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The Future of MPI

The Future of MPI. William Gropp Argonne National Laboratory www.mcs.anl.gov/~gropp. The Success of MPI. Applications Most recent Gordon Bell prize winners use MPI Libraries Growing collection of powerful software components Tools Performance tracing (Vampir, Jumpshot, etc.)

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The Future of MPI

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  1. The Future of MPI William GroppArgonne National Laboratorywww.mcs.anl.gov/~gropp

  2. The Success of MPI • Applications • Most recent Gordon Bell prize winners use MPI • Libraries • Growing collection of powerful software components • Tools • Performance tracing (Vampir, Jumpshot, etc.) • Debugging (Totalview, etc.) • Results • Papers: http://www.mcs.anl.gov/mpi/papers • Clusters • Ubiquitous parallel computing

  3. Why Was MPI Successful? • It address all of the following issues: • Portability • Performance • Simplicity and Symmetry • Modularity • Composability • Completeness

  4. Portability and Performance • Portability does not require a “lowest common denominator” approach • Good design allows the use of special, performance enhancing features without requiring hardware support • MPI’s nonblocking message-passing semantics allows but does not require “zero-copy” data transfers • BTW, it is “Greatest Common Denominator”

  5. Simplicity and Symmetry • MPI is organized around a small number of concepts • The number of routines is not a good measure of complexity • Fortran • Large number of intrinsic functions • C and Java runtimes are large • Development Frameworks • Hundreds to thousands of methods • This doesn’t bother millions of programmers

  6. Measuring Complexity • Complexity should be measured in the number of concepts, not functions or size of the manual • MPI is organized around a few powerful concepts • Point-to-point message passing • Datatypes • Blocking and nonblocking buffer handling • Communication contexts and process groups

  7. Elegance of Design • MPI often uses one concept to solve multiple problems • Example: Datatypes • Describe noncontiguous data transfers, necessary for performance • Describe data formats, necessary for heterogeneous systems • “Proof” of elegance: • Datatypes exactly what is needed for high-performance I/O, added in MPI-2.

  8. Parallel I/O • Collective model provides high I/O performance • Matches applications most general view: objects, distributed among processes • MPI Datatypes extend I/O model to noncontiguous data in both memory and file • Unix readv/writev only applies to memory

  9. Posix style I/O Parallel I/O Performance with MPI-IO Structured Mesh I/O Unstructured Grid I/O (Posix too slow to show)

  10. Modularity • Modern algorithms are hierarchical • Do not assume that all operations involve all or only one process • Provide tools that don’t limit the user • Modern software is built from components • MPI designed to support libraries • Many applications have no explicit MPI calls; all MPI contained within well-designed libraries

  11. Composability • Environments are built from components • Compilers, libraries, runtime systems • MPI designed to “play well with others” • MPI exploits newest advancements in compilers • … without ever talking to compiler writers • OpenMP is an example

  12. Completeness • MPI provides a complete parallel programming model and avoids simplifications that limit the model • Contrast: Models that require that synchronization only occurs collectively for all processes or tasks • Make sure that the functionality is there when the user needs it • Don’t force the user to start over with a new programming model when a new feature is needed

  13. Is Ease of Use the Overriding Goal? • MPI often described as “the assembly language of parallel programming” • C and Fortran have been described as “portable assembly languages” • Ease of use is important. But completeness is more important. • Don’t force users to switch to a different approach as their application evolves

  14. Lessons From MPI • A general programming model for high-performance technical computing must address many issues to succeed • Even that is not enough. Also needs: • Good design • Buy-in by the community • Effective implementations • MPI achieved these through an Open Standards Process

  15. An Open and Balanced Process • Balanced representation from • Users • What users want and need • Including correctness • Implementers (Vendors) • What can be provided • Many MPI features determined by implementation needs • Researchers • Directions and Futures • MPI planned for interoperation with OpenMP before OpenMP conceived • Support for libraries strongly influenced by research

  16. Where Next? • Improving MPI • Simplifying and enhancing the expression of MPI programs • Improving MPI Implementations • Performance • Performance • Performance • New Directions • What can displace (or complement) MPI?(Yesterday’s panel presentation on programming models project and tomorrow’s panel on the future of supercomputing)

