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检索条件"任意字段=14th Annual Workshop on General Purpose Processing using Graphics Processing Unit, GPGPU 2022"

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gpgpu 2022 - 14th workshop on general purpose processing using GPU

GPGPU 2022 - 14th Workshop on General Purpose Processing Usi...

引用

14th annual workshop on general purpose processing using graphics processing unit, gpgpu 2022

ISBN: (纸本)9781450393485

the proceedings contain 6 papers. the topics discussed include: near LLC versus near main memory processing;accelerating data transfer between host and device using idle GPU;systematically extending a high-level code generator with support for tensor cores;compiler-assisted scheduling for multi-instance GPUs;ScaleServe: a scalable multi-GPU machine learning inference system and benchmarking suite;and understanding wafer-scale GPU performance using an architectural simulator.

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Accelerating Data Transfer between Host and Device using Idle GPU 22

Accelerating Data Transfer between Host and Device using Idl...

引用

14th annual workshop on general purpose processing using graphics processing unit (gpgpu)

作者： Tatsugi, Yuya Nukada, Akira Univ Tsukuba Tsukuba Ibaraki Japan

ISBN: (纸本)9781450393485

When running single-GPU applications on multi-GPU compute nodes, the remaining GPU devices are kept idle. We propose a novel technology to accelerate these single-GPU applications using the idle GPU devices. the data transfers between host and device are performed not only by the first GPU but also by the second GPU as well as the alternative route with PCI-Express and NV-Link connected to it. Our performance evaluations show the proposed method enables about twice data transfer speed as native single GPU case for large data sizes.

关键词： NVIDIA CUDA Multi-GPU data transfer

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Near LLC Versus Near Main Memory processing 22

Near LLC Versus Near Main Memory Processing

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14th annual workshop on general purpose processing using graphics processing unit (gpgpu)

作者： Bitalebi, Hossein Geraeinejad, Vahid Ebrahimi, Masoumeh KTH Royal Inst Technol Stockholm Sweden

ISBN: (纸本)9781450393485

Emerging advanced applications, such as deep learning and graph processing, with enormous processing demand and massive memory requests call for a comprehensive processing system or advanced solutions to address these requirements. Near data processing is one of the promising structures targeting this goal. However, most recent studies have focused on processing instructions near the main memory data banks while ignoring the benefits of processing instructions near other memory hierarchy levels such as LLC. In this study, we investigate the near LLC processing structures, and compare it to the near main memory processing alternative, specifically in graphics processing units. We analyze these two structures on various applications in terms of performance and power. Results show a clear benefit of near LLC processing over near main memory processing in a class of applications. Further, we suggest an architecture, which could benefit from both near main memory and near LLC processing structures, but requiring the applications to be characterized in advance or at run time.

关键词： Near data processing processing near main memory processing near LLC GPU Irregular memory accesses

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Understanding Wafer-Scale GPU Performance using an Architectural Simulator 22

Understanding Wafer-Scale GPU Performance using an Architect...

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14th annual workshop on general purpose processing using graphics processing unit (gpgpu)

作者： thames, Chris Yan, Hang Sun, Yifan William & Mary Williamsburg VA 23185 USA Huazhong Univ Sci & Technol Wuhan Hubei Peoples R China

ISBN: (纸本)9781450393485

Wafer-Scale chips have the potential to break the die-size limitation and provide extreme performance scalability. Existing solutions have demonstrated the possibility of integrating multi-CPU and multi-GPU systems at a significantly larger scale on a wafer. this increased capability results in an increase in complexity in managing the memory and computing resources. To support the community studying wafer-scale systems, this paper develops an architectural simulator dedicated to modeling wafer-scale multi-device systems. Also, this work demonstrates an analysis of initial results from simulations on wafer-scale GPU systems, providing useful insight that can guide future system design.

关键词： Wafer-Scale GPU Simulation

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Multi-stage programming for GPUs in C++ using PACXX 16

Multi-stage programming for GPUs in C++ using PACXX

引用

9th annual workshop on general purpose processing using graphics processing unit, gpgpu 2016

作者： Haidl, Michael Steuwer, Michel Humernbrum, Tim Gorlatch, Sergei University of Muenster Germany University of Edinburgh United Kingdom

ISBN: (纸本)9781450341950

Writing and optimizing programs for high performance on systems with graphics processing units (GPUs) remains a challenging task even for expert programmers. A promising optimization technique is multi-stage programming - evaluating parts of the program upfront on the CPU and embedding the computed values in the GPU code, thus allowing for more aggressive compiler optimizations. Unfortunately, such optimizations are not possible in CUDA, whereas to apply them in OpenCL, programmers are forced to manipulate the GPU source code as plain strings, which is error-prone and type-unsafe. In this paper, we describe PACXX - our approach to GPU programming in C++, with the convenient features of modern C++14 standard: type deduction, lambda expressions, and algorithms from the standard template library (STL). using PACXX, a GPU program is written as a single C++ program, rather than two distinct host and kernel programs. We extend PACXX with an easy-to-use and type-safe API for multi-stage programming avoiding the pitfalls of string manipulation. using just-in-time compilation techniques, PACXX generates efficient GPU code at runtime. Our evaluation shows that using PACXX allows for writing multi-stage code easier and safer than currently possible in CUDA or OpenCL. With two application studies we demonstrate that multi-stage programs can significantly outperform equivalent non-staged versions. Furthermore, we show that PACXX generates code with high performance, comparable to industrial-strength OpenCL compilers. © 2016 ACM.

关键词： graphics processing unit

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