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Compiling Parallel Loops for High Performance Computers: Partitioning, Data Assignment and Remapping (The Springer International Series in Engineering and Computer Science, 200)
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Compiling Parallel Loops for High Performance Computers: Partitioning, Data Assignment and Remapping (The Springer International Series in Engineering and Computer Science, 200)
Unlock the potential of high-performance computing with our essential guide to compiling parallel loops.
Compiling Parallel Loops for High Performance Computers: Partitioning, Data Assignment and Remapping (The Springer International Series in Engineering and Computer Science, 200)
Item #: 125872832

Compiling Parallel Loops for High Performance Computers: Partitioning, Data Assignment and Remapping (The Springer International Series in

Item #: 125872832

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  • 4. 2 Code Segments . . . . . . . . . . . . . . . 96 4. 3 Determining Communication Parameters . 99 4. 4 Multicast Communication Overhead · 103 4. 5 Partitioning . . . . . . · 103 4. 6 Experimental Results . 117 4. 7 Conclusion. . . . . . . · 121 5 COLLECTIVE PARTITIONING AND REMAPPING FOR MULTIPLE LOOP NESTS 125 5. 1 Introduction. . . . . . . . . 125 5. 2 Program Enclosure Trees. . 128 5. 3 The CPR Algorithm . . 132 5. 4 Experimental Results. . 141 5. 5 Conclusion. . 146 BIBLIOGRAPHY. 149 INDEX . . . . . . . . 157 LIST OF FIGURES Figure 1. 1 The Butterfly Architecture. . . . . . . . . . 5 1. 2 Example of an iterative data-parallel loop . . 7 1. 3 Contiguous tiling and assignment of an iteration space. 13 2. 1 Communication along a line segment. . . 24 2. 2 Access pattern for the access offset, (3,2). 25 2. 3 Decomposing an access vector along an orthogonal basis set of vectors. . . . . . . . . . . . . . . . . . . 26 2. 4 An analysis of communication patterns. 29 2. 5 Decomposing a vector along two separate basis sets of vectors. 31 2. 6 Cache lines aligning with borders. 33 2. 7 Cache lines not aligned with borders. 34 2. 8 nh is the difference of nd and nb. 42 2. 9 nh is the sum of nd and nb. 42 2. 10 The ADAPT system. 44 2. 11 Code segment used in experiments. . 46 2. 12 Execution rates for various partitions. 47 2. 13 Execution time of partitions on Multimax. 48 2. 14 Performance increase as processing power increases. 49 2. 15 Percentage miss ratios for various aspect ratios and line sizes.
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Compiling Parallel Loops for High Performance Computers: Partitioning, Data Assignment and Remapping (The Springer International Series in Engineering and Computer Science, 200)

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  • Optimize performance with expert insights on parallel loop compilation.
  • Explore advanced techniques in data partitioning and assignment.
  • Enhance communication efficiency with detailed multicast strategies.
  • Learn cutting-edge algorithms for collective partitioning and remapping.
  • Access comprehensive experimental results to inform your implementations.
  • Benefit from a structured approach to high-performance computing topics.

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