  17. Improving MPI • Simpler interfaces • Use compiler or precompiler techniques to support simpler, integrated syntax • Fortran 95 arrays, datatypes in C/C++ • Eliminate function calls • Use program analysis and transformation to inline operations • More tools for correctness and performance debugging • MPI profiling interface is a good start • Debugger interface used by Totalview is an example of tool development • Effort to provide a common interface to internal performance data, such as idle time waiting for a message • Changes to MPI • E.g., MPI-2 RMA lacks a read-modify-write • But don’t hold your breath • These require research and experimentation before they are ready for a standardization process

  18. Improving MPI Implementations • Faster Point-to-point • Some current implementations make unnecessary copies • Collective operations • Better algorithms exist • SMP optimizations • Scatter-gather broadcast, reduce, etc. • Optimizing for new hardware • RDMA networks • NIC-enabled remote atomic operations • Wide area networks • Optimizations for high latency • Speculative sends • Quality of service extensions (through MPI attributes) • Massive scaling • Many implementations optimize internal buffers for modest numbers of processes • Some MPI routines (e.g., MPI_Graph_create) do not have scalable definitions

  19. More Improvements for MPI Implementations • Reduce latency • Automatic techniques to compress code paths • Closer match to hardware capabilities • Improve RMA • Many current implementations at best functional • Parallel I/O, particularly for clusters • Communication aggregation • Reliability in the presence of faults • Fault tolerance • Exploit MPI Intercommunicators to generalize the two-party model • Thread safe and efficient implementations • Lock-free design • Software engineering for common MPI implementation source tree • Many groups working on improved MPI implementations • MPICH-2 is an all-new and efficient implementation • Includes many of these ideas • Designed, as MPICH was, to encourage others to experiment and extend MPI

  20. What’s New in MPICH2 • Beta-test version available for groups that expect to perform research on MPI implementations with MPICH2 • Version 0.92 released last Friday • Contains • All of MPI-1, MPI-I/O, service functions from MPI-2, active-target RMA • C, C++, Fortran 77 bindings • Example devices for TCP, Infiniband, shared memory • Documentation • Passes extensive correctness tests • Intel test suite (as corrected); good unit test suite • MPICH test suite; adequate system test suite • Notre Dame C++ tests, based on IBM C test suite • Passes more tests than MPICH1 

  21. MPICH2 Research • All new implementation is our vehicle for research in • Thread safety and efficiency (e.g., avoid thread locks) • Optimized MPI datatypes • Optimized Remote Memory Access (RMA) • High Scalability (64K MPI processes and more) • Exploiting Remote Direct Memory Access (RDMA) capable networks • All of MPI-2, including dynamic process management, parallel I/O, RMA • Usability and Robustness • Software engineering techniques that automate and simplify creating and maintaining a solid, user-friendly implementation • Allow extensive runtime error checking but do not require it • Integrated performance debugging • Clean interfaces to other system components such as scalable process managers

  22. Some Target Platforms • Clusters (TCP, UDP, Infiniband, Myrinet, Proprietary Interconnects, …) • Clusters of SMPs • Grids (UDP, TCP, Globus I/O, …) • Cray Red Storm • BlueGene/x • 64K processors; 64K address spaces • ANL/IBM developing MPI for BG/L • QCDoC • Cray X1 (at least I/O) • Other systems

  23. (Logical) Structure of MPICH-2 MPICH-2 PMI ADIO ADI-3 remshell Vendors PVFS Fork MPD bproc Other parallel file systems NFS XFS Windows Unix(python) HFS SFS Myrinet, Other NIC Multi- Method Existing Channel Interface Portals In Progress TCP shmem Infiniband For others MM BG/L shmem TCP

  24. Conclusions • The Future of MPI is Bright! • Higher-performance implementations • More libraries and applications • Better tools for developing and tuning MPI programs • Leverage of complementary technologies • Full MPI-2 implementations will become common • Several already exist; many ES apps use MPI RMA

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