GPU parallel program development using CUDA
"GPU Parallel Program Development using CUDA teaches GPU programming by showing the differences among different families of GPUs. This approach prepares the reader for the next generation and future generations of GPUs. The book emphasizes concepts that will remain relevant for a long time, rather than concepts that are platform-specific. At the same time, the book also provides platform-dependent explanations that are as valuable as generalized GPU concepts. The book consists of three separate…
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- Full title :GPU parallel program development using CUDA / Tolga Soyata.
Summary
"GPU Parallel Program Development using CUDA teaches GPU programming by showing the differences among different families of GPUs. This approach prepares the reader for the next generation and future generations of GPUs. The book emphasizes concepts that will remain relevant for a long time, rather than concepts that are platform-specific. At the same time, the book also provides platform-dependent explanations that are as valuable as generalized GPU concepts. The book consists of three separate parts; it starts by explaining parallelism using CPU multi-threading in Part I. A few simple programs are used to demonstrate the concept of dividing a large task into multiple parallel sub-tasks and mapping them to CPU threads. Multiple ways of parallelizing the same task are analyzed and their pros/cons are studied in terms of both core and memory operation. Part II of the book introduces GPU massive parallelism. The same programs are parallelized on multiple Nvidia GPU platforms and the same performance analysis is repeated. Because the core and memory structures of CPUs and GPUs are different, the results differ in interesting ways. The end goal is to make programmers aware of all the good ideas, as well as the bad ideas, so readers can apply the good ideas and avoid the bad ideas in their own programs. Part III of the book provides pointer for readers who want to expand their horizons. It provides a brief introduction to popular CUDA libraries (such as cuBLAS, cuFFT, NPP, and Thrust),the OpenCL programming language, an overview of GPU programming using other programming languages and API libraries (such as Python, OpenCV, OpenGL, and Apple's Swift and Metal,) and the deep learning library cuDNN" --
Read More ↓Edition Statements
- Publication Date:2018
Publication Statements
- Publisher(s):CRC Press, Taylor & Francis Group
- Place of Publication:Boca Raton, FL
- Language:eng
- Publication Date:2018
The Physical Object
- Description:xxxv, 440 pages ; : 27 cm.
Edition Identifiers
- ISBN:9781498750752, 1498750753, 9780367572242, 0367572249
- oclc:on1019854627
- LC Classification:QA76.642
- Dewey Decimal:005.2/75
Table of Contents
Machine generated contents note: ch. 1 Introduction to CPU Parallel Programming -- 1.1.Evolution Of Parallel Programming -- 1.2.More Cores, More Parallelism -- 1.3.Cores Versus Threads -- 1.3.1.More Threads or More Cores to Parallelize? -- 1.3.2.Influence of Core Resource Sharing -- 1.3.3.Influence of Memory Resource Sharing -- 1.4.Our First Serial Program -- 1.4.1.Understanding Data Transfer Speeds -- 1.4.2.The main() Function in imflip.c -- 1.4.3.Flipping Rows Vertically: FliplmageV() -- 1.4.4.Flipping Columns Horizontally: FliplmageH() -- 1.5.Writing, Compiling, Running Our Programs -- 1.5.1.Choosing an Editor and a Compiler -- 1.5.2.Developing in Windows 7, 8, and Windows 10 Platforms -- 1.5.3.Developing in a Mac Platform -- 1.5.4.Developing in a Unix Platform -- 1.6.Crash Course On UNIX -- 1.6.1.Unix Directory-Related Commands -- 1.6.2.Unix File-Related Commands -- 1.7.Debugging Your Programs -- 1.7.1.gdb -- 1.7.2.Old School Debugging -- 1.7.3.valgrind
Note continued: 1.8.Performance Of Our First Serial Program -- 1.8.1.Can We Estimate the Execution Time? -- 1.8.2.What Does the OS Do When Our Code Is Executing? -- 1.8.3.How Do We Parallelize It? -- 1.8.4.Thinking About the R…
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