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Optimizing parallel reduction in CUDA 规约优化文档下载
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2019-09-28 09:00:22
Optimizing parallel reduction in CUDA 规约优化文档
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Optimizing parallel reduction in CUDA 规约优化文档下载
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Optimizing
parallel
reduction
in
CUDA
规约
优化
文档
Optimizing
parallel
reduction
in
CUDA
规约
优化
文档
Optimizing
Autodock with
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美国斯坦福大学研究生教程-
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高性能计算 Introduction to Massively
Parallel
Computing GPU History and
CUDA
Programming Basics
CUDA
Treads and Atomics
CUDA
Memories Performance Considerations
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Patterns I
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Patterns II Introduction to Thrust Sparse Matrix-Vector Multiplication on Throughput-Oriented Processors PDE Solvers The Fermi Architecture Ray Tracing Case Study Future of Throughput Path Planning Case Study
Optimizing
GPU Performance Final lecture TBD
GPU Programming in MATLAB
GPU Programming in MATLAB by Nikolaos Ploskas English | 28 July 2016 | ISBN: 0128051329 | 318 Pages | PDF | 27.82 MB GPU programming in MATLAB is intended for scientists, engineers, or students who develop or maintain applications in MATLAB and would like to accelerate their codes using GPU programming without losing the many benefits of MATLAB. The book starts with coverage of the
Parallel
Computing Toolbox and other MATLAB toolboxes for GPU computing, which allow applications to be ported straightforwardly onto GPUs without extensive knowledge of GPU programming. The next part covers built-in, GPU-enabled features of MATLAB, including options to leverage GPUs across multicore or different computer systems. Finally, advanced material includes
CUDA
code in MATLAB and
optimizing
existing GPU applications. Throughout the book, examples and source codes illustrate every concept so that readers can immediately apply them to their own development. Provides in-depth, comprehensive coverage of GPUs with MATLAB, including the
parallel
computing toolbox and built-in features for other MATLAB toolboxes Explains how to accelerate computationally heavy applications in MATLAB without the need to re-write them in another language Presents case studies illustrating key concepts across multiple fields Includes source code, sample datasets, and lecture slides
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