research spotlights by the Duke Center for Evolutionary Intelligence. The talk will start with the structured sparsity learning (SSL) method which attempts to learn a compact structure from a bigger DNN to reduce computation cost. It generates a regularized structure with high execution efficiency. Our experiments on CPU, GPU, and FPGA platforms show on average 3~5 times speedup of convolutional layer computation of AlexNet. Then, the implementation and acceleration of DNN applications on mobile computing systems will be introduced. MoDNN is a local distributed system which partitions DNN models onto several mobile devices to accelerate computations. ApesNet is an efficient pixel-wise segmentation network, which understands road scenes in real-time, and has achieved promising accuracy. Our prospects on the adoption of emerging technology will also be given at the end of this talk, offering the audiences an alternative thinking about the future evolution and revolution of modern computing systems.
Hadoop Summit 2014 - San Jose - Introduction to Deep Learning on HadoopJosh Patterson
As the data world undergoes its cambrian explosion phase our data tools need to become more advanced to keep pace. Deep Learning has emerged as a key tool in the non-linear arms race of machine learning. In this session we will take a look at how we parallelize Deep Belief Networks in Deep Learning on Hadoop’s next generation YARN framework with Iterative Reduce. We’ll also look at some real world examples of processing data with Deep Learning such as image classification and natural language processing.
Accelerate Machine Learning Software on Intel Architecture Intel® Software
This session presents performance data for deep learning training for image recognition that achieves greater than 24 times speedup performance with a single Intel® Xeon Phi™ processor 7250 when compared to Caffe*. In addition, we present performance data that shows training time is further reduced by 40 times the speedup with a 128-node Intel® Xeon Phi™ processor cluster over Intel® Omni-Path Architecture (Intel® OPA).
Squeezing Deep Learning Into Mobile PhonesAnirudh Koul
A practical talk by Anirudh Koul aimed at how to run Deep Neural Networks to run on memory and energy constrained devices like smart phones. Highlights some frameworks and best practices.
This presentation focuses on Deep Learning (DL) concepts, such as neural networks, backprop, activation functions, and Convolutional Neural Networks. You'll also learn how to incorporate Deep Learning in Android applications. Basic knowledge of matrices is helpful for this session, which is targeted primarily to beginners.
Presentation of few recent papers on Deep Learning ... in particular Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding, Song Han, Huizi Mao, William J. Dally International Conference on Learning Representations ICLR2016
Deep Learning Frameworks Using Spark on YARN by Vartika SinghData Con LA
Abstract:- Traditional machine learning and feature engineering algorithms are not efficient enough to extract complex and nonlinear patterns hallmarks of big data. Deep learning, on the other hand, helps translate the scale and complexity of the data into solutions like molecular interaction in drug design, the search for subatomic particles and automatic parsing of microscopic images. Co-locating a data processing pipeline with a deep learning framework makes data exploration/algorithm and model evolution much simpler, while streamlining data governance and lineage tracking into a more focused effort. In this talk, we will discuss and compare the different deep learning frameworks on Spark in a distributed mode, ease of integration with the Hadoop ecosystem, and relative comparisons in terms of feature parity.
Improving Hardware Efficiency for DNN ApplicationsChester Chen
Speaker: Dr. Hai (Helen) Li is the Clare Boothe Luce Associate Professor of Electrical and Computer Engineering and Co-director of the Duke Center for Evolutionary Intelligence at Duke University
In this talk, I will introduce a few recent research spotlights by the Duke Center for Evolutionary Intelligence. The talk will start with the structured sparsity learning (SSL) method which attempts to learn a compact structure from a bigger DNN to reduce computation cost. It generates a regularized structure with high execution efficiency. Our experiments on CPU, GPU, and FPGA platforms show on average 3~5 times speedup of convolutional layer computation of AlexNet. Then, the implementation and acceleration of DNN applications on mobile computing systems will be introduced. MoDNN is a local distributed system which partitions DNN models onto several mobile devices to accelerate computations. ApesNet is an efficient pixel-wise segmentation network, which understands road scenes in real-time, and has achieved promising accuracy. Our prospects on the adoption of emerging technology will also be given at the end of this talk, offering the audiences an alternative thinking about the future evolution and revolution of modern computing systems.
Hadoop Summit 2014 - San Jose - Introduction to Deep Learning on HadoopJosh Patterson
As the data world undergoes its cambrian explosion phase our data tools need to become more advanced to keep pace. Deep Learning has emerged as a key tool in the non-linear arms race of machine learning. In this session we will take a look at how we parallelize Deep Belief Networks in Deep Learning on Hadoop’s next generation YARN framework with Iterative Reduce. We’ll also look at some real world examples of processing data with Deep Learning such as image classification and natural language processing.
Accelerate Machine Learning Software on Intel Architecture Intel® Software
This session presents performance data for deep learning training for image recognition that achieves greater than 24 times speedup performance with a single Intel® Xeon Phi™ processor 7250 when compared to Caffe*. In addition, we present performance data that shows training time is further reduced by 40 times the speedup with a 128-node Intel® Xeon Phi™ processor cluster over Intel® Omni-Path Architecture (Intel® OPA).
Massively Parallel K-Nearest Neighbor Computation on Distributed Architectures Intel® Software
This session discuss the implementation and performance of the K-nearest neighbor (KNN) computation on a distributed architecture using the Intel® Xeon Phi™ processor.
