Trace-Based Runtime Prediction of Reoccurring Data-Parallel Processing Jobs PDF Download
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Author: Alireza Alamgiralem
Publisher: GRIN Verlag
ISBN: 3346602001
Category : Computers
Languages : en
Pages : 103
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Book Description
Master's Thesis from the year 2021 in the subject Engineering - Computer Engineering, grade: 1.7, Technical University of Berlin, language: English, abstract: The present research proposes a novel approach to estimate incoming jobs runtime based on similarities of reocurring jobs. To achieve this goal, we utilize the latest achievements in neural network techniques to embed the job dependencies. Subsequently, we perform multiple clustering techniques to form meaningful groups of reoccurring jobs. Finally, based on the similarities within the groups of samples, we predict runtimes. A recently published trace dataset allows us to develop and evaluate our contribution with more than 200,000 complex and real-world jobs. The cloud data centers should daily handle numerous jobs with complex parallelization. In order to schedule such a heavy and complicated workload and reach efficient resource utilization, runtime prediction is critical. Moreover, accurate runtime prediction may assist cloud users in choosing their required resources more intelligently. Despite the importance of runtime prediction, achieving an accurate prediction is not straightforward because the execution time of jobs in complicated environments of clouds is affected by many factors, e.g., cluster status, users’ requirements, etc.
Author: Alireza Alamgiralem
Publisher: GRIN Verlag
ISBN: 3346602001
Category : Computers
Languages : en
Pages : 103
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Book Description
Master's Thesis from the year 2021 in the subject Engineering - Computer Engineering, grade: 1.7, Technical University of Berlin, language: English, abstract: The present research proposes a novel approach to estimate incoming jobs runtime based on similarities of reocurring jobs. To achieve this goal, we utilize the latest achievements in neural network techniques to embed the job dependencies. Subsequently, we perform multiple clustering techniques to form meaningful groups of reoccurring jobs. Finally, based on the similarities within the groups of samples, we predict runtimes. A recently published trace dataset allows us to develop and evaluate our contribution with more than 200,000 complex and real-world jobs. The cloud data centers should daily handle numerous jobs with complex parallelization. In order to schedule such a heavy and complicated workload and reach efficient resource utilization, runtime prediction is critical. Moreover, accurate runtime prediction may assist cloud users in choosing their required resources more intelligently. Despite the importance of runtime prediction, achieving an accurate prediction is not straightforward because the execution time of jobs in complicated environments of clouds is affected by many factors, e.g., cluster status, users’ requirements, etc.
Author: Dror Feitelson
Publisher: Springer Science & Business Media
ISBN: 3540253300
Category : Business & Economics
Languages : en
Pages : 323
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Book Description
This book constitutes the thoroughly refereed postproceedings of the 10th International Workshop on Job Scheduling Strategies for Parallel Processing, JSSPP 2004, held in New York, NY in June 2004. The 15 revised full research papers presented together with a report on scheduling on the Top 50 machines went through two rounds of reviewing and improvement. Various current issues in job scheduling and load balancing are addressed in the context of computing clusters, parallel and distributed systems, multi-processor systems, and supercomputers.
Author: James Reinders
Publisher: Apress
ISBN: 9781484255735
Category : Computers
Languages : en
Pages : 548
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Book Description
Learn how to accelerate C++ programs using data parallelism. This open access book enables C++ programmers to be at the forefront of this exciting and important new development that is helping to push computing to new levels. It is full of practical advice, detailed explanations, and code examples to illustrate key topics. Data parallelism in C++ enables access to parallel resources in a modern heterogeneous system, freeing you from being locked into any particular computing device. Now a single C++ application can use any combination of devices—including GPUs, CPUs, FPGAs and AI ASICs—that are suitable to the problems at hand. This book begins by introducing data parallelism and foundational topics for effective use of the SYCL standard from the Khronos Group and Data Parallel C++ (DPC++), the open source compiler used in this book. Later chapters cover advanced topics including error handling, hardware-specific programming, communication and synchronization, and memory model considerations. Data Parallel C++ provides you with everything needed to use SYCL for programming heterogeneous systems. What You'll Learn Accelerate C++ programs using data-parallel programming Target multiple device types (e.g. CPU, GPU, FPGA) Use SYCL and SYCL compilers Connect with computing’s heterogeneous future via Intel’s oneAPI initiative Who This Book Is For Those new data-parallel programming and computer programmers interested in data-parallel programming using C++.
Author: Ananth Grama
Publisher: Pearson Education
ISBN: 9780201648652
Category : Computers
Languages : en
Pages : 664
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Book Description
A complete source of information on almost all aspects of parallel computing from introduction, to architectures, to programming paradigms, to algorithms, to programming standards. It covers traditional Computer Science algorithms, scientific computing algorithms and data intensive algorithms.
