User Level I O Accelerations For High Performance Deep Learning Applications

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User-Level I/O Accelerations for High-Performance Deep Learning Applications

Author : Yue Zhu
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (134 download)

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Book Synopsis User-Level I/O Accelerations for High-Performance Deep Learning Applications by : Yue Zhu

Download or read book User-Level I/O Accelerations for High-Performance Deep Learning Applications written by Yue Zhu and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the popularity of microprocessors and scale-out system architectures, many large-scale high-performance computing (HPC) systems are built from a collection of compute servers, with an identical set of resources such as CPU, memory, and storage. A variety of applications have been leveraging the tremendous computation capacity on these large-scale HPC systems. Scientific applications and deep learning (DL) training are two of the popular workloads on HPC systems. However, with the rapid growth of the computation power, it has also become increasingly important to fill in the computation and I/O performance gap for these applications and workloads on HPC systems. In recent years, many research efforts have been made to explore user-level file systems on HPC systems for various workloads due to the flexibility of implementation and maintenance in user space. In particular, scientific applications which have two typical I/O patterns (checkpoint/restart and multi-dimensional I/O) have been able to utilize different specialized user-level file systems in a single job. However, non-trivial overheads can be introduced in such a method. We need to carefully review the overheads in order to mit- igate the performance degradation. In addition, the existing methods of using user-level file systems are not sufficient to meet the fundamental I/O needs of large-scale DL training on HPC systems. Firstly, in DL training, random samples are organized into batches to update model parameters in iterations. This is to avoid the model being biased by the input sequences' noise, which allows faster convergence speed and reduces memory consumption during the training computation. This results in massive random reads for data shuffling across the entire datasets on storage systems. Such a random read I/O pattern is significantly different from the traditional scientific workloads. Moreover, leadership HPC systems are often equipped with a large pool of burst buffers in the form of flash or non-volatile memory (NVM) devices. DL applica- tions running on these systems face the resource underutilization problem. This is because NVM devices' performance with respect to low latency and high bandwidth can be severely underutilized under heavy CPU and memory workloads. In this environment, the flash or NVMe storage devices are capable of low-latency and high-bandwidth I/O services, but the complex software stack significantly hampers such capabilities for I/O processing in the kernel. Also, due to DL training accuracy and performance concerns, the storage capacity and the performance of on-node storage devices on the nodes allocated to the training job are not sufficient to store an entire dataset and match the training speed, respectively.This dissertation focus on applying user-level file systems on HPC systems. Our overarching goal is to accelerate the I/O supports on HPC systems through specialized user-level file systems for popular workloads. In specific, we want to bring lightweight user-level file systems as efficient intermediates to reduce the performance overhead and ease the use of multiple FUSE file systems in a single job, orchestrate the data movement between storage tiers and DL applications, and improve the storage resource utilization for a pool of NVMe SSDs in DL training. Based on these design goals, we investigate the issues and challenges when applying existing user-level file systems to the popular workloads, then propose three strategies to meet our goals. Firstly, we have studied the problem of excessive cost in crossing the user-kernel boundary when using multiple traditional user-level file systems, and we design Direct-FUSE to support multiple FUSE file sys- tems as well as other, custom user-level file systems in user space without the need to cross the user/kernel boundary into the FUSE kernel module. All layers of Direct-FUSE are in user space, and applications can directly use pre-defined unified file system calls to interact with different user-defined file systems. Our performance results show that Direct-FUSE can outperform some native FUSE file systems and does not add significant overhead over backend file systems. Secondly, we examine the I/O patterns of deep neural networks and study the performance overheads when loading samples from some popular DL applications. Then, we introduce an entropy-aware I/O framework called DeepIO for large-scale deep learning on HPC systems. It coordinates the use of memory, communication, and I/O resources for efficient training of datasets. DeepIO features an I/O pipeline that utilizes several novel optimizations: RDMA (Remote Direct Memory Access)-assisted in-situ shuffling, input pipelining, and entropy-aware opportunistic ordering. It outperforms the state-of-the-art persistent memory based distributed file systems for efficient sample load- ing during DL training. Thirdly, besides examining the I/O patterns of deep neural networks, we also reveal a critical need of loading many small samples randomly and the issues of storage resources underutilization for successful training. Based on these understandings, we design a specialized Deep Learning File System (DLFS) with an in-memory tree-based sample directory for metadata management and user-level storage disaggregation through the SPDK protocol. Our experimental results show that DLFS can dramatically improve the throughput of training for deep neural networks when compared with the kernel-based local Ext4 file system. Furthermore, DLFS demonstrates its capability of achieving efficient user-level storage disaggregation with very little CPU utilization. In conclusion, the first branch concentrates on enriching the functionality and enhancing the performance of the Direct-FUSE framework; the second and third branches focus on wisely storing and prefetching datasets with the coordination of hierarchical storage tiers and fast interconnect, respectively. By exploring these three branches, we can further accelerate the specialized user-level file systems for popular workloads on HPC systems.

