MPI + MPI: a new hybrid approach to parallel programming with MPI plus shared memory
Hybrid parallel programming with the message passing interface (MPI) for internode communication in conjunction with a shared-memory programming model to manage intranode parallelism has become a dominant approach to scalable parallel programming. While this model provides a great deal of flexibilit...
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| Published in: | Computing Vol. 95; no. 12; pp. 1121 - 1136 |
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| Main Authors: | , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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Vienna
Springer Vienna
01.12.2013
Springer Nature B.V |
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| ISSN: | 0010-485X, 1436-5057 |
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| Abstract | Hybrid parallel programming with the message passing interface (MPI) for internode communication in conjunction with a shared-memory programming model to manage intranode parallelism has become a dominant approach to scalable parallel programming. While this model provides a great deal of flexibility and performance potential, it saddles programmers with the complexity of utilizing two parallel programming systems in the same application. We introduce an MPI-integrated shared-memory programming model that is incorporated into MPI through a small extension to the one-sided communication interface. We discuss the integration of this interface with the MPI 3.0 one-sided semantics and describe solutions for providing portable and efficient data sharing, atomic operations, and memory consistency. We describe an implementation of the new interface in the MPICH2 and Open MPI implementations and demonstrate an average performance improvement of 40 % to the communication component of a five-point stencil solver. |
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| AbstractList | Hybrid parallel programming with the message passing interface (MPI) for internode communication in conjunction with a shared-memory programming model to manage intranode parallelism has become a dominant approach to scalable parallel programming. While this model provides a great deal of flexibility and performance potential, it saddles programmers with the complexity of utilizing two parallel programming systems in the same application. We introduce an MPI-integrated shared-memory programming model that is incorporated into MPI through a small extension to the one-sided communication interface. We discuss the integration of this interface with the MPI 3.0 one-sided semantics and describe solutions for providing portable and efficient data sharing, atomic operations, and memory consistency. We describe an implementation of the new interface in the MPICH2 and Open MPI implementations and demonstrate an average performance improvement of 40 % to the communication component of a five-point stencil solver. Hybrid parallel programming with the message passing interface (MPI) for internode communication in conjunction with a shared-memory programming model to manage intranode parallelism has become a dominant approach to scalable parallel programming. While this model provides a great deal of flexibility and performance potential, it saddles programmers with the complexity of utilizing two parallel programming systems in the same application. We introduce an MPI-integrated shared-memory programming model that is incorporated into MPI through a small extension to the one-sided communication interface. We discuss the integration of this interface with the MPI 3.0 one-sided semantics and describe solutions for providing portable and efficient data sharing, atomic operations, and memory consistency. We describe an implementation of the new interface in the MPICH2 and Open MPI implementations and demonstrate an average performance improvement of 40 % to the communication component of a five-point stencil solver.[PUBLICATION ABSTRACT] |
| Author | Balaji, Pavan Gropp, William Dinan, James Kale, Vivek Barrett, Brian Buntinas, Darius Hoefler, Torsten Thakur, Rajeev Brightwell, Ron |
| Author_xml | – sequence: 1 givenname: Torsten surname: Hoefler fullname: Hoefler, Torsten email: htor@inf.ethz.ch organization: ETH Zurich – sequence: 2 givenname: James surname: Dinan fullname: Dinan, James organization: Argonne National Laboratory – sequence: 3 givenname: Darius surname: Buntinas fullname: Buntinas, Darius organization: Argonne National Laboratory – sequence: 4 givenname: Pavan surname: Balaji fullname: Balaji, Pavan organization: Argonne National Laboratory – sequence: 5 givenname: Brian surname: Barrett fullname: Barrett, Brian organization: Sandia National Laboratories – sequence: 6 givenname: Ron surname: Brightwell fullname: Brightwell, Ron organization: Sandia National Laboratories – sequence: 7 givenname: William surname: Gropp fullname: Gropp, William organization: University of Illinois at Urbana-Champaign – sequence: 8 givenname: Vivek surname: Kale fullname: Kale, Vivek organization: University of Illinois at Urbana-Champaign – sequence: 9 givenname: Rajeev surname: Thakur fullname: Thakur, Rajeev organization: Argonne National Laboratory |
