Performance Analysis and Tuning for General Purpose Graphics Processing Units (GPGPU)

Performance Analysis and Tuning for General Purpose Graphics Processing Units (GPGPU)
Author :
Publisher : Morgan & Claypool Publishers
Total Pages : 99
Release :
ISBN-10 : 9781608459544
ISBN-13 : 1608459543
Rating : 4/5 (543 Downloads)

Book Synopsis Performance Analysis and Tuning for General Purpose Graphics Processing Units (GPGPU) by : Hyesoon Kim

Download or read book Performance Analysis and Tuning for General Purpose Graphics Processing Units (GPGPU) written by Hyesoon Kim and published by Morgan & Claypool Publishers. This book was released on 2012 with total page 99 pages. Available in PDF, EPUB and Kindle. Book excerpt: General-purpose graphics processing units (GPGPU) have emerged as an important class of shared memory parallel processing architectures, with widespread deployment in every computer class from high-end supercomputers to embedded mobile platforms. Relative to more traditional multicore systems of today, GPGPUs have distinctly higher degrees of hardware multithreading (hundreds of hardware thread contexts vs. tens), a return to wide vector units (several tens vs. 1-10), memory architectures that deliver higher peak memory bandwidth (hundreds of gigabytes per second vs. tens), and smaller caches/scratchpad memories (less than 1 megabyte vs. 1-10 megabytes). In this book, we provide a high-level overview of current GPGPU architectures and programming models. We review the principles that are used in previous shared memory parallel platforms, focusing on recent results in both the theory and practice of parallel algorithms, and suggest a connection to GPGPU platforms. We aim to provide hints to architects about understanding algorithm aspect to GPGPU. We also provide detailed performance analysis and guide optimizations from high-level algorithms to low-level instruction level optimizations. As a case study, we use n-body particle simulations known as the fast multipole method (FMM) as an example. We also briefly survey the state-of-the-art in GPU performance analysis tools and techniques.


Performance Analysis and Tuning for General Purpose Graphics Processing Units (GPGPU) Related Books

Performance Analysis and Tuning for General Purpose Graphics Processing Units (GPGPU)
Language: en
Pages: 99
Authors: Hyesoon Kim
Categories: Computers
Type: BOOK - Published: 2012 - Publisher: Morgan & Claypool Publishers

DOWNLOAD EBOOK

General-purpose graphics processing units (GPGPU) have emerged as an important class of shared memory parallel processing architectures, with widespread deploym
Performance Analysis and Tuning for General Purpose Graphics Processing Units (GPGPU)
Language: en
Pages: 88
Authors: Hyesoon Kim
Categories: Technology & Engineering
Type: BOOK - Published: 2022-05-31 - Publisher: Springer Nature

DOWNLOAD EBOOK

General-purpose graphics processing units (GPGPU) have emerged as an important class of shared memory parallel processing architectures, with widespread deploym
General-Purpose Graphics Processor Architectures
Language: en
Pages: 122
Authors: Tor M. Aamodt
Categories: Technology & Engineering
Type: BOOK - Published: 2022-05-31 - Publisher: Springer Nature

DOWNLOAD EBOOK

Originally developed to support video games, graphics processor units (GPUs) are now increasingly used for general-purpose (non-graphics) applications ranging f
Computational Science – ICCS 2020
Language: en
Pages: 726
Authors: Valeria V. Krzhizhanovskaya
Categories: Computers
Type: BOOK - Published: 2020-06-18 - Publisher: Springer Nature

DOWNLOAD EBOOK

The seven-volume set LNCS 12137, 12138, 12139, 12140, 12141, 12142, and 12143 constitutes the proceedings of the 20th International Conference on Computational
Efficient Processing of Deep Neural Networks
Language: en
Pages: 254
Authors: Vivienne Sze
Categories: Technology & Engineering
Type: BOOK - Published: 2022-05-31 - Publisher: Springer Nature

DOWNLOAD EBOOK

This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks (DNNs). DNNs are curren