High-Performance Big Data Computing

Author : Dhabaleswar K. Panda
Publisher : MIT Press
ISBN 13 : 0262369427
Total Pages : 275 pages
Book Rating : 4.2/5 (623 download)

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Book Synopsis High-Performance Big Data Computing by : Dhabaleswar K. Panda

Download or read book High-Performance Big Data Computing written by Dhabaleswar K. Panda and published by MIT Press. This book was released on 2022-08-02 with total page 275 pages. Available in PDF, EPUB and Kindle. Book excerpt: An in-depth overview of an emerging field that brings together high-performance computing, big data processing, and deep lLearning. Over the last decade, the exponential explosion of data known as big data has changed the way we understand and harness the power of data. The emerging field of high-performance big data computing, which brings together high-performance computing (HPC), big data processing, and deep learning, aims to meet the challenges posed by large-scale data processing. This book offers an in-depth overview of high-performance big data computing and the associated technical issues, approaches, and solutions. The book covers basic concepts and necessary background knowledge, including data processing frameworks, storage systems, and hardware capabilities; offers a detailed discussion of technical issues in accelerating big data computing in terms of computation, communication, memory and storage, codesign, workload characterization and benchmarking, and system deployment and management; and surveys benchmarks and workloads for evaluating big data middleware systems. It presents a detailed discussion of big data computing systems and applications with high-performance networking, computing, and storage technologies, including state-of-the-art designs for data processing and storage systems. Finally, the book considers some advanced research topics in high-performance big data computing, including designing high-performance deep learning over big data (DLoBD) stacks and HPC cloud technologies.

High Performance Computing

Author : Amanda Bienz
Publisher : Springer Nature
ISBN 13 : 3031408438
Total Pages : 677 pages
Book Rating : 4.0/5 (314 download)

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Book Synopsis High Performance Computing by : Amanda Bienz

Download or read book High Performance Computing written by Amanda Bienz and published by Springer Nature. This book was released on 2023-09-25 with total page 677 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume constitutes the papers of several workshops which were held in conjunction with the 38th International Conference on High Performance Computing, ISC High Performance 2023, held in Hamburg, Germany, during May 21–25, 2023. The 49 revised full papers presented in this book were carefully reviewed and selected from 70 submissions. ISC High Performance 2023 presents the following workshops: 2nd International Workshop on Malleability Techniques Applications in High-Performance Computing (HPCMALL) 18th Workshop on Virtualization in High-Performance Cloud Computing (VHPC 23) HPC I/O in the Data Center (HPC IODC) Workshop on Converged Computing of Cloud, HPC, and Edge (WOCC’23) 7th International Workshop on In Situ Visualization (WOIV’23) Workshop on Monitoring and Operational Data Analytics (MODA23) 2nd Workshop on Communication, I/O, and Storage at Scale on Next-Generation Platforms: Scalable Infrastructures First International Workshop on RISC-V for HPC Second Combined Workshop on Interactive and Urgent Supercomputing (CWIUS) HPC on Heterogeneous Hardware (H3)

