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Parallel and High Performance Computing
Robert Robey, Yuliana ZamoraAvez-vous aimé ce livre?
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Parallel and High Performance Computing offers techniques guaranteed to boost your code’s effectiveness.
Summary
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. Parallel and High Performance Computing 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.
About the technology
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.
About the book
Parallel and High Performance Computing 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.
What's inside
• Planning a new parallel project
• Understanding differences in CPU and GPU architecture
• Addressing underperforming kernels and loops
• Managing applications with batch scheduling
About the reader
For experienced programmers proficient with a high-performance computing language like C, C++, or Fortran.
About the author
Robert Robey works at Los Alamos National Laboratory and has been active in the field of parallel computing for over 30 years.
Yuliana Zamora is currently a PhD student and Siebel Scholar at the University of Chicago, and has lectured on programming modern hardware at numerous national conferences.
Summary
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. Parallel and High Performance Computing 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.
About the technology
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.
About the book
Parallel and High Performance Computing 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.
What's inside
• Planning a new parallel project
• Understanding differences in CPU and GPU architecture
• Addressing underperforming kernels and loops
• Managing applications with batch scheduling
About the reader
For experienced programmers proficient with a high-performance computing language like C, C++, or Fortran.
About the author
Robert Robey works at Los Alamos National Laboratory and has been active in the field of parallel computing for over 30 years.
Yuliana Zamora is currently a PhD student and Siebel Scholar at the University of Chicago, and has lectured on programming modern hardware at numerous national conferences.
Catégories:
Année:
2021
Edition:
1
Editeur::
Manning Publications
Langue:
english
Pages:
704
ISBN 10:
1617296465
ISBN 13:
9781617296468
Fichier:
PDF, 35.83 MB
Vos balises:
IPFS:
CID , CID Blake2b
english, 2021
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