A Performance Evaluation Process Algebra-Based Fluid Approximation Approach to Evaluate Large Scale Content Adaptation Systems

2012 ◽  
Vol 10 (8) ◽  
pp. 1698-1707 ◽  
Author(s):  
Jie Ding ◽  
Xin-Shan Zhu ◽  
Bin Li
Keyword(s):  
Performance Evaluation ◽  
Large Scale ◽  
Process Algebra ◽  
Evaluation Process ◽  
Content Adaptation ◽  
Fluid Approximation ◽  
Approximation Approach ◽  
A Performance

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

2008 ◽  
Vol 18 (04) ◽  
pp. 453-469 ◽  
Author(s):  
KEVIN J. BARKER ◽  
KEI DAVIS ◽  
ADOLFY HOISIE ◽  
DARREN J. KERBYSON ◽  
MIKE LANG ◽  
...  
Keyword(s):  
Performance Evaluation ◽  
Large Scale ◽  
Scientific Computing ◽  
First Generation ◽  
Second Generation ◽  
Building Blocks ◽  
Simultaneous Multithreading ◽  
Scientific Applications ◽  
Memory Controller ◽  
A Performance

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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