Applied Machine Learning and High-Performance Computing on AWS

Author : Mani Khanuja
Publisher : Packt Publishing Ltd
ISBN 13 : 1803244445
Total Pages : 382 pages
Book Rating : 4.8/5 (32 download)

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Book Synopsis Applied Machine Learning and High-Performance Computing on AWS by : Mani Khanuja

Download or read book Applied Machine Learning and High-Performance Computing on AWS written by Mani Khanuja and published by Packt Publishing Ltd. This book was released on 2022-12-30 with total page 382 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build, train, and deploy large machine learning models at scale in various domains such as computational fluid dynamics, genomics, autonomous vehicles, and numerical optimization using Amazon SageMaker Key FeaturesUnderstand the need for high-performance computing (HPC)Build, train, and deploy large ML models with billions of parameters using Amazon SageMakerLearn best practices and architectures for implementing ML at scale using HPCBook Description Machine learning (ML) and high-performance computing (HPC) on AWS run compute-intensive workloads across industries and emerging applications. Its use cases can be linked to various verticals, such as computational fluid dynamics (CFD), genomics, and autonomous vehicles. This book provides end-to-end guidance, starting with HPC concepts for storage and networking. It then progresses to working examples on how to process large datasets using SageMaker Studio and EMR. Next, you'll learn how to build, train, and deploy large models using distributed training. Later chapters also guide you through deploying models to edge devices using SageMaker and IoT Greengrass, and performance optimization of ML models, for low latency use cases. By the end of this book, you'll be able to build, train, and deploy your own large-scale ML application, using HPC on AWS, following industry best practices and addressing the key pain points encountered in the application life cycle. What you will learnExplore data management, storage, and fast networking for HPC applicationsFocus on the analysis and visualization of a large volume of data using SparkTrain visual transformer models using SageMaker distributed trainingDeploy and manage ML models at scale on the cloud and at the edgeGet to grips with performance optimization of ML models for low latency workloadsApply HPC to industry domains such as CFD, genomics, AV, and optimizationWho this book is for The book begins with HPC concepts, however, it expects you to have prior machine learning knowledge. This book is for ML engineers and data scientists interested in learning advanced topics on using large datasets for training large models using distributed training concepts on AWS, deploying models at scale, and performance optimization for low latency use cases. Practitioners in fields such as numerical optimization, computation fluid dynamics, autonomous vehicles, and genomics, who require HPC for applying ML models to applications at scale will also find the book useful.

Deep Learning with JAX

Author : Grigory Sapunov
Publisher : Manning
ISBN 13 : 9781633438880
Total Pages : 0 pages
Book Rating : 4.4/5 (388 download)

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Book Synopsis Deep Learning with JAX by : Grigory Sapunov

