Every area of science and engineering today has to process voluminous data sets. Using exact, or even approximate, algorithms to solve intractable problems in critical areas, such as computational biology, takes time that is exponential in some of the underlying parameters. Parallel computing addres
Multicore computing : algorithms, architectures, and applications
โ Scribed by Sanguthevar Rajasekaran
- Publisher
- Chapman and Hall/CRC
- Year
- 2014
- Tongue
- English
- Leaves
- 451
- Series
- Chapman & Hall/CRC computer and information science series, 29
- Category
- Library
No coin nor oath required. For personal study only.
โฆ Table of Contents
Content: Memory Hierarchy for Multicore and Manycore Processors Mohamed Zahran and Bushra Ahsan Design Issues Physical Memory Cache Hierarchy Organization Cache Hierarchy Sharing Cache Hierarchy Optimization Cache Coherence Support for Memory Consistency Models Cache Hierarchy in Light of New Technologies Concluding Remarks FSB: A Flexible Set Balancing Strategy for Last Level Caches Mohammad Hammoud, Sangyeun Cho, and Rami Melhem Introduction Motivation and Background Flexible Set Balancing (FSB) Quantitative Evaluation Related Work Conclusions and Future Work The SPARC Processor Architecture Simone Secchi, Antonino Tumeo, and Oreste Villa Introduction The SPARC Instruction Set Architecture Memory Access Synchronization The NIAGARA Processor Architecture Core Micro-Architecture Core Interconnection Memory Subsystem Niagara Evolutions The Cilk and Cilk++ Programming Languages Hans Vandierendonck Abstract Introduction The Cilk Language Implementation Analyzing Parallelism in Cilk Programs Hyperobjects Conclusion Multithreading in the PLASMA Library Jakub Kurzak, Piotr Luszczek, Asim YarKhan, Mathieu Faverge, Julien Langou, Henricus Bouwmeester, and Jack Dongarra Introduction Multithreading in PLASMA Dynamic Scheduling with QUARK Parallel Composition Task Aggregation Nested Parallelism Efficient Aho-Corasick String Matching on Emerging Multicore Architectures Antonino Tumeo, Oreste Villa, Simone Secchi, and Daniel Chavarria-Miranda Introduction Related Work Preliminaries Algorithm Design Experimental Results Conclusions Sorting on a Graphics Processing Unit (GPU) Shibdas Bandyopadhyay and Sartaj Sahni Graphics Processing Units Sorting Numbers on GPUs Sorting Records on GPUs Scheduling DAG Structured Computations Yinglong Xia and Viktor K. Prasanna Introduction Background Related Work Lock-Free Collaborative Scheduling Hierarchical Scheduling with Dynamic Thread Grouping Conclusion Evaluating Multicore Processors and Accelerators for Dense Numerical Computations Seunghwa Kang, Nitin Arora, Aashay Shringarpure, Richard W. Vuduc, and David A. Bader Introduction Interarchitectural Design Trade-Offs Descriptions and Qualitative Analysis of Computational Statistics Kernels Baseline Architecture-Specific Implementations for the Computational Statistics Kernels Experimental Results for the Computational Statistics Kernels Descriptions and Qualitative Analysis of Direct N-Body Kernels Direct N-Body Implementations Experimental Results and Discussion for the Direct N-Body Implementations Conclusions Sorting on the Cell Broadband Engine Shibdas Bandyopadhyay, Dolly Sharma, Reda A. Ammar, Sanguthevar Rajasekaran, and Sartaj Sahni The Cell Broadband Engine High-level Strategies for Sorting SPU Vector and Memory Operations Sorting Numbers Sorting Records GPU Matrix Multiplication Junjie Li, Sanjay Ranka, and Sartaj Sahni Introduction GPU Architecture Programming Model Occupancy Single Core Matrix Multiply Multicore Matrix Multiply GPU Matrix Multiply A Comparison Backprojection Algorithms for Multicore and GPU Architectures William Chapman, Sanjay Ranka, Sartaj Sahni, Mark Schmalz, Linda Moore, Uttam Majumder, and Bracy Elton Summary of Backprojection Partitioning Backprojection for Implementation on a GPU Single Core Backprojection GPU Backprojection Conclusion Acknowledgments Index
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