Synthetic dialogue generation with Deep LearningS N
A walkthrough of a Deep Learning based technique which would generate TV scripts using Recurrent Neural Network. The model will generate a completely new TV script for a scene, after being training from a dataset. One will learn the concepts around RNN, NLP and various deep learning techniques.
Technologies to be used:
Python 3, Jupyter, TensorFlow
Source code: https://github.com/syednasar/talks/tree/master/synthetic-dialog
Deploying deep learning models with Docker and KubernetesPetteriTeikariPhD
Short introduction for platform agnostic production deployment with some medical examples.
Alternative download: https://www.dropbox.com/s/qlml5k5h113trat/deep_cloudArchitecture.pdf?dl=0
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/wavecomp/embedded-vision-training/videos/pages/may-2017-embedded-vision-summit-nicol
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Chris Nicol, CTO at Wave Computing, presents the "New Dataflow Architecture for Machine Learning" tutorial at the May 2017 Embedded Vision Summit.
Data scientists have made tremendous advances in the use of deep neural networks (DNNs) to enhance business models and service offerings. But training DNNs can take a week or more using traditional hardware solutions that rely on legacy architectures that are limited in performance and scalability. New innovations that can reduce training time for both image-centric and text-centric deep neural networks will lead to an explosion of new applications. Dr. Chris Nicol, Wave Computing’s Chief Technology Officer, examines the performance challenge faced by data scientists today. Nicol outlines the technical factors underlying this bottleneck for systems relying on CPUs, GPUs, FPGAs and ASICs, and introduces a new dataflow-centric approach to DNN training.
by Vikram Madan, Sr. Product Manager, AWS Deep Learning
In this workshop, we will provide cover deep learning fundamentals and focus on the powerful and scalable Apache MXNet open source deep learning framework. At the end of this tutorial you’ll be able to train your own deep neural network and fine tune existing state of the art models for image and object recognition. We’ll also deep dive on setting up your deep learning infrastructure on AWS and model deployment on AWS Lambda.
Josh Patterson, Advisor, Skymind – Deep learning for Industry at MLconf ATL 2016MLconf
DL4J and DataVec for Enterprise Deep Learning Workflows: Applications in NLP, sensor processing (IoT), image processing, and audio processing have all emerged as prime deep learning applications. In this session we will take a look at a practical review of building practical and secure Deep Learning workflows in the enterprise. We’ll see how DL4J’s DataVec tool enables scalable ETL and vectorization pipelines to be created for a single machine or scale out to Spark on Hadoop. We’ll also see how Deep Networks such as Recurrent Neural Networks are able to leverage DataVec to more quickly process data for modeling.
Deep learning on mobile - 2019 Practitioner's GuideAnirudh Koul
The 2019 Guide to Deep Learning on Mobile, from Inference to Training on iOS and Android smartphones. Featuring CoreML, Tensorflow Lite, MLKit, Fritz, AutoML Approaches (Hardware Aware Neural Architecture Search) to make models more efficient, and lots of videos. Presented by Anirudh Koul, Siddha Ganju and Meher Anand Kasam. More details at PracticalDL.ai in the upcoming O'Reilly Book 'Practical Deep Learning on Cloud & Mobile'
TensorFlow에 대한 분석 내용
- TensorFlow?
- 배경
- DistBelief
- Tutorial - Logistic regression
- TensorFlow - 내부적으로는
- Tutorial - CNN, RNN
- Benchmarks
- 다른 오픈 소스들
- TensorFlow를 고려한다면
- 설치
- 참고 자료
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/altera/embedded-vision-training/videos/pages/may-2015-embedded-vision-summit
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Deshanand Singh, Director of Software Engineering at Altera, presents the "Efficient Implementation of Convolutional Neural Networks using OpenCL on FPGAs" tutorial at the May 2015 Embedded Vision Summit.
Convolutional neural networks (CNN) are becoming increasingly popular in embedded applications such as vision processing and automotive driver assistance systems. The structure of CNN systems is characterized by cascades of FIR filters and transcendental functions. FPGA technology offers a very efficient way of implementing these structures by allowing designers to build custom hardware datapaths that implement the CNN structure. One challenge of using FPGAs revolves around the design flow that has been traditionally centered around tedious hardware description languages.
In this talk, Deshanand gives a detailed explanation of how CNN algorithms can be expressed in OpenCL and compiled directly to FPGA hardware. He gives detail on code optimizations and provides comparisons with the efficiency of hand-coded implementations.
On-device machine learning: TensorFlow on AndroidYufeng Guo
Machine learning has traditionally been the solely performed on servers and high performance machines. But there is great value is having on-device machine learning for mobile devices. Doing ML inference on mobile devices has huge potential and is still in its early stages. However, it's already more powerful than most realize.
In this demo-oriented talk, you will see some examples of deep learning models used for local prediction on mobile devices. Learn how to use TensorFlow to implement a machine learning model that is tailored to a custom dataset, and start making delightful experiences today!
This is an 1 hour presentation on Neural Networks, Deep Learning, Computer Vision, Recurrent Neural Network and Reinforcement Learning. The talks later have links on how to run Neural Networks on
A practical talk by Anirudh Koul aimed at how to run Deep Neural Networks to run on memory and energy constrained devices like smartphones. Highlights some frameworks and best practices.