Download or read book Deep Learning with JAX written by Grigory Sapunov and published by Manning. This book was released on 2024-10-29 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Accelerate deep learning and other number-intensive tasks with JAX, Google’s awesome high-performance numerical computing library. In Deep Learning with JAX you will learn how to: Use JAX for numerical calculations Build differentiable models with JAX primitives Run distributed and parallelized computations with JAX Use high-level neural network libraries such as Flax and Haiku Leverage libraries and modules from the JAX ecosystem The JAX numerical computing library tackles the core performance challenges at the heart of deep learning and other scientific computing tasks. By combining Google’s Accelerated Linear Algebra platform (XLA) with a hyper-optimized version of NumPy and a variety of other high-performance features, JAX delivers a huge performance boost in low-level computations and transformations. Deep Learning with JAX is a hands-on guide to using JAX for deep learning and other mathematically-intensive applications. Google Developer Expert Grigory Sapunov steadily builds your understanding of JAX’s concepts. The engaging examples introduce the fundamental concepts on which JAX relies and then show you how to apply them to real-world tasks. You’ll learn how to use JAX’s ecosystem of high-level libraries and modules, and also how to combine TensorFlow and PyTorch with JAX for data loading and deployment. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. About the technology The JAX Python mathematics library is used by many successful deep learning organizations, including Google’s groundbreaking DeepMind team. This exciting newcomer already boasts an amazing ecosystem of tools including high-level deep learning libraries Flax by Google, Haiku by DeepMind, gradient processing and optimization libraries, libraries for evolutionary computations, federated learning, and much more! JAX brings a functional programming mindset to Python deep learning, letting you improve your composability and parallelization in a cluster. About the book Deep Learning with JAX teaches you how to use JAX and its ecosystem to build neural networks. You’ll learn by exploring interesting examples including an image classification tool, an image filter application, and a massive scale neural network with distributed training across a cluster of TPUs. Discover how to work with JAX for hardware and other low-level aspects and how to solve common machine learning problems with JAX. By the time you’re finished with this awesome book, you’ll be ready to start applying JAX to your own research and prototyping! About the reader For intermediate Python programmers who are familiar with deep learning. About the author Grigory Sapunov is a co-founder and CTO of Intento. He is a software engineer with more than twenty years of experience. Grigory holds a Ph.D. in artificial intelligence and is a Google Developer Expert in Machine Learning.

Intelligent Computing Theories and Application

Author : De-Shuang Huang
Publisher : Springer Nature
ISBN 13 : 303084529X
Total Pages : 778 pages
Book Rating : 4.0/5 (38 download)

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Book Synopsis Intelligent Computing Theories and Application by : De-Shuang Huang

Download or read book Intelligent Computing Theories and Application written by De-Shuang Huang and published by Springer Nature. This book was released on 2021-08-09 with total page 778 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set of LNCS 12836 and LNCS 12837 constitutes - in conjunction with the volume LNAI 12838 - the refereed proceedings of the 17th International Conference on Intelligent Computing, ICIC 2021, held in Shenzhen, China in August 2021. The 192 full papers of the three proceedings volumes were carefully reviewed and selected from 458 submissions. The ICIC theme unifies the picture of contemporary intelligent computing techniques as an integral concept that highlights the trends in advanced computational intelligence and bridges theoretical research with applications. The theme for this conference is “Advanced Intelligent Computing Methodologies and Applications.” The papers are organized in the following subsections: Intelligent Computing in Computer Vision, Intelligent Control and Automation, Intelligent Modeling Technologies for Smart Cities, Machine Learning, and Theoretical Computational Intelligence and Applications.

IBM Reference Architecture for High Performance Data and AI in Healthcare and Life Sciences

Author : Dino Quintero
Publisher : IBM Redbooks
ISBN 13 : 073845690X
Total Pages : 88 pages
Book Rating : 4.7/5 (384 download)

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Book Synopsis IBM Reference Architecture for High Performance Data and AI in Healthcare and Life Sciences by : Dino Quintero

Download or read book IBM Reference Architecture for High Performance Data and AI in Healthcare and Life Sciences written by Dino Quintero and published by IBM Redbooks. This book was released on 2019-09-08 with total page 88 pages. Available in PDF, EPUB and Kindle. Book excerpt: This IBM® Redpaper publication provides an update to the original description of IBM Reference Architecture for Genomics. This paper expands the reference architecture to cover all of the major vertical areas of healthcare and life sciences industries, such as genomics, imaging, and clinical and translational research. The architecture was renamed IBM Reference Architecture for High Performance Data and AI in Healthcare and Life Sciences to reflect the fact that it incorporates key building blocks for high-performance computing (HPC) and software-defined storage, and that it supports an expanding infrastructure of leading industry partners, platforms, and frameworks. The reference architecture defines a highly flexible, scalable, and cost-effective platform for accessing, managing, storing, sharing, integrating, and analyzing big data, which can be deployed on-premises, in the cloud, or as a hybrid of the two. IT organizations can use the reference architecture as a high-level guide for overcoming data management challenges and processing bottlenecks that are frequently encountered in personalized healthcare initiatives, and in compute-intensive and data-intensive biomedical workloads. This reference architecture also provides a framework and context for modern healthcare and life sciences institutions to adopt cutting-edge technologies, such as cognitive life sciences solutions, machine learning and deep learning, Spark for analytics, and cloud computing. To illustrate these points, this paper includes case studies describing how clients and IBM Business Partners alike used the reference architecture in the deployments of demanding infrastructures for precision medicine. This publication targets technical professionals (consultants, technical support staff, IT Architects, and IT Specialists) who are responsible for providing life sciences solutions and support.

