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Presentation at the 4 th PMEO-PDS Workshop

Presentation at the 4 th PMEO-PDS Workshop. Benchmark Measurements of Current UPC Platforms Zhang Zhang and Steve Seidel Michigan Technological University Denver, Colorado 3/22/2005. Presentation Outline. Background Unified Parallel C, implementations and users.

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Presentation at the 4 th PMEO-PDS Workshop

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  1. Presentation at the 4th PMEO-PDS Workshop Benchmark Measurements of Current UPC Platforms Zhang Zhang and Steve Seidel Michigan Technological University Denver, Colorado 3/22/2005

  2. Presentation Outline • Background • Unified Parallel C, implementations and users. • Previous UPC performance studies. • Experiments • Available UPC platforms • Benchmarks • Performance measurements • Conclusions

  3. UPC Overview • UPC is an extension of C for partitioned shared memory parallel programming. • A special case of shared memory programming model. • Similar languages: Co-Array Fortran, Titanium. • UPC homepage: http://www.upc.gwu.edu • Platforms supported: • Cray X1, Cray T3E, SGI Origin, HP AlphaServer, HP UX, Linux clusters, IBM SP. • UPC compilers: • Open source: MuPC, Berkeley UPC, Intrepid UPC • Commercial: HP UPC, Cray UPC • Users: • LBNL, IDA, AHPCRC, …

  4. Related UPC Performance Studies • Performance benchmark suites • UPC_Bench (GWU) • Synthetic microbenchmark based on the STREAM benchmark. • Application benchmarks: Sobel edge detection, matrix multiplication, N-Queens problem • UPC NAS Parallel Benchmarks (GWU) • Performance monitoring • Performance analysis for HP UPC compiler (GWU) • Performance of Berkeley UPC on HP AlphaServer (Berkeley) • Performance of Intrepid UPC on SGI Origin (GWU)

  5. Benchmarking UPC Systems • Extended shared memory bandwidth microbenchmarks to cover various reference patterns: • Scalar references: 11 access patterns • Block memory operations: 9 access patterns • Benchmarked six combinations of available UPC compilers and platforms using both the UPC STREAM (MTU code) and the UPC NAS Parallel Benchmarks (GWU code). • Compilers: MuPC, HP UPC, Berkeley UPC and Intrepid UPC • Platforms: Myrinet Linux cluster, HP AlphaServer SC, and T3E • The first comparison of performance for currently available UPC implementations. • The first report on MuPC performance.

  6. Benchmarks • Synthetic benchmarks: • The STREAM microbenchmark was rewritten using UPC with more diversities of shared memory access patterns: • Local shared read / write • Unit stride shared read / write / copy • Random shared read / write / copy • Stride-n shared read / write / copy • Block transfers with variations of source and sink affinities. • NAS Parallel Benchmark Suite v2.4 • The UPC version was developed at GWU. • Five cores: CG, EP, FT, IS and MG. • Two variations: Naïve version and Hand-tuned version. • Input size: Class A workload.

  7. Local Shared References • Intrepid UPC: performance is poor on local shared accesses. • HP UPC: cache state has significant effects on local shared accesses.

  8. Remote Shared References • HP UPC and MuPC: caches help unit stride remote shared accesses. • Intrepid UPC does the best for remote shared accesses.

  9. Block Memory Operations • HP UPC: performance is poor on certain string functions. • Intrepid UPC: low performance on all categories.

  10. NPB – CG • The only case that scales well: Berkeley UPC + optimized code.

  11. NPB – EP

  12. NPB – FT • HP, Berkeley and MuPC: performance is comparable.

  13. NPB – IS • HP, Berkeley and MuPC: performance is comparable.

  14. NPB – MG • MG performance is very inconsistent.

  15. Conclusions • STREAM benchmarking: • UPC language overhead reduces performance of local shared references. • Remote reference caching helps stride-1 accesses. • Copying between two locations with the same affinity to a remote thread needs optimization. • NPB benchmarking: • Some implementation failed for some benchmarks. More stable and reliable implementations are needed. • Hand-tuning techniques (e.g. prefetching) are critical in performance. • Berkeley UPC is the best at handling unstructured, fine-grained references. • MuPC experience shows that it will be more rewarding to optimize remote shared references than to improve network interconnects.

  16. Thank you! For more information: http://www.upc.mtu.edu

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