Using Deep Learning to do Real-Time Scoring in Practical ApplicationsGreg Makowski
http://www.meetup.com/SF-Bay-ACM/events/227480571/
(see also YouTube for a recording of the presentation)
The talk will cover a brief review of neural network basics and the following types of neural network deep learning:
* autocorrelational - unsupervised learning for extracting features. He will describe how additional layers build complexity in the feature extraction.
* convolutional - how to detect shift invariant patterns in various data sources. Horizontal shift invariant detection applies to signals like speech recognition or IoT data. Horizontal and vertical shift invariance applies to images or videos, for faces or self driving cars
* discuss details of applying deep net systems for continuous or real time scoring
* reinforcement learning or Q Learning - such as learning how to play Atari video games
* continuous space word models - such as word2vec, skipgram training, NLP understanding and translation
instruction of install Caffe step by step on ubuntu 14.04
برای دریافت دی وی دی های این کارگاه (شامل فیلم کارگاه و فایل های مورد نیاز) به آدرس زیر ایمیل بزنید:
pouya.ahmadvand@gmail.com
Slides for the hands on PyData workshop.
Cover three main topics:
- Current state of NLP models at Walmart
- Steps we took to optimize serving BERT
- how we serve models with Facebook’s TorchServe.
Corresponding repo for notebooks for handson:
https://bit.ly/pytorch-workshop-2021
Алгоритмы шифрования и их применение в .Net приложениях для защиты данных.Pavel Tsukanov
Доклад посвящен краткому обзору существующих алгоритмов шифрования, их реализации для платформы .net. Также, помимо шифрования будут рассмотрены и другие варианты защиты данных.
Massively Parallel K-Nearest Neighbor Computation on Distributed Architectures Intel® Software
This session discuss the implementation and performance of the K-nearest neighbor (KNN) computation on a distributed architecture using the Intel® Xeon Phi™ processor.
Synthetic dialogue generation with Deep LearningS N
A walkthrough of a Deep Learning based technique which would generate TV scripts using Recurrent Neural Network. The model will generate a completely new TV script for a scene, after being training from a dataset. One will learn the concepts around RNN, NLP and various deep learning techniques.
Technologies to be used:
Python 3, Jupyter, TensorFlow
Source code: https://github.com/syednasar/talks/tree/master/synthetic-dialog
Deploying deep learning models with Docker and KubernetesPetteriTeikariPhD
Short introduction for platform agnostic production deployment with some medical examples.
Alternative download: https://www.dropbox.com/s/qlml5k5h113trat/deep_cloudArchitecture.pdf?dl=0
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/wavecomp/embedded-vision-training/videos/pages/may-2017-embedded-vision-summit-nicol
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Chris Nicol, CTO at Wave Computing, presents the "New Dataflow Architecture for Machine Learning" tutorial at the May 2017 Embedded Vision Summit.
Data scientists have made tremendous advances in the use of deep neural networks (DNNs) to enhance business models and service offerings. But training DNNs can take a week or more using traditional hardware solutions that rely on legacy architectures that are limited in performance and scalability. New innovations that can reduce training time for both image-centric and text-centric deep neural networks will lead to an explosion of new applications. Dr. Chris Nicol, Wave Computing’s Chief Technology Officer, examines the performance challenge faced by data scientists today. Nicol outlines the technical factors underlying this bottleneck for systems relying on CPUs, GPUs, FPGAs and ASICs, and introduces a new dataflow-centric approach to DNN training.
by Vikram Madan, Sr. Product Manager, AWS Deep Learning
In this workshop, we will provide cover deep learning fundamentals and focus on the powerful and scalable Apache MXNet open source deep learning framework. At the end of this tutorial you’ll be able to train your own deep neural network and fine tune existing state of the art models for image and object recognition. We’ll also deep dive on setting up your deep learning infrastructure on AWS and model deployment on AWS Lambda.
Josh Patterson, Advisor, Skymind – Deep learning for Industry at MLconf ATL 2016MLconf
DL4J and DataVec for Enterprise Deep Learning Workflows: Applications in NLP, sensor processing (IoT), image processing, and audio processing have all emerged as prime deep learning applications. In this session we will take a look at a practical review of building practical and secure Deep Learning workflows in the enterprise. We’ll see how DL4J’s DataVec tool enables scalable ETL and vectorization pipelines to be created for a single machine or scale out to Spark on Hadoop. We’ll also see how Deep Networks such as Recurrent Neural Networks are able to leverage DataVec to more quickly process data for modeling.
Deep learning on mobile - 2019 Practitioner's GuideAnirudh Koul
The 2019 Guide to Deep Learning on Mobile, from Inference to Training on iOS and Android smartphones. Featuring CoreML, Tensorflow Lite, MLKit, Fritz, AutoML Approaches (Hardware Aware Neural Architecture Search) to make models more efficient, and lots of videos. Presented by Anirudh Koul, Siddha Ganju and Meher Anand Kasam. More details at PracticalDL.ai in the upcoming O'Reilly Book 'Practical Deep Learning on Cloud & Mobile'
TensorFlow에 대한 분석 내용
- TensorFlow?
- 배경
- DistBelief
- Tutorial - Logistic regression
- TensorFlow - 내부적으로는
- Tutorial - CNN, RNN
- Benchmarks
- 다른 오픈 소스들
- TensorFlow를 고려한다면
- 설치
- 참고 자료
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/altera/embedded-vision-training/videos/pages/may-2015-embedded-vision-summit
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Deshanand Singh, Director of Software Engineering at Altera, presents the "Efficient Implementation of Convolutional Neural Networks using OpenCL on FPGAs" tutorial at the May 2015 Embedded Vision Summit.