Machine Learning Paradigms

Author : George A. Tsihrintzis
Publisher : Springer Nature
ISBN 13 : 3030497240
Total Pages : 429 pages
Book Rating : 4.0/5 (34 download)

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Book Synopsis Machine Learning Paradigms by : George A. Tsihrintzis

Download or read book Machine Learning Paradigms written by George A. Tsihrintzis and published by Springer Nature. This book was released on 2020-07-23 with total page 429 pages. Available in PDF, EPUB and Kindle. Book excerpt: At the dawn of the 4th Industrial Revolution, the field of Deep Learning (a sub-field of Artificial Intelligence and Machine Learning) is growing continuously and rapidly, developing both theoretically and towards applications in increasingly many and diverse other disciplines. The book at hand aims at exposing its reader to some of the most significant recent advances in deep learning-based technological applications and consists of an editorial note and an additional fifteen (15) chapters. All chapters in the book were invited from authors who work in the corresponding chapter theme and are recognized for their significant research contributions. In more detail, the chapters in the book are organized into six parts, namely (1) Deep Learning in Sensing, (2) Deep Learning in Social Media and IOT, (3) Deep Learning in the Medical Field, (4) Deep Learning in Systems Control, (5) Deep Learning in Feature Vector Processing, and (6) Evaluation of Algorithm Performance. This research book is directed towards professors, researchers, scientists, engineers and students in computer science-related disciplines. It is also directed towards readers who come from other disciplines and are interested in becoming versed in some of the most recent deep learning-based technological applications. An extensive list of bibliographic references at the end of each chapter guides the readers to probe deeper into their application areas of interest.

IBM Power System S822LC for High Performance Computing Introduction and Technical Overview

Author : Scott Vetter
Publisher : IBM Redbooks
ISBN 13 : 073845561X
Total Pages : 82 pages
Book Rating : 4.7/5 (384 download)

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Book Synopsis IBM Power System S822LC for High Performance Computing Introduction and Technical Overview by : Scott Vetter

Download or read book IBM Power System S822LC for High Performance Computing Introduction and Technical Overview written by Scott Vetter and published by IBM Redbooks. This book was released on 2017-09-28 with total page 82 pages. Available in PDF, EPUB and Kindle. Book excerpt: This IBM® RedpaperTM publication is a comprehensive guide that covers the IBM Power SystemTM S822LC for High Performance Computing (HPC) server (8335-GTB model). The S822LC for HPC server is designed for high-performance computing applications that support the Linux operating system and high-performance data analytics, the enterprise data center, and accelerated cloud deployments. This paper introduces the major innovative S822LC for HPC server features and their relevant functions: Powerful IBM POWER8® processors that offer 16 cores at 3.259 GHz with 3.857 GHz turbo performance or 20 cores at 2.860 GHz with 3.492 GHz turbo A 19-inch rack-mount 2U configuration NVIDIA NVLink technology for exceptional processor-to-accelerator intercommunication Four dedicated connectors for the NVIDIA Tesla P100 GPU This publication is for professionals who want to acquire a better understanding of IBM Power Systems products and is intended for the following audience: Clients Sales and marketing professionals Technical support professionals IBM Business Partners Independent software vendors This paper expands the set of IBM Power Systems documentation by providing a desktop reference that offers a detailed technical description of the S822LC for HPC server. This paper does not replace the latest marketing materials and configuration tools. It is intended as an additional source of information that, together with existing sources, can be used to enhance your knowledge of IBM server solutions.

