Rolf Rabenseifner, High Performance Computing Center Stuttgart (HLRS), Germany
Georg Hager, Erlangen Regional Computing Center (RRZE), Germany
Gabriele Jost, Texas Advanced Computing Center, The University of Texas at Austin, USA
Half-day Tutorial proposed for Supercomputing 2010 (SC2010)
Most HPC systems are clusters of shared memory nodes. Such systems can be PC clusters with single/multi-socket and multi-core SMP nodes, but also "constellation" type systems with large SMP nodes. Parallel programming may combine the distributed memory parallelization on the node inter-connect with the shared memory parallelization inside of each node.
This tutorial analyzes the strength and weakness of several parallel programming models on clusters of SMP nodes. Various hybrid MPI+OpenMP programming models are compared with pure MPI. Benchmark results of several platforms are presented. The thread-safety quality of several existing MPI libraries is also discussed. Case studies will be provided to demonstrate various aspects of hybrid MPI/OpenMP programming. Another option is the use of distributed virtual shared-memory technologies. Application categories that can take advantage of hybrid programming are identified. Multi-socket-multi-core systems in highly parallel environments are given special consideration.
Straightforward programming of clusters of shared memory nodes often leads to unsatisfactory performance results. The participant learns hybrid parallel programming. Pure message passing (one MPI process on each core) and mixed model programming (multi-threaded MPI processes) only partially fit to the architecture of modern HPC systems. The tutorial teaches about solving those performance problems, but also teaches technical aspects of mixed model programming. At the end of the tutorial, the attendee will be sensitive about many pitfalls in parallel programming on clusters of SMP nodes. He/she has learned about the thread-safety level of MPI libraries and also about the limits of pure OpenMP enabled by virtual shared memory technology. The participant can also learn from sample applications as, e.g., a hybrid implementation of sparse matrix-vector multiply used in iterative solvers.
People who are in charge with the development of efficient parallel software on clusters of shared memory nodes.
20% Introductory, 50% Intermediate, 30% Advanced
Some knowledge about parallel programming with MPI and OpenMP.
Why the topic is relevant to SC attendees:
Most systems in HPC and supercomputing environments are clusters of SMP nodes, ranging from clusters of dual/quad-core CPUs to large constellations in Tera-scale computing. Numerical software for these systems often scales worse than expected. This tutorial helps to find the appropriate programming model and to prevent pitfalls with mixed model (MPI+OpenMP) programming.
General description of tutorial content:
Most HPC systems are clusters of shared memory nodes. Such systems can be PC clusters with quad-core single/multi CPU boards, but also "constellation" type systems with large SMP nodes. Parallel programming must combine the distributed memory parallelization on the node inter-connect with the shared memory parallelization inside of each node.
This tutorial analyzes the strength and weakness of several parallel programming models on clusters of SMP nodes. Various hybrid MPI+OpenMP programming models are compared with pure MPI. Benchmark results of several platforms are presented. Bandwidth and latency is shown for intra-socket, inter-socket and inter-node communication. The affinity of processes and their threads and memory is a key-factor. The thread-safety status of several existing MPI libraries is also discussed. Case studies with the Multi-zone NAS Parallel Benchmarks will be provided to demonstrate various aspect of hybrid MPI/OpenMP programming.
Another option is the use of distributed virtual shared-memory technologies which enable the utilization of "near-standard" OpenMP on distributed memory architectures. The performance issues of this approach and its impact on existing applications are discussed. This tutorial analyzes strategies to overcome typical drawbacks of easily usable programming schemes on clusters of SMP nodes.
Resume / Curriculum Vitae
Dr. Rolf Rabenseifner
Rolf Rabenseifner studied mathematics and physics at the University of Stuttgart. Since 1984, he has worked at the High-Performance Computing-Center Stuttgart (HLRS). He led the projects DFN-RPC, a remote procedure call tool, and MPI-GLUE, the first metacomputing MPI combining different vendor's MPIs without loosing the full MPI interface. In his dissertation, he developed a controlled logical clock as global time for trace-based profiling of parallel and distributed applications. Since 1996, he has been a member of the MPI-2 Forum and since Dec. 2007 he is in the steering committee of the MPI-3 Forum and was responsible for new MPI-2.1 standard. From January to April 1999, he was an invited researcher at the Center for High-Performance Computing at Dresden University of Technology.
Currently, he is head of Parallel Computing - Training and Application Services at HLRS. He is involved in MPI profiling and benchmarking, e.g., in the HPC Challenge Benchmark Suite. In recent projects, he studied parallel I/O, parallel programming models for clusters of SMP nodes, and optimization of MPI collective routines. In workshops and summer schools, he teaches parallel programming models in many universities and labs in Germany.
List of publications: https://fs.hlrs.de//projects/rabenseifner/publ/
International teaching: https://fs.hlrs.de//projects/rabenseifner/publ/#tutorials
Dr. Georg Hager
Georg Hager studied theoretical physics at the University of Bayreuth, specializing in nonlinear dynamics, and holds a PhD in Computational Physics from the University of Greifswald. He is a senior researcher in the HPC Services group at Erlangen Regional Computing Center (RRZE), which is part of the University of Erlangen-Nuremberg. Recent research includes architecture-specific optimization strategies for current microprocessors and special topics in shared memory and hybrid programming. His daily work encompasses all aspects of user support in High Performance Computing like tutorials and training, code parallelization, profiling and optimization and the assessment of novel computer architectures and tools.
List of publications: http://www.blogs.uni-erlangen.de/hager/topics/Publications/
List of talks and teaching activities: http://www.blogs.uni-erlangen.de/hager/topics/Talks/
Book: Georg Hager and Gerhard Wellein: Introduction to High Performance Computing for Scientists and Engineers. CRC Press, July 2010, ISBN 978-1439811924
Dr. Gabriele Jost
Gabriele Jost obtained her doctorate in Applied Mathematics from the University of Gφttingen, Germany. For more than a decade she worked for various vendors (Suprenum GmbH, Thinking Machines Corporation, and NEC) of high performance parallel computers in the areas of vectorization, parallelization, performance analysis and optimization of scientific and engineering applications.
In 1998 she joined the NASA Ames Research Center in Moffett Field, California, USA as a Research Scientist. Here her work focused on evaluating and enhancing tools for parallel program development and investigating the usefulness of different parallel programming paradigms.
In 2005 she moved from California to the Pacific Northwest and joined Sun Microsystems as a staff engineer in the Compiler Performance Engineering team. In 2006 she joined Oracle as a Principal Software Engineer and worked on performance analysis of application server software. In 2008 she decided to return to California and pursue her passion for High Performance Computing. She joined the Texas Advanced Computing Center and works as an on-site analyst at the Naval Postgraduate School in Monterey, CA.
List of publications:
Book: Barbara Chapman, Gabriele Jost, and Ruud van der Pas: Using OpenMP. MIT Press, Oct. 2007, ISBN 978-0262533027.
Keywords: Clusters, Optimization, Parallel Programming, Performance, Tools
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