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Application Performance on the Tri-Lab Linux Capacity Cluster - TLCC

Application Performance on the Tri-Lab Linux Capacity Cluster - TLCC

Mahesh Rajan, Douglas Doerfler, Courtenay T. Vaughan, Marcus Epperson, Jeff Ogden
Copyright: © 2010 |Volume: 1 |Issue: 2 |Pages: 17
ISSN: 1947-3532|EISSN: 1947-3540|EISBN13: 9781609604349|DOI: 10.4018/jdst.2010040102
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MLA

Rajan, Mahesh, et al. "Application Performance on the Tri-Lab Linux Capacity Cluster - TLCC." IJDST vol.1, no.2 2010: pp.23-39. http://doi.org/10.4018/jdst.2010040102

APA

Rajan, M., Doerfler, D., Vaughan, C. T., Epperson, M., & Ogden, J. (2010). Application Performance on the Tri-Lab Linux Capacity Cluster - TLCC. International Journal of Distributed Systems and Technologies (IJDST), 1(2), 23-39. http://doi.org/10.4018/jdst.2010040102

Chicago

Rajan, Mahesh, et al. "Application Performance on the Tri-Lab Linux Capacity Cluster - TLCC," International Journal of Distributed Systems and Technologies (IJDST) 1, no.2: 23-39. http://doi.org/10.4018/jdst.2010040102

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Abstract

In a recent acquisition by DOE/NNSA several large capacity computing clusters called TLCC have been installed at the DOE labs: SNL, LANL and LLNL. TLCC architecture with ccNUMA, multi-socket, multi-core nodes, and InfiniBand interconnect, is representative of the trend in HPC architectures. This paper examines application performance on TLCC contrasting them with Red Storm/Cray XT4. TLCC and Red Storm share similar AMD processors and memory DIMMs. Red Storm however has single socket nodes and custom interconnect. Micro-benchmarks and performance analysis tools help understand the causes for the observed performance differences. Control of processor and memory affinity on TLCC with the numactl utility is shown to result in significant performance gains and is essential to attenuate the detrimental impact of OS interference and cache-coherency overhead. While previous studies have investigated impact of affinity control mostly in the context of small SMP systems, the focus of this paper is on highly parallel MPI applications.

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