High Performance Computing in Biomimetics

Author : Kamarul Arifin Ahmad
Publisher : Springer Nature
ISBN 13 : 9819710170
Total Pages : 309 pages
Book Rating : 4.8/5 (197 download)

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Book Synopsis High Performance Computing in Biomimetics by : Kamarul Arifin Ahmad

Download or read book High Performance Computing in Biomimetics written by Kamarul Arifin Ahmad and published by Springer Nature. This book was released on with total page 309 pages. Available in PDF, EPUB and Kindle. Book excerpt:

IBM Power Systems L and LC Server Positioning Guide

Author : Scott Vetter
Publisher : IBM Redbooks
ISBN 13 : 0738455814
Total Pages : 30 pages
Book Rating : 4.7/5 (384 download)

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Book Synopsis IBM Power Systems L and LC Server Positioning Guide by : Scott Vetter

Download or read book IBM Power Systems L and LC Server Positioning Guide written by Scott Vetter and published by IBM Redbooks. This book was released on 2017-02-16 with total page 30 pages. Available in PDF, EPUB and Kindle. Book excerpt: This IBM® RedpaperTM publication is written to assist you in locating the optimal server/workload fit within the IBM Power SystemsTM L and IBM OpenPOWER LC product lines. IBM has announced several scale-out servers, and as a partner in the OpenPOWER organization, unique design characteristics that are engineered into the LC line have broadened the suite of available workloads beyond typical client OS hosting. This paper looks at the benefits of the Power Systems L servers and OpenPOWER LC servers, and how they are different, providing unique benefits for Enterprise workloads and use cases.

Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI

Author : Jeffrey Nichols
Publisher : Springer Nature
ISBN 13 : 3030633934
Total Pages : 555 pages
Book Rating : 4.0/5 (36 download)

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Book Synopsis Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI by : Jeffrey Nichols

Download or read book Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI written by Jeffrey Nichols and published by Springer Nature. This book was released on 2020-12-22 with total page 555 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the revised selected papers of the 17th Smoky Mountains Computational Sciences and Engineering Conference, SMC 2020, held in Oak Ridge, TN, USA*, in August 2020. The 36 full papers and 1 short paper presented were carefully reviewed and selected from a total of 94 submissions. The papers are organized in topical sections of computational applications: converged HPC and artificial intelligence; system software: data infrastructure and life cycle; experimental/observational applications: use cases that drive requirements for AI and HPC convergence; deploying computation: on the road to a converged ecosystem; scientific data challenges. *The conference was held virtually due to the COVID-19 pandemic.

Operating AI

Author : Ulrika Jagare
Publisher : John Wiley & Sons
ISBN 13 : 1119833213
Total Pages : 237 pages
Book Rating : 4.1/5 (198 download)

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Book Synopsis Operating AI by : Ulrika Jagare

Download or read book Operating AI written by Ulrika Jagare and published by John Wiley & Sons. This book was released on 2022-04-19 with total page 237 pages. Available in PDF, EPUB and Kindle. Book excerpt: A holistic and real-world approach to operationalizing artificial intelligence in your company In Operating AI, Director of Technology and Architecture at Ericsson AB, Ulrika Jägare, delivers an eye-opening new discussion of how to introduce your organization to artificial intelligence by balancing data engineering, model development, and AI operations. You'll learn the importance of embracing an AI operational mindset to successfully operate AI and lead AI initiatives through the entire lifecycle, including key areas such as; data mesh, data fabric, aspects of security, data privacy, data rights and IPR related to data and AI models. In the book, you’ll also discover: How to reduce the risk of entering bias in our artificial intelligence solutions and how to approach explainable AI (XAI) The importance of efficient and reproduceable data pipelines, including how to manage your company's data An operational perspective on the development of AI models using the MLOps (Machine Learning Operations) approach, including how to deploy, run and monitor models and ML pipelines in production using CI/CD/CT techniques, that generates value in the real world Key competences and toolsets in AI development, deployment and operations What to consider when operating different types of AI business models With a strong emphasis on deployment and operations of trustworthy and reliable AI solutions that operate well in the real world—and not just the lab—Operating AI is a must-read for business leaders looking for ways to operationalize an AI business model that actually makes money, from the concept phase to running in a live production environment.

