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High performance parallelism pearls : multicore and many-core programming approaches. Volume two /
High Performance Parallelism Pearls Volume 2 offers another set of examples that demonstrate how to leverage parallelism. Similar to Volume 1, the techniques included here explain how to use processors and coprocessors with the same programming - illustrating the most effective ways to combine Xeon...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Waltham, MA :
Morgan Kaufmann an imprint of Elsevier,
[2015]
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Front Cover; High Performance Parallelism Pearls: Multicore and Many-core Programming Approaches; Copyright; Contents; Contributors; Acknowledgments; Foreword; Making a bet on many-core; 2013 Stampede-Intel Many-Core System
- A First; HPC journey and revelation; Stampede users discover: Its parallel programming; This book is timely and important; Preface; Inspired by 61 cores: A new era in programming; Chapter 1: Introduction; Applications and techniques; SIMD and vectorization; OpenMP and nested parallelism; Latency optimizations; Python; Streams; Ray tracing; Tuning prefetching.
- MPI shared memoryUsing every last core; OpenCL vs. OpenMP; Power analysis for nodes and clusters; The future of many-core; Downloads; For more information; Chapter 2: Numerical Weather Prediction Optimization; Numerical weather prediction: Background and motivation; WSM6 in the NIM; Shared-memory parallelism and controlling horizontal vector length; Array alignment; Loop restructuring; Compile-time constants for loop and array bounds; Performance improvements; Summary; For more information; Chapter 3: WRF Goddard Microphysics Scheme Optimization; The motivation and background.
- WRF Goddard microphysics schemeGoddard microphysics scheme; Benchmark setup; Code optimization; Removal of the vertical dimension from temporary variables for a reduced memory footprint; Collapse i- and j-loops into smaller cells for smaller footprint per thread; Addition of vector alignment directives; Summary of the code optimizations; Analysis using an instruction Mix report; VTune performance metrics; Performance effects of the optimization of Goddard microphysics scheme on the WRF; Summary; Acknowledgments; For more information; Chapter 4: Pairwise DNA Sequence Alignment Optimization.
- Pairwise sequence alignmentParallelization on a single coprocessor; Multi-threading using OpenMP; Vectorization using SIMD intrinsics; Parallelization across multiple coprocessors using MPI; Performance results; Summary; For more information; Chapter 5: Accelerated Structural Bioinformatics for Drug Discovery; Parallelism enables proteome-scale structural bioinformatics; Overview of eFindSite; Benchmarking dataset; Code profiling; Porting eFindSite for coprocessor offload; Parallel version for a multicore processor; Task-level scheduling for processor and coprocessor; Case study; Summary.
- For more informationChapter 6: Amber PME Molecular Dynamics Optimization; Theory of MD; Acceleration of neighbor list building using the coprocessor; Acceleration of direct space sum using the coprocessor; Additional optimizations in coprocessor code; Removing locks whenever possible; Exclusion list optimization; Reduce data transfer and computation in offload code; Modification of load balance algorithm; PME direct space sum and neighbor list work; PME reciprocal space sum work; Bonded force work; Compiler optimization flags; Results; Conclusions; For more information.