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Memory ReStructuring

Memory ReStructuring. From a memory/cache perspective, application behavior depends on: Memory structure Input data Data structures Compiler can determine some of this behavior. Dynamic monitoring and re-writing of memory (data structures and data layout) can buy us much more.

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Memory ReStructuring

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  1. Memory ReStructuring • From a memory/cache perspective, application behavior depends on: • Memory structure • Input data • Data structures • Compiler can determine some of this behavior. • Dynamic monitoring and re-writing of memory (data structures and data layout) can buy us much more.

  2. Object Level Reorganization Data/node layout A B C Unit of Banking D E F G H I

  3. Object Level Reorganization Data/node layout A A Hot Path B C Unit of Banking Unit of Banking D E F G H I

  4. Field Level Reorganization Transformation Unit of banking Assumes that each record can be split into HOT and COLD field

  5. Hotness vs. Execution Time

  6. Hotness vs. Execution Time

  7. Hotness Total Elements: 10,000

  8. Hotness Total Elements: 100,000

  9. Size of Record vs. Execution Time Hotness

  10. Hotness Total Elements: 50,000

  11. Related Work • Improving Cache Performance in Dynamic Applications through Data and Computation Reorganization at Run Time • Chen Ding and Ken Kennedy – Rice University • PLDI ’99 and Ding’s Thesis • Reorders memory access on arrays at runtime via a library call • Creates mapping from original position to new position • Use compiler optimization to help remove mapping overhead • Heuristic-guided memory packing • Eliminates 97-99% of cache misses with locality grouping • Reduces 21%-84% L2 misses with dynamic data packing

  12. Related Work • Memory Hierarchy Management for Iterative Graph Structures • Ibraheem Al-Furaih – Syracuse University and Sanjay Ranka – University of Florida • 12th Intl. Parallel Processing Symposium (1998) • Partition graph into sets that can fit into cache • And/or layout cache in a breadth-first manner • Applied as a preprocessing step • Speedups range from 1.2 to 1.5 times

  13. Related Work • Data Remapping for Design Space Optimization of Embedded Memory Systems • Rodric M. Rabbah and Krishna V. Palem – Georgia Institute of Technology • ACM Trans. On Embedded Computing Systems (May 2003) • Generic, pointer-centric data reorganization done at compile time • Use profile data of access patterns along program’s hot path • Splits global arrays of structs into parallel arrays • Intercept dynamic data allocation requests and pool results together • 20% performance improvement on average

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