Learning Deep Learning

Author : Magnus Ekman
Publisher : Addison-Wesley Professional
ISBN 13 : 0137470290
Total Pages : 1106 pages
Book Rating : 4.1/5 (374 download)

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Book Synopsis Learning Deep Learning by : Magnus Ekman

Download or read book Learning Deep Learning written by Magnus Ekman and published by Addison-Wesley Professional. This book was released on 2021-07-19 with total page 1106 pages. Available in PDF, EPUB and Kindle. Book excerpt: NVIDIA's Full-Color Guide to Deep Learning: All You Need to Get Started and Get Results "To enable everyone to be part of this historic revolution requires the democratization of AI knowledge and resources. This book is timely and relevant towards accomplishing these lofty goals." -- From the foreword by Dr. Anima Anandkumar, Bren Professor, Caltech, and Director of ML Research, NVIDIA "Ekman uses a learning technique that in our experience has proven pivotal to success—asking the reader to think about using DL techniques in practice. His straightforward approach is refreshing, and he permits the reader to dream, just a bit, about where DL may yet take us." -- From the foreword by Dr. Craig Clawson, Director, NVIDIA Deep Learning Institute Deep learning (DL) is a key component of today's exciting advances in machine learning and artificial intelligence. Learning Deep Learning is a complete guide to DL. Illuminating both the core concepts and the hands-on programming techniques needed to succeed, this book is ideal for developers, data scientists, analysts, and others--including those with no prior machine learning or statistics experience. After introducing the essential building blocks of deep neural networks, such as artificial neurons and fully connected, convolutional, and recurrent layers, Magnus Ekman shows how to use them to build advanced architectures, including the Transformer. He describes how these concepts are used to build modern networks for computer vision and natural language processing (NLP), including Mask R-CNN, GPT, and BERT. And he explains how a natural language translator and a system generating natural language descriptions of images. Throughout, Ekman provides concise, well-annotated code examples using TensorFlow with Keras. Corresponding PyTorch examples are provided online, and the book thereby covers the two dominating Python libraries for DL used in industry and academia. He concludes with an introduction to neural architecture search (NAS), exploring important ethical issues and providing resources for further learning. Explore and master core concepts: perceptrons, gradient-based learning, sigmoid neurons, and back propagation See how DL frameworks make it easier to develop more complicated and useful neural networks Discover how convolutional neural networks (CNNs) revolutionize image classification and analysis Apply recurrent neural networks (RNNs) and long short-term memory (LSTM) to text and other variable-length sequences Master NLP with sequence-to-sequence networks and the Transformer architecture Build applications for natural language translation and image captioning NVIDIA's invention of the GPU sparked the PC gaming market. The company's pioneering work in accelerated computing--a supercharged form of computing at the intersection of computer graphics, high-performance computing, and AI--is reshaping trillion-dollar industries, such as transportation, healthcare, and manufacturing, and fueling the growth of many others. Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

Heterogeneous Computing Architectures

Author : Olivier Terzo
Publisher : CRC Press
ISBN 13 : 0429680031
Total Pages : 315 pages
Book Rating : 4.4/5 (296 download)

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Book Synopsis Heterogeneous Computing Architectures by : Olivier Terzo