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| Cites_doi | 10.1007/978-3-642-33518-1_18 10.1145/1065010.1065042 10.1145/331532.331581 10.1109/MCSE.2010.122 10.1145/1379022.1375591 10.1109/MC.2006.180 10.1145/2076450.2076465 10.1137/0911008 10.1016/j.jcp.2009.01.017 10.1109/HiPC.2012.6507509 10.1016/S0167-8191(00)00072-7 10.1137/1.9780898718003 10.1109/PDP.2009.43 10.1002/1096-9128(200010)12:12<1121::AID-CPE531>3.0.CO;2-N 10.1145/1040305.1040336 10.1145/363911.363920 |
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| Keywords | Hybrid parallel programming 68N19 other progamming techniques (objects-oriented, sequential, concurrent, automatic, etc.) MPI-3.0 Shared memory |
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| References | Wolf MM, Heroux MA, Boman EG (2010) Factors impacting performance of multithreaded sparse triangular solve. Technical report SAND2010-0331 presented at VECPAR’10 EslerKPKimJCeperleyDMShulenburgerLAccelerating quantum Monte Carlo simulations of real materials on GPU clustersComput Sci Eng2012141405110.1109/MCSE.2010.122 BoehmHJAdveSVYou do not know jack about shared variables or memory modelsCommun. ACM2012552485410.1145/2076450.2076465 Saad Y (2003) Iterative methods for sparse linear systems. Society for Industrial and Applied Mathematics SmithLBullMDevelopment of mixed mode MPI/OpenMP applicationsSci Program200192,38398 Bhargava P (1997) MPI-LITE: multithreading support for MPI. http://pcl.cs.ucla.edu/projects/sesame/mpi_lite/mpi_lite.html Shen K, Tang H, Yang T (1999) Adaptive two-level thread management for fast MPI execution on shared memory machines. In: Proceedings of the ACM/IEEE conference on supercomputing Sandia National Laboratories (2012) Mantevo project. http://www.mantevo.org Heroux MA, Brightwell R, Wolf MM (2011) Bi-modal MPI and MPI+threads computing on scalable multicore systems. IJHPCA (Submitted) Boehm HJ (2005) Threads cannot be implemented as a library. In: Proceedings of the 2005 ACM SIGPLAN conference on programming language design and implementation. PLDI ’05, New York, NY, USA, ACM pp 261–268 Rabenseifner R, Hager G, Jost G (2009) Hybrid MPI/OpenMP parallel programming on clusters of multi-core SMP nodes. In: Proceedings of the 17th Euromicro international conference on parallel, distributed and network-based processing TaftJRAchieving 60 GFLOP/s on the production CFD code OVERFLOW-MLPParallel Comput200127452153610.1016/S0167-8191(00)00072-70972.68083 BoehmHJAdveSVFoundations of the C++ concurrency memory modelSIGPLAN Not2008436687810.1145/1379022.1375591 EslerKPKimJCeperleyDMPurwantoWWalterEJKrakauerHZhangSKentPRCHennigRGUmrigarCBajdichMKolorenJMitasLSrinivasanAQuantum monte carlo algorithms for electronic structure at the petascale; the endstation projectJ Phys20081251012057 AhoAVSethiRUllmanJDCompilers: principles, techniques, and tools1986BostonAddison-Wesley Longman Publishing Co. Inc. SmithLKentPDevelopment and performance of a mixed OpenMP/MPI quantum Monte Carlo codeConcurr Pract Exp200012121121112910.1002/1096-9128(200010)12:12<1121::AID-CPE531>3.0.CO;2-N1008.68557 Niu Q, Dinan J, Tirukkovalur S, Mitas L, Wagner L, Sadayappan P (2012) A global address space approach to automated data management for parallel quantum Monte Carlo applications. In: Proceedings 19th international conference on high performance computing. HiPC’12 Esler KP Einspline libaray. Online: http://einspline.svn.sourceforge.net Hoefler T, Dinan J, Buntinas D, Balaji P, Barrett B, Brightwell R, Gropp W, Kale V, Thakur R (2012) Leveraging MPIs one-sided communication interface for shared-memory programming. In: Träff J, Benkner S, Dongarra J (eds) Recent advances in the message passing interface. vol 7490, pp 132–141 TangHShenKYangTProgram transformation and runtime support for threaded MPI execution on shared memory machinesACM Trans Program Lang Syst20002267370010.1145/363911.363920 MPI Forum (2012) MPI: a message-passing interface standard. version 3.0 Manson J, Pugh W, Adve SV (2005) The Java memory model. In: Proceedings of the 32nd ACM SIGPLAN-SIGACT symposium on principles of programming languages. POPL ’05, New York, ACM pp 378–391 LeeEAThe problem with threadsComputer2006395334210.1109/MC.2006.180 Pérachec M, Carribault P, Jourdren H (2009) MPC-MPI: an MPI implementation reducing the overall memory consumption. In: Proceedings of EuroPVM/MPI 2009, Springer, pp 94–103 Shirley D (2000) Enhancing MPI applications through selective use of shared memory on SMPs. In: Proceedings of the 1st SIAM conference on CSE SaltzJHAggregation methods for solving sparse triangular systems on multiprocessorsSIAM J Sci Stat Comput1990111123144103223110.1137/09110080692.65009 Demaine E (1997) A threads-only MPI implementation for the development of parallel programs. In: Proceedings of the 11th international symposium on HPC systems. pp 153–163 WagnerLKBajdichMMitasLQwalk: a quantum monte carlo program for electronic structureJ Comput Phys200922893390340410.1016/j.jcp.2009.01.017 Los Alamos National Laboratory (2001) Unified parallel software users’ guide and reference manual. http://public.lanl.gov/ups/Doc_Directory/UserGuide/UserGuide.pdf AV Aho (324_CR15) 1986 324_CR19 JR Taft (324_CR12) 2001; 27 KP Esler (324_CR29) 2012; 14 LK Wagner (324_CR25) 2009; 228 324_CR21 324_CR23 EA Lee (324_CR18) 2006; 39 324_CR26 324_CR27 324_CR9 JH Saltz (324_CR22) 1990; 11 H Tang (324_CR7) 2000; 22 324_CR20 L Smith (324_CR2) 2001; 9 324_CR1 324_CR4 HJ Boehm (324_CR17) 2008; 43 324_CR3 324_CR6 324_CR5 324_CR8 HJ Boehm (324_CR14) 2012; 55 KP Esler (324_CR24) 2008; 125 324_CR11 324_CR10 324_CR13 L Smith (324_CR28) 2000; 12 324_CR16 |
| References_xml | – reference: Manson J, Pugh W, Adve SV (2005) The Java memory model. In: Proceedings of the 32nd ACM SIGPLAN-SIGACT symposium on principles of programming languages. POPL ’05, New York, ACM pp 378–391 – reference: Hoefler T, Dinan J, Buntinas D, Balaji P, Barrett B, Brightwell R, Gropp W, Kale V, Thakur R (2012) Leveraging MPIs one-sided communication interface for shared-memory programming. In: Träff J, Benkner S, Dongarra J (eds) Recent advances in the message passing interface. vol 7490, pp 132–141 – reference: Bhargava P (1997) MPI-LITE: multithreading support for MPI. http://pcl.cs.ucla.edu/projects/sesame/mpi_lite/mpi_lite.html – reference: SmithLKentPDevelopment and performance of a mixed OpenMP/MPI quantum Monte Carlo codeConcurr Pract Exp200012121121112910.1002/1096-9128(200010)12:12<1121::AID-CPE531>3.0.CO;2-N1008.68557 – reference: AhoAVSethiRUllmanJDCompilers: principles, techniques, and tools1986BostonAddison-Wesley Longman Publishing Co. Inc. – reference: SaltzJHAggregation methods for solving sparse triangular systems on multiprocessorsSIAM J Sci Stat Comput1990111123144103223110.1137/09110080692.65009 – reference: Demaine E (1997) A threads-only MPI implementation for the development of parallel programs. In: Proceedings of the 11th international symposium on HPC systems. pp 153–163 – reference: Rabenseifner R, Hager G, Jost G (2009) Hybrid MPI/OpenMP parallel programming on clusters of multi-core SMP nodes. In: Proceedings of the 17th Euromicro international conference on parallel, distributed and network-based processing – reference: BoehmHJAdveSVYou do not know jack about shared variables or memory modelsCommun. ACM2012552485410.1145/2076450.2076465 – reference: TaftJRAchieving 60 GFLOP/s on the production CFD code OVERFLOW-MLPParallel Comput200127452153610.1016/S0167-8191(00)00072-70972.68083 – reference: Saad Y (2003) Iterative methods for sparse linear systems. Society for Industrial and Applied Mathematics – reference: Shirley D (2000) Enhancing MPI applications through selective use of shared memory on SMPs. In: Proceedings of the 1st SIAM conference on CSE – reference: Niu Q, Dinan J, Tirukkovalur S, Mitas L, Wagner L, Sadayappan P (2012) A global address space approach to automated data management for parallel quantum Monte Carlo applications. In: Proceedings 19th international conference on high performance computing. HiPC’12 – reference: MPI Forum (2012) MPI: a message-passing interface standard. version 3.0 – reference: Shen K, Tang H, Yang T (1999) Adaptive two-level thread management for fast MPI execution on shared memory machines. In: Proceedings of the ACM/IEEE conference on supercomputing – reference: EslerKPKimJCeperleyDMPurwantoWWalterEJKrakauerHZhangSKentPRCHennigRGUmrigarCBajdichMKolorenJMitasLSrinivasanAQuantum monte carlo algorithms for electronic structure at the petascale; the endstation projectJ Phys20081251012057 – reference: Heroux MA, Brightwell R, Wolf MM (2011) Bi-modal MPI and MPI+threads computing on scalable multicore systems. IJHPCA (Submitted) – reference: Esler KP Einspline libaray. Online: http://einspline.svn.sourceforge.net/ – reference: SmithLBullMDevelopment of mixed mode MPI/OpenMP applicationsSci Program200192,38398 – reference: Pérachec M, Carribault P, Jourdren H (2009) MPC-MPI: an MPI implementation reducing the overall memory consumption. In: Proceedings of EuroPVM/MPI 2009, Springer, pp 94–103 – reference: Wolf MM, Heroux MA, Boman EG (2010) Factors impacting performance of multithreaded sparse triangular solve. 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