Data Parallel C Mastering Dpc For Programming Of Heterogeneous Systems Using C And Sycl
Download Data Parallel C Mastering Dpc For Programming Of Heterogeneous Systems Using C And Sycl PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Data Parallel C Mastering Dpc For Programming Of Heterogeneous Systems Using C And Sycl book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Data Parallel C++
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++.
Euro-Par 2024: Parallel Processing Workshops
Author: Silvina Caino-Lores
language: en
Publisher: Springer Nature
Release Date: 2025-06-10
The two-volume set LNCS 15385 + 15386 constitutes the proceedings of the workshops and associated events that were held in conjunction with the 30th European Conference on Parallel and Distributed Processing, Euro-Par 2024, which took place in Madrid, Spain, during August 26–30, 2024. Overall, the Euro-Par Workshops received a total of 84 submissions of which 60 were accepted for presentation. They stem from the following workshops: – The 1st European Workshop on Quantum Computing for High-Performance Computing (EUROQHPC 2024) – The 19th Workshop on Virtualization in High-Performance Cloud Computing (VHPC 2024) – The 1st Workshop in High-Performance Computing in Physics (PHYSHPC 2024) – The 4th Workshop on Asynchronous Many-Task Systems for Exascale (AMTE 2024) – The 3rd EuroHPC Workshop on Dynamic Resources in HPC (DYNRESHPC 2024) – The 22nd International Workshop on Algorithms, Models and Tools for Parallel Computing on Heterogeneous Platforms (HETEROPAR 2024) – The 1st Workshop on Next Steps in IoT-Edge-Cloud Continuum Evolution: Research and Practice (IECCONT 2024) – The 1st Workshop about High-Performance e-Science (HIPES 2024) – The 2nd International Workshop on Scalable Compute Continuum (WSCC 2024) In addition, the proceedings contain 14 poster and demo papers that have been accepted from 30 submissions, and 18 contributions in the PhD Symposium track that were accepted from 22 submissions.
Programming Heterogeneous Hardware via Managed Runtime Systems
This book provides an introduction to both heterogeneous execution and managed runtime environments (MREs) by discussing the current trends in computing and the evolution of both hardware and software. To this end, it first details how heterogeneous hardware differs from traditional CPUs, what their key components are and what challenges they pose to heterogenous execution. The most ubiquitous ones are General Purpose Graphics Processing Units (GPGPUs) which are pervasive across a plethora of application domains ranging from graphics processing to training of AI and Machine Learning models. Subsequently, current solutions on programming heterogeneous MREs are described, highlighting for each current existing solution the associated advantages and disadvantages. This book is written for scientists and advanced developers who want to understand how choices at the programming API level can affect performance and/or programmability of heterogeneous hardware accelerators, how toimprove the underlying runtime systems in order to seamlessly integrate diverse hardware resources, or how to exploit acceleration techniques from their preferred programming languages.