Convolutional neural networks (CNN) are becoming increasingly popular in embedded applications such as vision processing and automotive driver assistance systems. The structure of CNN systems is characterized by cascades of FIR filters and transcendental functions. FPGA technology offers a very efficient way of implementing these structures by allowing designers to build custom hardware datapaths that implement the CNN structure. One challenge of using FPGAs revolves around the design flow that has been traditionally centered around tedious hardware description languages.
In this talk, Deshanand gives a detailed explanation of how CNN algorithms can be expressed in OpenCL and compiled directly to FPGA hardware. He gives detail on code optimizations and provides comparisons with the efficiency of hand-coded implementations.
On-device machine learning: TensorFlow on AndroidYufeng Guo
Machine learning has traditionally been the solely performed on servers and high performance machines. But there is great value is having on-device machine learning for mobile devices. Doing ML inference on mobile devices has huge potential and is still in its early stages. However, it's already more powerful than most realize.
In this demo-oriented talk, you will see some examples of deep learning models used for local prediction on mobile devices. Learn how to use TensorFlow to implement a machine learning model that is tailored to a custom dataset, and start making delightful experiences today!
This is an 1 hour presentation on Neural Networks, Deep Learning, Computer Vision, Recurrent Neural Network and Reinforcement Learning. The talks later have links on how to run Neural Networks on
A practical talk by Anirudh Koul aimed at how to run Deep Neural Networks to run on memory and energy constrained devices like smartphones. Highlights some frameworks and best practices.
Using Deep Learning to do Real-Time Scoring in Practical ApplicationsGreg Makowski
http://www.meetup.com/SF-Bay-ACM/events/227480571/
(see also YouTube for a recording of the presentation)
The talk will cover a brief review of neural network basics and the following types of neural network deep learning:
* autocorrelational - unsupervised learning for extracting features. He will describe how additional layers build complexity in the feature extraction.
* convolutional - how to detect shift invariant patterns in various data sources. Horizontal shift invariant detection applies to signals like speech recognition or IoT data. Horizontal and vertical shift invariance applies to images or videos, for faces or self driving cars
* discuss details of applying deep net systems for continuous or real time scoring
* reinforcement learning or Q Learning - such as learning how to play Atari video games
* continuous space word models - such as word2vec, skipgram training, NLP understanding and translation
instruction of install Caffe step by step on ubuntu 14.04
برای دریافت دی وی دی های این کارگاه (شامل فیلم کارگاه و فایل های مورد نیاز) به آدرس زیر ایمیل بزنید:
pouya.ahmadvand@gmail.com
Slides for the hands on PyData workshop.
Cover three main topics:
- Current state of NLP models at Walmart
- Steps we took to optimize serving BERT
- how we serve models with Facebook’s TorchServe.
Corresponding repo for notebooks for handson:
https://bit.ly/pytorch-workshop-2021
Алгоритмы шифрования и их применение в .Net приложениях для защиты данных.Pavel Tsukanov
Доклад посвящен краткому обзору существующих алгоритмов шифрования, их реализации для платформы .net. Также, помимо шифрования будут рассмотрены и другие варианты защиты данных.
ЭЛЕМЕНТЫ ИСКУСТВЕННОГО ИНТЕЛЛЕКТА ПРИ ПРОГРАММИРОВАНИИ. (http://tuladev.net/e...Pavel Tsukanov
Видео на http://tuladev.net/events/128
Расскажу про нейронные сети, генетические алгоритмы, машинное зрение и нечёткую логику. Всё с реальными примерами. Подискутирую что-же такое ИИ (как же без этого :) ). Если хотите услышать что ещё оставляйте свои комментарии. На самом деле тема обширная, можно рассказать о многом, главное начать.
Основы "мобильной" разработки на примере платформы iOs (iPhone)Pavel Tsukanov
Легкая обзорная лекция по платформе iOS. Рассмотрим специфику разработки под мобильные платформы, средства разработки, язык Objective-C, концепции применяемые при разработке под iOS. Расскажу шаги которые нужно сделать для создания вашего первого мобильного приложения.
SIGNALR - ОБМЕН СООБЩЕНИЯМИ В РЕАЛЬНОМ ВРЕМЕНИPavel Tsukanov
Поговорим об относительно новой библиотеке, разработанной Дэвидом Фаулером и Дамьеном Эдвардсом, основной задачей которой является мгновенный обмен сообщениями в Web приложениях, написанных на платформе .Net
Нас окружает мир сетей, мобильных устройств, сайтов, облаков. Чтобы работать с этим миром, придумано невероятное количество технологий и языков программирования. Есть ли среди них место для языков Си/Си++? Стоит ли тратить время на их изучение, стоит ли использовать их в своих проектах? Не пора ли этим языкам на пенсию? Эти темы в своем докладе обсудит Андрей Карпов, активно участвующий в жизни сообщества Си++-программистов. Забегая вперед можно утверждать - языки Си/Си++ живее всех живых. Андрей расскажет о развитии языка и новых возможностях, появившихся в Си++11. Многие возможности существенно облегчают работу программиста и сокращают объем кода.
РАЗРАБОТКА ПО С ИСПОЛЬЗОВАНИЕМ FINITE STATE MACHINE.Pavel Tsukanov
Мы расскажем что такое конечный автомат (Finite State Machine - FSM) и как его использовать при разработке ПО. Поделимся опытом использования, расскажем как улучшить дизайн программы или её отдельные части при помощи FSM. Рассмотрим некоторые реализации FSM.
