Presentation at the 4th PMEOPDS Workshop

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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
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Presentation Outline

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

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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,

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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)

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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.

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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.

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Local Shared References

• Intrepid UPC performance is poor on local shared
  accesses.
• HP UPC cache state has significant effects on
  local shared accesses.

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Remote Shared References

• HP UPC and MuPC caches help unit stride remote
  shared accesses.
• Intrepid UPC does the best for remote shared
  accesses.

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Block Memory Operations

• HP UPC performance is poor on certain string
  functions.
• Intrepid UPC low performance on all categories.

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NPB CG

• The only case that scales well Berkeley UPC
  optimized code.

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NPB EP
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NPB FT

• HP, Berkeley and MuPC performance is comparable.

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NPB IS

• HP, Berkeley and MuPC performance is comparable.

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NPB MG

• MG performance is very inconsistent.

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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.

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