Download or read book Heterogeneous Computing Architectures written by Olivier Terzo and published by CRC Press. This book was released on 2019-09-10 with total page 315 pages. Available in PDF, EPUB and Kindle. Book excerpt: Heterogeneous Computing Architectures: Challenges and Vision provides an updated vision of the state-of-the-art of heterogeneous computing systems, covering all the aspects related to their design: from the architecture and programming models to hardware/software integration and orchestration to real-time and security requirements. The transitions from multicore processors, GPU computing, and Cloud computing are not separate trends, but aspects of a single trend-mainstream; computers from desktop to smartphones are being permanently transformed into heterogeneous supercomputer clusters. The reader will get an organic perspective of modern heterogeneous systems and their future evolution.

Big Scientific Data Management

Author : Jianhui Li
Publisher : Springer
ISBN 13 : 3030280616
Total Pages : 332 pages
Book Rating : 4.0/5 (32 download)

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Book Synopsis Big Scientific Data Management by : Jianhui Li

Download or read book Big Scientific Data Management written by Jianhui Li and published by Springer. This book was released on 2019-08-06 with total page 332 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the First International Conference on Big Scientific Data Management, BigSDM 2018, held in Beijing, Greece, in November/December 2018. The 24 full papers presented together with 7 short papers were carefully reviewed and selected from 86 submissions. The topics involved application cases in the big scientific data management, paradigms for enhancing scientific discovery through big data, data management challenges posed by big scientific data, machine learning methods to facilitate scientific discovery, science platforms and storage systems for large scale scientific applications, data cleansing and quality assurance of science data, and data policies.

High Performance Computing for Big Data

Author : Chao Wang
Publisher : CRC Press
ISBN 13 : 1498784003
Total Pages : 287 pages
Book Rating : 4.4/5 (987 download)

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Book Synopsis High Performance Computing for Big Data by : Chao Wang

Download or read book High Performance Computing for Big Data written by Chao Wang and published by CRC Press. This book was released on 2017-10-16 with total page 287 pages. Available in PDF, EPUB and Kindle. Book excerpt: High-Performance Computing for Big Data: Methodologies and Applications explores emerging high-performance architectures for data-intensive applications, novel efficient analytical strategies to boost data processing, and cutting-edge applications in diverse fields, such as machine learning, life science, neural networks, and neuromorphic engineering. The book is organized into two main sections. The first section covers Big Data architectures, including cloud computing systems, and heterogeneous accelerators. It also covers emerging 3D IC design principles for memory architectures and devices. The second section of the book illustrates emerging and practical applications of Big Data across several domains, including bioinformatics, deep learning, and neuromorphic engineering. Features Covers a wide range of Big Data architectures, including distributed systems like Hadoop/Spark Includes accelerator-based approaches for big data applications such as GPU-based acceleration techniques, and hardware acceleration such as FPGA/CGRA/ASICs Presents emerging memory architectures and devices such as NVM, STT- RAM, 3D IC design principles Describes advanced algorithms for different big data application domains Illustrates novel analytics techniques for Big Data applications, scheduling, mapping, and partitioning methodologies Featuring contributions from leading experts, this book presents state-of-the-art research on the methodologies and applications of high-performance computing for big data applications. About the Editor Dr. Chao Wang is an Associate Professor in the School of Computer Science at the University of Science and Technology of China. He is the Associate Editor of ACM Transactions on Design Automations for Electronics Systems (TODAES), Applied Soft Computing, Microprocessors and Microsystems, IET Computers & Digital Techniques, and International Journal of Electronics. Dr. Chao Wang was the recipient of Youth Innovation Promotion Association, CAS, ACM China Rising Star Honorable Mention (2016), and best IP nomination of DATE 2015. He is now on the CCF Technical Committee on Computer Architecture, CCF Task Force on Formal Methods. He is a Senior Member of IEEE, Senior Member of CCF, and a Senior Member of ACM.