Component-based scientific application development
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Proceedings - 2012 SC Companion: High Performance Computing, Networking Storage and Analysis, SCC 2012
The predictions for exascale computing are dire. Although we have benefited from a consistent supercomputer architecture design, even across manufacturers, for well over a decade, recent trends indicate that future high-performance computers will have different hardware structure and programming models to which software must adapt. This paper provides an informal discussion on the ways in which changes in high-performance computing architecture will profoundly affect the scalability of our current generation of scientific visualization and analysis codes and how we must adapt our applications, workflows, and attitudes to continue our success at exascale computing. © 2012 IEEE.
Proceedings - 2012 SC Companion: High Performance Computing, Networking Storage and Analysis, SCC 2012
The push to exascale computing is informed by the assumption that the architecture, regardless of the specific design, will be fundamentally different from petascale computers. The Mantevo project has been established to produce a set of proxies, or 'miniapps,' which enable rapid exploration of key performance issues that impact a broad set of scientific applications programs of interest to ASC and the broader HPC community. Understanding the conditions under which a miniapp can be confidently used as predictive of an applications' behavior must be clearly elucidated. Toward this end, we have developed a methodology for assessing the predictive capabilities of application proxies. Adhering to the spirit of experimental validation, our approach provides a framework for examining data from the application with that provided by their proxies. In this poster we present this methodology, and apply it to three miniapps developed by the Mantevo project. © 2012 IEEE.
Reliability Engineering and System Safety
Sensitivity analysis is comprised of techniques to quantify the effects of the input variables on a set of outputs. In particular, sensitivity indices can be used to infer which input parameters most significantly affect the results of a computational model. With continually increasing computing power, sensitivity analysis has become an important technique by which to understand the behavior of large-scale computer simulations. Many sensitivity analysis methods rely on sampling from distributions of the inputs. Such sampling-based methods can be computationally expensive, requiring many evaluations of the simulation; in this case, the Sobol method provides an easy and accurate way to compute variance-based measures, provided a sufficient number of model evaluations are available. As an alternative, meta-modeling approaches have been devised to approximate the response surface and estimate various measures of sensitivity. In this work, we consider a variety of sensitivity analysis methods, including different sampling strategies, different meta-models, and different ways of evaluating variance-based sensitivity indices. The problem we consider is the 1-D Riemann problem. By a careful choice of inputs, discontinuous solutions are obtained, leading to discontinuous response surfaces; such surfaces can be particularly problematic for meta-modeling approaches. The goal of this study is to compare the estimated sensitivity indices with exact values and to evaluate the convergence of these estimates with increasing samples sizes and under an increasing number of meta-model evaluations. © 2011 Elsevier Ltd. All rights reserved.
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This report presents a specification for the Portals 4.0 network programming interface. Portals 4.0 is intended to allow scalable, high-performance network communication between nodes of a parallel computing system. Portals 4.0 is well suited to massively parallel processing and embedded systems. Portals 4.0 represents an adaption of the data movement layer developed for massively parallel processing platforms, such as the 4500-node Intel TeraFLOPS machine. Sandias Cplant cluster project motivated the development of Version 3.0, which was later extended to Version 3.3 as part of the Cray Red Storm machine and XT line. Version 4.0 is targeted to the next generation of machines employing advanced network interface architectures that support enhanced offload capabilities.
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Proposed for publication in Engineering with Computers.
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Proposed for publication in Human Factors: The Journal of Human Factors and Ergonomics Society.
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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Message passing paradigms provide for many to one messaging patterns that result in receive side resource exhaustion. Traditionally, MPI implementations layered over the Portals network programming interface provided a large default unexpected receive buffer space, the user was expected to configure the buffer size to the application demand, and the application was aborted when the buffer space was overrun. The Portals 4 design provides a set of primitives for implementing scalable resource exhaustion recovery without negatively impacting normal operation. A resource exhaustion recovery protocol for MPI implementations is presented, as well as performance results for an Open MPI implementation of the protocol. © 2012 Springer-Verlag.
Proceedings of the 2012 IEEE 26th International Parallel and Distributed Processing Symposium, IPDPS 2012
With the ubiquity of multicore processors, it is crucial that solvers adapt to the hierarchical structure of modern architectures. We present ShyLU, a "hybrid-hybrid" solver for general sparse linear systems that is hybrid in two ways: First, it combines direct and iterative methods. The iterative part is based on approximate Schur complements where we compute the approximate Schur complement using a value-based dropping strategy or structure-based probing strategy. Second, the solver uses two levels of parallelism via hybrid programming (MPI+threads). ShyLU is useful both in shared-memory environments and on large parallel computers with distributed memory. In the latter case, it should be used as a sub domain solver. We argue that with the increasing complexity of compute nodes, it is important to exploit multiple levels of parallelism even within a single compute node. We show the robustness of ShyLU against other algebraic preconditioners. ShyLU scales well up to 384 cores for a given problem size. We also study the MPI-only performance of ShyLU against a hybrid implementation and conclude that on present multicore nodes MPI-only implementation is better. However, for future multicore machines (96 or more cores) hybrid/ hierarchical algorithms and implementations are important for sustained performance. © 2012 IEEE.
Proceedings of the 2012 IEEE 26th International Parallel and Distributed Processing Symposium, IPDPS 2012
We design, implement, and evaluate algorithms for computing a matching of maximum cardinality in a bipartite graph on multicore and massively multithreaded computers. As computers with larger numbers of slower cores dominate the commodity processor market, the design of multithreaded algorithms to solve large matching problems becomes a necessity. Recent work on serial algorithms for the matching problem has shown that their performance is sensitive to the order in which the vertices are processed for matching. In a multithreaded environment, imposing a serial order in which vertices are considered for matching would lead to loss of concurrency and performance. But this raises the question: Would parallel matching algorithms on multithreaded machines improve performance over a serial algorithm? We answer this question in the affirmative. We report efficient multithreaded implementations of three classes of algorithms based on their manner of searching for augmenting paths: breadth-first-search, depth-first-search, and a combination of both. The Karp-Sipser initialization algorithm is used to make the parallel algorithms practical. We report extensive results and insights using three shared-memory platforms (a 48-core AMD Opteron, a 32-coreIntel Nehalem, and a 128-processor Cray XMT) on a representative set of real-world and synthetic graphs. To the best of our knowledge, this is the first study of augmentation-based parallel algorithms for bipartite cardinality matching that demonstrates good speedups on multithreaded shared memory multiprocessors. © 2012 IEEE.
Journal of Computational Physics
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Proposed for publication in IEEE Computer Magazine.
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Proposed for publication in Concurrency and Computation: Practice and Experience.
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Proposed for publication in arxiv.org.
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