TDD (Test-driven Development) как стиль разработки.Pavel Tsukanov
Как превратить рутинное написание Unit тестов в увлекательный процесс? Как побороть страхи, что система не будет работать должным образом? Как уверенно решать самые сложные для себя задачи? Я расскажу как TDD поможет найти ответы на эти и другие вопросы.
Наш сайт http://www.tuladev.net
Реализация REST и SOAP сервисов с помощью WCFPavel Tsukanov
На сегодняшний день одним из важнейших направлений в области разработки ПО является направление (веб)-сервисов. Сервисы позволяют строить большие распределенные системы. При этом подходов к построению сервисов сегодня как минимум два - SOAP и REST. В докладе расскажу как реализовать их при помощи WCF
Мы рассмотрим область применения, архитектуру и основные особенности такой известной операционной системы как Android. Также расскажем о процессе создания мобильного приложения TulaDev, о проблемах с которыми мы столкнулись и о способах их решения. Вы можете найти приложение для Android <a>на Google Play</a>
Cloud Computing Was Built for Web Developers—What Does v2 Look Like for Deep...Databricks
What we call the public cloud was developed primarily to manage and deploy web servers. The target audience for these products is Dev Ops. While this is a massive and exciting market, the world of Data Science and Deep Learning is very different — and possibly even bigger. Unfortunately, the tools available today are not designed for this new audience and the cloud needs to evolve. This talk would cover what the next 10 years of cloud computing will look like.
Building and deploying LLM applications with Apache AirflowKaxil Naik
Behind the growing interest in Generate AI and LLM-based enterprise applications lies an expanded set of requirements for data integrations and ML orchestration. Enterprises want to use proprietary data to power LLM-based applications that create new business value, but they face challenges in moving beyond experimentation. The pipelines that power these models need to run reliably at scale, bringing together data from many sources and reacting continuously to changing conditions.
This talk focuses on the design patterns for using Apache Airflow to support LLM applications created using private enterprise data. We’ll go through a real-world example of what this looks like, as well as a proposal to improve Airflow and to add additional Airflow Providers to make it easier to interact with LLMs such as the ones from OpenAI (such as GPT4) and the ones on HuggingFace, while working with both structured and unstructured data.
In short, this shows how these Airflow patterns enable reliable, traceable, and scalable LLM applications within the enterprise.
https://airflowsummit.org/sessions/2023/keynote-llm/
This contains the agenda of the Spark Meetup I organised in Bangalore on Friday, the 23rd of Jan 2014. It carries the slides for the talk I gave on distributed deep learning over Spark
Models for Parallel, Concurrent and Distributed Processing for Bioinformatics Software
Novartis Institute for BioMedical Research (NIBR) Geek Speak - Dec 4, 2014
Covers basics Artificial neural networks and motivation for deep learning and explains certain deep learning networks, including deep belief networks and autoencoders. It also details challenges of implementing a deep learning network at scale and explains how we have implemented a distributed deep learning network over Spark.
Data Orchestration Summit 2020 organized by Alluxio
https://www.alluxio.io/data-orchestration-summit-2020/
The Future of Computing is Distributed
Professor Ion Stoica, UC Berkeley RISELab
About Alluxio: alluxio.io
Engage with the open source community on slack: alluxio.io/slack
Architecting Solutions for the Manycore FutureTalbott Crowell
This talk will focus solution architects toward thinking about parallelism when designing applications and solutions specifically Threads vs Tasks on TPL, LINQ vs. PLINQ, and Object Oriented versus Functional Programming techniques. This talk will also compare programming languages, how languages differ when dealing with manycore programming, and the different advantages to these languages. Demonstration include C#, VB, and F# features for functional programming, LINQ and TPL. A demonstration of the Concurrency Visualizer in Visual Studio 2010 will also be included.
Towards high performance computing(hpc) through parallel programming paradigm...ijpla
Nowadays, we are to find out solutions to huge computing problems very rapidly. It brings the idea of parallel computing in which several machines or processors work cooperatively for computational tasks. In the past decades, there are a lot of variations in perceiving the importance of parallelism in computing machines. And it is observed that the parallel computing is a superior solution to many of the computing limitations like speed and density; non-recurring and high cost; and power consumption and heat dissipation etc. The commercial multiprocessors have emerged with lower prices than the mainframe machines and supercomputers machines. In this article the high performance computing (HPC) through parallel programming paradigms (PPPs) are discussed with their constructs and design approaches.
Тема доклада является логическим продолжением выступления Александра Бакулина в области робототехники и посвящена актуальной на сегодняшний момент проблеме технического зрения
CONTINUOUS INTEGRATION ДЛЯ ЧАЙНИКОВ ВМЕСТЕ С TEAMCITYPavel Tsukanov
то такое "Непрерывная Интеграция", зачем она нужна и с чем ее едят? Правда ли, что она нужна только для тестировщиков? На все эти вопросы мы постараемся найти ответы в ходе выступления Щербакова Ильи на нашей следующей юзер-группе.
По мотивам хабра ( http://habrahabr.ru/post/168645/ ), автор рассмотрит вопрос создания роботов в домашних условиях. Ожидается демонстрация робота в живую, в реальных боевых условиях!!!
