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A PERFORMANCE EVALUATION OF THE NEHALEM QUAD-CORE PROCESSOR FOR SCIENTIFIC COMPUTING

KEVIN J. BARKER

Performance and Architecture Lab (PAL), Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA

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KEI DAVIS

Performance and Architecture Lab (PAL), Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA

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ADOLFY HOISIE

Performance and Architecture Lab (PAL), Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA

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DARREN J. KERBYSON

Performance and Architecture Lab (PAL), Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA

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MIKE LANG

Performance and Architecture Lab (PAL), Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA

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SCOTT PAKIN

Performance and Architecture Lab (PAL), Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA

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, and

JOSE CARLOS SANCHO

Performance and Architecture Lab (PAL), Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA

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https://doi.org/10.1142/S012962640800351XCited by:33 (Source: Crossref)

Abstract

In this work we present an initial performance evaluation of Intel's latest, second-generation quad-core processor, Nehalem, and provide a comparison to first-generation AMD and Intel quad-core processors Barcelona and Tigerton. Nehalem is the first Intel processor to implement a NUMA architecture incorporating QuickPath Interconnect for interconnecting processors within a node, and the first to incorporate an integrated memory controller. We evaluate the suitability of these processors in quad-socket compute nodes as building blocks for large-scale scientific computing clusters. Our analysis of intra-processor and intra-node scalability of microbenchmarks, and a range of large-scale scientific applications, indicates that quad-core processors can deliver an improvement in performance of up to 4x over a single core depending on the workload being processed. However, scalability can be less when considering a full node. We show that Nehalem outperforms Barcelona on memory-intensive codes by a factor of two for a Nehalem node with 8 cores and a Barcelona node containing 16 cores. Further optimizations are possible with Nehalem, including the use of Simultaneous Multithreading, which improves the performance of some applications by up to 50%.

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