Parallel and High Performance Computing - by Robert Robey & Yuliana Zamora (Paperback)
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<p/><br></br><p><b> About the Book </b></p></br></br>"For experienced programmers proficient with a high-performance computing language like C, C++, or Fortran"--Page 4 of cover.<p/><br></br><p><b> Book Synopsis </b></p></br></br><b><i>Parallel and High Performance Computing</i> offers techniques guaranteed to boost your code's effectiveness.</b> <p/><b>Summary</b><br> Complex calculations, like training deep learning models or running large-scale simulations, can take an extremely long time. Efficient parallel programming can save hours--or even days--of computing time. <i>Parallel and High Performance Computing</i> shows you how to deliver faster run-times, greater scalability, and increased energy efficiency to your programs by mastering parallel techniques for multicore processor and GPU hardware. <p/> <b>About the technology</b><br> Write fast, powerful, energy efficient programs that scale to tackle huge volumes of data. Using parallel programming, your code spreads data processing tasks across multiple CPUs for radically better performance. With a little help, you can create software that maximizes both speed and efficiency. <p/> <b>About the book</b><br> <i>Parallel and High Performance Computing</i> offers techniques guaranteed to boost your code's effectiveness. You'll learn to evaluate hardware architectures and work with industry standard tools such as OpenMP and MPI. You'll master the data structures and algorithms best suited for high performance computing and learn techniques that save energy on handheld devices. You'll even run a massive tsunami simulation across a bank of GPUs. <p/> <b>What's inside</b> <p/> Planning a new parallel project<br> Understanding differences in CPU and GPU architecture<br> Addressing underperforming kernels and loops<br> Managing applications with batch scheduling <p/><b>About the reader</b><br> For experienced programmers proficient with a high-performance computing language like C, C++, or Fortran. <p/> <b>About the author</b><br> <b>Robert Robey</b> works at Los Alamos National Laboratory and has been active in the field of parallel computing for over 30 years. <b>Yuliana Zamora</b> is currently a PhD student and Siebel Scholar at the University of Chicago, and has lectured on programming modern hardware at numerous national conferences. <p/> <b>Table of Contents</b><br> PART 1 INTRODUCTION TO PARALLEL COMPUTING<br> 1 Why parallel computing?<br> 2 Planning for parallelization<br> 3 Performance limits and profiling<br> 4 Data design and performance models<br> 5 Parallel algorithms and patterns<br> PART 2 CPU: THE PARALLEL WORKHORSE<br> 6 Vectorization: FLOPs for free<br> 7 OpenMP that performs<br> 8 MPI: The parallel backbone<br> PART 3 GPUS: BUILT TO ACCELERATE<br> 9 GPU architectures and concepts<br> 10 GPU programming model<br> 11 Directive-based GPU programming<br> 12 GPU languages: Getting down to basics<br> 13 GPU profiling and tools<br> PART 4 HIGH PERFORMANCE COMPUTING ECOSYSTEMS<br> 14 Affinity: Truce with the kernel<br> 15 Batch schedulers: Bringing order to chaos<br> 16 File operations for a parallel world<br> 17 Tools and resources for better code<p/><br></br><p><b> About the Author </b></p></br></br><b>Robert Robey</b> works at Los Alamos National Laboratory and has been active in the field of parallel computing for over 30 years. <p/><b>Yuliana Zamora</b> has lectured on efficient programming of modern hardware at national conferences, based on her work developing applications running on tens of thousands of processing cores and the latest GPU architectures.
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