Осуществим вводный экскурс в Node.JS. Действительно это что-то новое и гениальное? Что оно может, а что нет? Кому будет полезен? В каких случаях применять, а в каких нет? На все эти вопросы я постараюсь ответить в своём докладе.
Будет проведён сравнительный анализ возможностей создания анимаций как в Flash так и в HTML5. Неужели и правда HTML5 способен полностью заменить Flash?
Мы коснёмся вопросов теории и практики безопасности компьютеров. Вы думаете, что вы знаете об этом всё? Я попробую вас в этом переубедить. Поговорим о социальной инженерии, расскажу о бот сетях (так сказать из первых уст), что есть есть правда, а что есть миф в рассказах о хакерах. В конечном итоге этот доклад будет интересен людям, находящимся по обоим сторонам баррикады. Почему? Потому, что я был на обоих её сторонах...
Будет раскрыта животрепещущая тема о заработке в Интеренете. Где в интернете есть деньги, как их заработать и, в конечном итоге, получить и обналичить. Александр расскажет что делать, если идея уже есть, а понимания как из нее извлечь деньги еще нет. И, главное, денег на начальном этапе тоже нет. Будут также затронуты вопросы организации платежей через сайты, мобильные телефоны, мобильные приложения.
ORM технологии в .NET (Nhibernate, Linq To SQL, Entity Framework)Pavel Tsukanov
Расскажу зачем они вообще нужны. Пройдемся по технологиям и промоем им косточки. Рассмотрим достоинства и недостатки, а также где и когда лучше всего применять ту или иную ORM.
В данном докладе мы рассмотрим пять основных принципов дизайна классов в объектно-ориентированном проектировании, которые известны, как принципы SOLID. А также как обеспечить достаточный уровень гибкости, связанности, управляемости, стабильности и понятности кода.
Андрей Карпов
Вы узнаете, что такое статический анализ кода и историю его развития. Узнаете, как эффективно применять инструменты статического анализа в своей работе, увидите практические примеры использования этой методологии. Доклад ориентирован на программистов, использующих языки Си/Си++, но будет полезен всем
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
GridMate - End to end testing is a critical piece to ensure quality and avoid...ThomasParaiso2
End to end testing is a critical piece to ensure quality and avoid regressions. In this session, we share our journey building an E2E testing pipeline for GridMate components (LWC and Aura) using Cypress, JSForce, FakerJS…
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
2. HPC & GPU Supercomputing Groups
Non-profit, free to join groups hosted on www.meetup.com
A group for the application of cutting-edge HPC & GPU
supercomputing technology to cutting-edge business
problems.
Started January 2011 with New York group and reached
today 1000 members with all groups from Boston, Silicon
Valley, Chicago, New
Mexico, Denver, Seattle, Austin, Washington D.C., South
Florida, Tokyo
Please visit
www.SupercomputingGroup.com
for South Florida group.
4. Many thanks to Andrew Sheppard for providing
supporting content for this presentation.
Andrew is the organizer of New York meetup group and he is a
financial consultant with extensive experience in quantitative
financial analysis, trading-desk software development, and
technical management. Andrew is also the author of the
forthcoming book "Programming GPUs‖, to be published by
O’Reilly (www.oreilly.com).
5. ―Thinking in Parallel‖
is the term for making the conceptual leap that takes
a developer from writing programs that run on
hardware with little real parallelism, to writing
programs that execute efficiently on massively
parallel hardware, with 100’s and 1000’s of
cores, leading to very substantial speedups
(x10, x100 and beyond).
6. ―[A]nd in this precious phial is the power to think twice
as fast, move twice as quickly, do twice as much
work in a given time as you could otherwise do.‖
—H. G. Wells, ―The New Accelerator‖ (1901)
7. Serial programs are traditional (most of the programs are
serial), sequential (just sequence of tasks) and relatively easy
to think the flow.
For example : Prefix Sum ( Scan )
5 1 8 11
where binary associative operator is summation.
5 5+1 5+1+8 5 + 1 + 8 + 11
5 6 14 25
data[] = {5, 1, 8, 11, 4}
forall i from 1 to n do
data[i] = data[i – 1] + data[i]
8. But sequential thinking is about to change, because the
serial performance improvement has slowed down from
50% to %20 since 2002 and we can not expect huge
improvement in serial performance anymore.
Therefore, programming is going parallel.
9. Multi- and many-core computing
hitting the mainstream
Today we have 4-12 cores, in few years 32 cores and Intel is
predicting in 2015 we will have 100 cores.
AMD Opteron (12) IBM Power 7 (8) Intel Xeon (12)
Sun UltraSparc T3 (16) Cell (9)
NVIDIA GeForce (1024)
Adapteva (4096) Tilera Tile-Gx (100)
There is lots of effort towards developing good runtime
compilers, debuggers and OS support:
MS TPL, Intel TBB, MPI, PVM, pthreads, PLINQ, OpenMP, MS Concurrency
Runtime, MS Dryad, MS C++ AMP, NVIDIA CUDA C, ATI
APP, OpenCL, Microsoft DirectCompute, Brooks, Shaders, PGI CUDA
Fortran, GPU.Net, HMPP, Thrust etc.
More than one hundred parallel programming languages in 2008
( http://perilsofparallel.blogspot.com/2008/09/101-parallel-languages-part-
1.html or http://tinyurl.com/3p4a8to )
10. What are some problems
moving into a multi-core world?
A lot of companies have a huge code base developed
with little or no parallelism. Converting those great product
to multi-core will take time.