Author: Pete Warden
Publisher: O'Reilly Media
ISBN: 1492052019
Category : Computers
Languages : en
Pages : 504
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Book Description
Deep learning networks are getting smaller. Much smaller. The Google Assistant team can detect words with a model just 14 kilobytes in size—small enough to run on a microcontroller. With this practical book you’ll enter the field of TinyML, where deep learning and embedded systems combine to make astounding things possible with tiny devices. Pete Warden and Daniel Situnayake explain how you can train models small enough to fit into any environment. Ideal for software and hardware developers who want to build embedded systems using machine learning, this guide walks you through creating a series of TinyML projects, step-by-step. No machine learning or microcontroller experience is necessary. Build a speech recognizer, a camera that detects people, and a magic wand that responds to gestures Work with Arduino and ultra-low-power microcontrollers Learn the essentials of ML and how to train your own models Train models to understand audio, image, and accelerometer data Explore TensorFlow Lite for Microcontrollers, Google’s toolkit for TinyML Debug applications and provide safeguards for privacy and security Optimize latency, energy usage, and model and binary size
Author: Christian Bischof
Publisher: IOS Press
ISBN: 158603796X
Category : Computers
Languages : en
Pages : 824
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Book Description
ParCo2007 marks a quarter of a century of the international conferences on parallel computing that started in Berlin in 1983. The aim of the conference is to give an overview of the developments, applications and future trends in high-performance computing for various platforms.
Author: Jimmy Lin
Publisher: Springer Nature
ISBN: 3031021363
Category : Computers
Languages : en
Pages : 171
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Book Description
Our world is being revolutionized by data-driven methods: access to large amounts of data has generated new insights and opened exciting new opportunities in commerce, science, and computing applications. Processing the enormous quantities of data necessary for these advances requires large clusters, making distributed computing paradigms more crucial than ever. MapReduce is a programming model for expressing distributed computations on massive datasets and an execution framework for large-scale data processing on clusters of commodity servers. The programming model provides an easy-to-understand abstraction for designing scalable algorithms, while the execution framework transparently handles many system-level details, ranging from scheduling to synchronization to fault tolerance. This book focuses on MapReduce algorithm design, with an emphasis on text processing algorithms common in natural language processing, information retrieval, and machine learning. We introduce the notion of MapReduce design patterns, which represent general reusable solutions to commonly occurring problems across a variety of problem domains. This book not only intends to help the reader "think in MapReduce", but also discusses limitations of the programming model as well. Table of Contents: Introduction / MapReduce Basics / MapReduce Algorithm Design / Inverted Indexing for Text Retrieval / Graph Algorithms / EM Algorithms for Text Processing / Closing Remarks
Author: Ivan Ivanov
Publisher: Springer Science & Business Media
ISBN: 1461423260
Category : Business & Economics
Languages : en
Pages : 393
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Book Description
The Cloud Computing and Services Science book comprises a collection of the best papers presented at the International Conference on Cloud Computing and Services Science (CLOSER), which was held in The Netherlands in May 2011. In netting papers from the conference researchers and experts from all over the world explore a wide-ranging variety of the emerging Cloud Computing platforms, models, applications and enabling technologies. Further, in several papers the authors exemplify essential links to Services Science as service development abstraction, service innovation, and service engineering, acknowledging the service-orientation in most current IT-driven structures in the Cloud. The Cloud Computing and Services Science book is organized around important dimensions of technology trends in the domain of cloud computing in relation to a broad scientific understanding of modern services emerging from services science. The papers of this book are inspired by scholarly and practical work on the latest advances related to cloud infrastructure, operations, security, services, and management through the global network. This book includes several features that will be helpful, interesting, and inspirational to students, researchers as well as practitioners. Professionals and decision makers working in this field will also benefit from this book
Author: Arun C. Murthy
Publisher: Pearson Education
ISBN: 0321934504
Category : Computers
Languages : en
Pages : 336
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Book Description
"Apache Hadoop is helping drive the Big Data revolution. Now, its data processing has been completely overhauled: Apache Hadoop YARN provides resource management at data center scale and easier ways to create distributed applications that process petabytes of data. And now in Apache HadoopTM YARN, two Hadoop technical leaders show you how to develop new applications and adapt existing code to fully leverage these revolutionary advances." -- From the Amazon
Author: David R. Kaeli
Publisher: Morgan Kaufmann
ISBN: 0128016493
Category : Computers
Languages : en
Pages : 330
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Book Description
Heterogeneous Computing with OpenCL 2.0 teaches OpenCL and parallel programming for complex systems that may include a variety of device architectures: multi-core CPUs, GPUs, and fully-integrated Accelerated Processing Units (APUs). This fully-revised edition includes the latest enhancements in OpenCL 2.0 including: • Shared virtual memory to increase programming flexibility and reduce data transfers that consume resources • Dynamic parallelism which reduces processor load and avoids bottlenecks • Improved imaging support and integration with OpenGL Designed to work on multiple platforms, OpenCL will help you more effectively program for a heterogeneous future. Written by leaders in the parallel computing and OpenCL communities, this book explores memory spaces, optimization techniques, extensions, debugging and profiling. Multiple case studies and examples illustrate high-performance algorithms, distributing work across heterogeneous systems, embedded domain-specific languages, and will give you hands-on OpenCL experience to address a range of fundamental parallel algorithms. Updated content to cover the latest developments in OpenCL 2.0, including improvements in memory handling, parallelism, and imaging support Explanations of principles and strategies to learn parallel programming with OpenCL, from understanding the abstraction models to thoroughly testing and debugging complete applications Example code covering image analytics, web plugins, particle simulations, video editing, performance optimization, and more
