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PAPI 3.0.8.1 on Blue Gene L

PAPI 3.0.8.1 on Blue Gene L. Using network performance counters to layout tasks for improved performance. Presentation overview. Project objectives PAPI explanation Blue Gene L explanation Current state of research. Project objectives. Upgrade PAPI on BG/L

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PAPI 3.0.8.1 on Blue Gene L

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  1. PAPI 3.0.8.1 on Blue Gene L Using network performance counters to layout tasks for improved performance

  2. Presentation overview • Project objectives • PAPI explanation • Blue Gene L explanation • Current state of research

  3. Project objectives • Upgrade PAPI on BG/L • Provide interface for network counters • Allow Lawrence Livermore National Lab users to also have access to PAPI • Using network counters to place tasks optimally on BG/L

  4. PAPI – Intro Courtesy of http://icl.cs.utk.edu/papi/

  5. PAPI – Intro • PAPI useful to profile your own programs. • Many tools based on PAPI • PapiEx – Command line measurement tool • PerfSuite – Aggregate measurement and statistical profiling package and API • HPCToolkit – Statistical profiling package • Many more!

  6. PAPI – Supported platforms • IBM – POWER3, 604, 604e, POWER4 • Cray T3E, Cray X1 • AMD – Athlon, Opteron • Intel – P1 to P4, Itanium I and II • UltraSparc I, II & III • MIPS R10K, R12K, R14K • Alpha

  7. PAPI – Generic Interface • Call sequence for generic interface • PAPI_library_init – Initialize memory for PAPI’s data structures • PAPI_create_eventset – Create an empty list of events • PAPI_add_event – Add events to be counted • PAPI_start – Begin counting all events within the specified eventset • PAPI_stop – Stop all counters and read their current values

  8. PAPI – Events: Presets • Presets – list of predefined events implemented on all systems where they can be supported • Not all presets available on every architecture (e.g. BG/L has no cache lower than L3 – thus L1 cache hit preset not applicable) • Native events form the basic building blocks for PAPI presets

  9. PAPI – Events: Presets Courtesy of http://icl.cs.utk.edu/papi/

  10. PAPI – Events: Native • In addition to the predefined PAPI preset events, the PAPI library also exposes a majority of the events native to each platform • Can be added to eventsets in the same manner as presets

  11. PAPI – Events: Native

  12. PAPI – Internals • Array of eventsets is the main portion

  13. PAPI – Other features • Multiplexing – If there are not enough hardware counters • Thread safe – Profiling is thread safe • Overflow detection – Hardware counters have limited space

  14. PAPI – PAPI2 vs PAPI3 • PAPI 3 significantly reduced overheads for starting, stopping and reading the counters Courtesy of http://icl.cs.utk.edu/papi/

  15. PAPI – PAPI2 vs PAPI3 • Better native event support in PAPI3 • Better thread support in PAPI3 • Overflow and Profiling enhancements in PAPI3 • Myriad bug fixes and code cleanup in PAPI3

  16. PAPI – PAPI2 vs PAPI3 • Overlapping eventsets supported in PAPI2 • Minor changes in the API – mostly dereferencing variables

  17. Blue Gene L – Intro • 65,536 nodes connected in 64 x 32 x 32 3D torus • Nodes made up of PowerPC 440 embedded processors • Smaller than most super computers • Consumes less power

  18. Blue Gene L

  19. Blue Gene L - Networks • 3D torus network (node to node) • Tree network (broadcasts)

  20. Blue Gene L – HW counters • 48 universal performance counters • 4 floating point unit counters • Counters 32 bit – must use virtual counters to prevent overflow

  21. Blue Gene L – HW counters

  22. Research – Overall goals • Network hardware counters new • Use network counters to determine traffic between tasks • Try to optimize placement of tasks to minimize communication latency • Given counts and distances: cost = counts * distance. Minimize over all nodes

  23. Research – Counting • First goal to determine what is being counted

  24. Research – Networks • For each MPI call – determine which network counters are being used • Tree is supposed to be for broadcasts • Torus is supposed to be for point to point communication • Ambiguities in the specification

  25. Research – Future decisions • How to profile a target application • Manually insert PAPI instrumentation: a lot of work • Instrument binaries with counting code • What information to store • All counts on each node: a lot of data • Sample of all nodes: not as accurate (what if the tasks behave / communicate differently?

  26. Research – Future decisions • How to use collected information • Profile an application to obtain counter feedback to determine optimized static task layout • Dynamically migrate tasks in response to counters

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