We haven’t been teaching much about parallelism for
many years. Most students we educated in the last 10
years know very little about parallelism.
Engineers need to understand parallelism, understand all
the issues of parallelism, to utilize all these cores.
Parallel thinking is not the latest API, library or hardware.
Parallel thinking is a set of core ideas we have to identify
and teach our students and workforce.
11. Writing good serial software was hard, writing good
parallel software is harder: require new tools, new
techniques, and a new ―Thinking in Parallel‖ mindset.
Performance
Competitive
Advantage
Serial Applications
Time
2004 – Multi-core is on desktop
12. Parallel Prefix Sum
1 1
Parallel Prefix Sum (Scan) with CUDA (NVIDIA) 6
(@ http://tinyurl.com/3s9as2j)
14. Concurrency Parallelism
Programming issue Property of the machine
Single processor Multi-processor
Goal : running multiple Goal : speedup
interleaved threads Threads are executed
Only one thread executes at simultaneously
any given time
Time >>>
Time >>>
Task A Task B Task A Task B
Thread 1 Thread 1 Thread 2
Thread 2 Thread 1 Thread 2
Thread 1 Thread 1 Thread 2
Thread 2
Thread 1
Thread 2
15. Flynn’s Taxonomy of
Architectures
Single Instruction/
Single Instruction/ Multiple Data
Single Data
Multiple Instruction/ Multiple Instruction/
Single Data Multiple Data
18. Analyzing Parallelism
Amdahl’s Law helps to predict the theoretical
maximum speedup on fixed problem size:
Gustafson's Law proposes that larger problems
can be solved by scaling the parallel computing
power :
S = rs + n . rp
21. Design Patterns
Finding Concurrency Design Space
Algorithm Structure Design Space
Supporting Structures Design Space
Implementation Mechanism Design
Space
22. Finding Concurrency Design Space
Decomposition Dependency Analysis
Task Decomposition Group Task
Design Evaluation
Data Decomposition Order Task
Data-Flow Decomposition Data Sharing
Algorithm Structure Design Space
Organize by Tasks Organize by Data Decomp. Organize by Flow of Data
Task Parallelism Geometric Decomposition Pipeline
Divide and Conquer Recursive Data Event-Based Coordination
Supporting Structures Design Space
Program Structures Data Structures
SPMD Loop Parallelism Shared Data Distributed Array
Master / Worker Fork / Join Shared Queue
Implementation Mechanism Design Space
23. 8 Rules for ―Thinking in
Parallel‖
1. Identify truly independent computations.
2. Implement parallelism at the highest level possible.
3. Plan early for scalability to take advantage of increasing numbers
of cores.
4. Hide parallelization in libraries.
5. Use the right parallel programming model.
6. Never assume a particular order of execution.
7. Use non-shared storage whenever possible.
8. Dare to change the algorithm for a better chance of parallelism.
9. Be creative and pragmatic.
24. Pragmatic Parallelization
Programming, in practice, is pragmatic.
Most people prefer a practical ―good enough‖
solution over an ―ideal‖ solution.
Chaotic Pragmatic Bureaucratic
Importance of Rules
25. Parallel Programming Support
CPU GPU
MS TPL NVIDIA CUDA C
Intel TBB ATI APP
MPI OpenCL
PVM Microsoft DirectCompute
pthreads Brooks
PLINQ Shaders
OpenMP PGI CUDA Fortran
MS Concurrency Runtime GPU.Net
MS Dryad HMPP
MS C++ AMP Thrust
etc. etc.
26. Links and References
Patterns for Parallel Programming. Mattson, Timothy G.; Sanders,
Beverly A.; Massingill, Berna L. (2004-09-15). Addison-Wesley
Professional.
An Introduction to Parallel Programming. Pacheco, Peter (2011-01-
07). Morgan Kaufmann.
The Art of Concurrency . Breshears, Clay (2009-05-07). O'Reilly
Media.
Wikipedia
http://newsroom.intel.com/community/intel_newsroom/blog/2011/0
9/15/the-future-accelerated-multi-core-goes-mainstream-
computing-pushed-to-extremes
http://perilsofparallel.blogspot.com/2011/09/conversation-with-
intels-james-reinders.html
Traditionally, much of computer programming has been serial in nature. A program begins at a well defined entry point and works through a sequence of tasks in succession. Designing serial programs are relatively easy because you can think sequentially for the most part. This is the simplified programming model that most programmers today have learned and use. But it’s about to change, because programming is going parallel. And given how hard it seems to be to write good serial software (judging by how many software projects struggle even in the serial world) then the new challenges of parallel programming will require new tools, new techniques, and a new “Thinking in Parallel” mindset. This short talk focuses on some of the issues relating to “Thinking in Parallel”. It’s written from the perspective of a practitioner in-the-trenches and not from the perspective of a computer scientist. Nor is it a comprehensive overview of topic. It’s merely a starting point. My hope is that even though the meetup group ranges from newbies to experts, everyone will come away with at least one idea to think about. The journey begins …
Traditionally, much of computer programming has been serial in nature. A program begins at a well defined entry point and works through a sequence of tasks in succession. Designing serial programs are relatively easy because you can think sequentially for the most part. This is the simplified programming model that most programmers today have learned and use. But it’s about to change, because programming is going parallel. And given how hard it seems to be to write good serial software (judging by how many software projects struggle even in the serial world) then the new challenges of parallel programming will require new tools, new techniques, and a new “Thinking in Parallel” mindset. This short talk focuses on some of the issues relating to “Thinking in Parallel”. It’s written from the perspective of a practitioner in-the-trenches and not from the perspective of a computer scientist. Nor is it a comprehensive overview of topic. It’s merely a starting point. My hope is that even though the meetup group ranges from newbies to experts, everyone will come away with at least one idea to think about. The journey begins …
Traditionally, much of computer programming has been serial in nature. A program begins at a well defined entry point and works through a sequence of tasks in succession. Designing serial programs are relatively easy because you can think sequentially for the most part. This is the simplified programming model that most programmers today have learned and use. But it’s about to change, because programming is going parallel. And given how hard it seems to be to write good serial software (judging by how many software projects struggle even in the serial world) then the new challenges of parallel programming will require new tools, new techniques, and a new “Thinking in Parallel” mindset. This short talk focuses on some of the issues relating to “Thinking in Parallel”. It’s written from the perspective of a practitioner in-the-trenches and not from the perspective of a computer scientist. Nor is it a comprehensive overview of topic. It’s merely a starting point. My hope is that even though the meetup group ranges from newbies to experts, everyone will come away with at least one idea to think about. The journey begins …A large percentage of people who provide applications are going to have to care about parallelism in order to match the capabilities of their competitors.
