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Performance Evaluation of S3D using TAU Sameer Shende tau-team@cs.uoregon.edu
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TAU Performance SystemS3D Scalability Study2 Acknowledgements Alan Morris [UO] Kevin Huck [UO] Allen D. Malony [UO] Kenneth Roche [ORNL] Bronis R. de Supinski [LLNL] The performance data presented here is available at: http://www.cs.uoregon.edu/research/tau/s3d
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TAU Performance SystemS3D Scalability Study3 TAU Parallel Performance System http://www.cs.uoregon.edu/research/tau/ Multi-level performance instrumentation Multi-language automatic source instrumentation Flexible and configurable performance measurement Widely-ported parallel performance profiling system Computer system architectures and operating systems Different programming languages and compilers Support for multiple parallel programming paradigms Multi-threading, message passing, mixed-mode, hybrid
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TAU Performance SystemS3D Scalability Study4 Scalability Study Harness testcase Platform: Jaguar Cray XT3 at ORNL 1728p 8000p Goal: to identify nodes with poor performance Scalability of MPI operations
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TAU Performance SystemS3D Scalability Study5 Using MPICH_RANK_REORDER_METHOD=1 Inclusive time increases! 512p
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TAU Performance SystemS3D Scalability Study6 Using MPICH_RANK_REORDER_METHOD=1 MPI_Wait time increases! 512p
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TAU Performance SystemS3D Scalability Study7 Scatter Plot - Axes and Color 6400p Two processors have low MPI_Wait times!
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TAU Performance SystemS3D Scalability Study8 Scatter Plot - Axes and Color 1728 p Two processors do something different!
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TAU Performance SystemS3D Scalability Study9 Second run 1728p shows one cpu in blue The variation (20.99s to 95s) is not as much in this run
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TAU Performance SystemS3D Scalability Study10 Scatter Plot - Axes and Color 12000p
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TAU Performance SystemS3D Scalability Study11 MPI_Wait() - 1728p - Identifying the ranks!
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TAU Performance SystemS3D Scalability Study12 MPI_Wait() - 1728p - Second run
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TAU Performance SystemS3D Scalability Study13 MetaData
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TAU Performance SystemS3D Scalability Study14 MetaData MPI Processor name nid1194
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TAU Performance SystemS3D Scalability Study15 8000p Two slow processors - MFlops
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TAU Performance SystemS3D Scalability Study16 8000p Two slow processors - Time Running the 8000p job with metadata next. Job is waiting in queue.
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TAU Performance SystemS3D Scalability Study17 S3D - Building with TAU Change name of compiler in build/make.XT3 ftn=> tau_f90.sh cc => tau_cc.sh Set compile time environment variables setenv TAU_MAKEFILE /spin/proj/perc/TOOLS/tau_latest/xt3/lib/ Makefile.tau-callpath-multiplecounters-mpi-papi-pdt-pgi Choose callpath, PAPI counters, MPI profiling, PDT for source instrumentation setenv TAU_OPTIONS ‘-optTauSelectFile=select.tau -optPreProcess’ Selective instrumentation file eliminates instrumentation in lightweight routines Pre-process Fortran source code using cpp before compiling Set runtime environment variables for instrumentation control and event PAPI counter selection in job submission script: export TAU_THROTTLE=1 export COUNTER1 GET_TIME_OF_DAY export COUNTER2 PAPI_FP_INS export COUNTER3 PAPI_L1_DCM export COUNTER4 PAPI_RES_STL export COUNTER5 PAPI_L2_DCM
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TAU Performance SystemS3D Scalability Study18 Selective Instrumentation in TAU % cat select.tau BEGIN_EXCLUDE_LIST MCADIF GETRATES TRANSPORT_M::MCAVIS_NEW MCEDIF MCACON CKYTCP THERMCHEM_M::MIXCP THERMCHEM_M::MIXENTH THERMCHEM_M::GIBBSENRG_ALL_DIMT CKRHOY MCEVAL4 THERMCHEM_M::HIS THERMCHEM_M::CPS THERMCHEM_M::ENTROPY END_EXCLUDE_LIST BEGIN_INSTRUMENT_SECTION loops routine="#" END_INSTRUMENT_SECTION
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TAU Performance SystemS3D Scalability Study19 Getting Access to TAU on Jaguar set path=(/spin/proj/perc/TOOLS/tau_latest/x86_64/bin $path) Choose Stub Makefiles (TAU_MAKEFILE env. var.) from /spin/proj/perc/TOOLS/tau_latest/xt3/lib/Makefile.* Makefile.tau-mpi-pdt-pgi (flat profile) Makefile.tau-mpi-pdt-pgi-trace (event trace, for use with Vampir) Makefile.tau-callpath-mpi-pdt-pgi (single metric, callpath profile) Binaries of S3D can be found in: ~sameer/scratch/S3D-BINARIES withtau »papi, multiplecounters, mpi, pdt, pgi options without_tau
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TAU Performance SystemS3D Scalability Study20 Concluding Discussion Performance tools must be used effectively More intelligent performance systems for productive use Evolve to application-specific performance technology Deal with scale by “full range” performance exploration Autonomic and integrated tools Knowledge-based and knowledge-driven process Performance observation methods do not necessarily need to change in a fundamental sense More automatically controlled and efficiently use Develop next-generation tools and deliver to community Open source with support by ParaTools, Inc. http://www.cs.uoregon.edu/research/tau
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TAU Performance SystemS3D Scalability Study21 Support Acknowledgements Department of Energy (DOE) Office of Science LLNL, LANL, ORNL, ASC PERI
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