Publications

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Power and energy concerns are motivating chip manufacturers to consider future hybrid-core processor designs that combine a small number of traditional cores optimized for single-thread performance with a large number of simpler cores optimized for throughput performance. This trend is likely to impact the way compute resources for network protocol processing functions are allocated and managed. In particular, the performance of MPI match processing is critical to achieving high message throughput. In this paper, we analyze the ability of simple and more complex cores to perform MPI matching operations for various scenarios in order to gain insight into how MPI implementations for future hybrid-core processors should be designed.

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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.

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The importance of memory performance and capacity is a growing concern for high performance computing laboratories around the world. It has long been recognized that improvements in processor speed exceed the rate of improvement in dynamic random access memory speed and, as a result, memory access times can be the limiting factor in high performance scientific codes. The use of multi-core processors exacerbates this problem with the rapid growth in the number of cores not being matched by similar improvements in memory capacity, increasing the likelihood of memory contention. In this paper, we present WMTools , a lightweight memory tracing tool and analysis framework for parallel codes, which is able to identify peak memory usage and also analyse per-function memory use over time. An evaluation of WMTools , in terms of its effectiveness and also its overheads, is performed using nine established scientific applications/benchmark codes representing a variety of programming languages and scientific domains. We also show how WMTools can be used to automatically generate a parameterized memory model for one of these applications, a two-dimensional non-linear magnetohydrodynamics application, Lare2D . Through the memory model we are able to identify an unexpected growth term which becomes dominant at scale. With a refined model we are able to predict memory consumption with under 7% error.

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