Before we begin, let’s clear up a common misunderstanding. What’s the difference between concurrent versus parallel? They both share some common concepts and difficulties, but they are different. Concurrent execution of two or more programs (or multiple tasks within a single program) means that only a single thread executes at any given time, but switching between threads is so rapid that it appears as though all tasks proceed concurrently. Parallel execution means that two or more threads are actually running simultaneously in hardware. It is not as though the tasks appear to proceed in parallel, they really are running in parallel. The difference between can be illustrated simply as follows. [diagram]Of course, multicore CPUs, GPUs and clusters of the same are all about running in parallel. In the case of individual GPUs and large clusters of CPUs, they run in a massively parallel way.
SynchronizationWhenever two or more tasks need the same resources, there is the possibility for contention. For multi-threaded applications this is often solved with mutexes, semaphores, critical sections and their like. On GPUs there are likewise synchronization objects.Race ConditionA major obstacle to efficient parallel execution is resource contention, whether that be for memory or I/O (though one could argue all data access is I/O of one form or another.) Resource contention is particularly prevalent for MISD and MIMD execution models. But the potential is always present when two executing tasks need to share the same resource which itself is not parallelizable.
There are dozens of different parallel architectures, among them networks of workstations, clusters of off-the-shelf PCs, massively parallel supercomputers, tightly coupled symmetric multiprocessors, and multiprocessor workstations.Flynn’s taxonomy categorizes all computers according to the number of instruction streams and data streams they have, where a stream is a sequence of instructions or data on which a computer operatesSISD Single instruction, single data (SISD) means a single thread or core operating on a single piece of data. SIMD Single instruction, multiple data (SIMD) means the same code running on multiple threads or cores, but operating on different parts of the data set. This is the execution model for GPUs. MISD Multiple instructions, single data (MISD) means different programs running in multiple threads or cores operating on the same data. MIMD Multiple instructions, multiple data (MIMD) means different programs running in multiple threads or cores operating on different parts of the data set.
Since the GOF (Gang of Four: Gamma, Helm, Johnson and Vissides) wrote “Design Patterns: Elements of Reusable Object-Oriented Software”, programming patterns have gained in popularity. They are now used in one way or another by most mainstream programmers. Not surprisingly, design patterns for parallel programming have emerged and attempt, to one degree or another, to map a problem onto an underlying execution model.
Task Decomposition Task decomposition, as its name implies, breaks the problem into parts so that each can be independently assigned to a different computational resource to run in parallel. Data Decomposition Data decomposition is breaking your data set into smaller parts so that each can be processed separately by different compute resources. For data decomposition what is needed is not just a partitioning of the data, but rather a data plan that includes such things as how the data will be encoded and moved around. Both can affect performance considerably, because some encodings are more efficient and compact than others, and leaving data in-situ and doing multiple operations on it is clearly better than the converseGroup Tasks If a group shares a temporal constraint (for example, waiting on one group to finish filling a file before another group can begin reading it), we can satisfy that constraint once for the whole group. If a group of tasks must work together on a shared data structure, the required synchronization can be worked out once for the whole group. If a set of tasks are independent, combining them into a single group and scheduling them for execution as a single large group can simplify the design and increase the available concurrency (thereby letting the solution scale to more PEs).Order Tasksa way of decomposing a problem into tasks and a way of collecting these tasks into logically related groups, how must these groups of tasks be ordered to satisfy constraints among tasks?
These rules are from “The Art of Concurrency” by Clay Breshears. I’ve modified them slightly so they are equally applicable to “Thinking in Parallel”:
Traditional languages gain support for parallel programming through libraries that seek to hide as much of the underlying hardware and parallelism as possibleFunctional languages have, in recent times, gained in popularity because of their strong support for parallelism; even if parallelism isn’t built into the language it is often well supported through the “functional” style. Some functional languages, notably Erlang, are intrinsically parallel given that it was intended for parallel execution by design. OtherScala, for example, is a language that supports a mix of programming models and styles, from OOP to functional. It also supports parallel programming quite well. Given the large number of languages out there, you will no doubt find a parallel programming language to your taste!