scholarly journals lo2s — Multi-core System and Application Performance Analysis for Linux

2017 ◽  
Author(s):  
Thomas Ilsche ◽  
Robert Schone ◽  
Mario Bielert ◽  
Andreas Gocht ◽  
Daniel Hackenberg
Keyword(s):  
Performance Analysis ◽  
Core System ◽  
Application Performance

Design and implementation of Performance Analysis Unit (PAU) for AXI-based multi-core System on Chip (SOC)

2010 ◽  
Vol 34 (2-4) ◽  
pp. 102-116 ◽  
Author(s):  
Hyun-min Kyung ◽  
Gi-ho Park ◽  
Jong Wook Kwak ◽  
Tae-jin Kim ◽  
Sung-Bae Park
Keyword(s):  
Performance Analysis ◽  
System On Chip ◽  
Core System ◽  
Design And Implementation ◽  
On Chip

A Scalable Approach to MPI Application Performance Analysis

2005 ◽  
pp. 309-316 ◽  
Author(s):  
Shirley Moore ◽  
Felix Wolf ◽  
Jack Dongarra ◽  
Sameer Shende ◽  
Allen Malony ◽  
...  
Keyword(s):  
Performance Analysis ◽  
Application Performance

A PERFORMANCE ANALYSIS OF THREE GENERATIONS OF BLUE GENE

2013 ◽  
Vol 23 (04) ◽  
pp. 1340007
Author(s):  
DARREN J. KERBYSON ◽  
KEVIN J. BARKER ◽  
DIEGO S. GALLO ◽  
DONG CHEN ◽  
JOSE R. BRUNHEROTO ◽  
...  
Keyword(s):  
Performance Analysis ◽  
System Integration ◽  
Power Efficiency ◽  
Performance Model ◽  
Peak Performance ◽  
Comparative Performance ◽  
Application Performance ◽  
Successive Generations ◽  
Blue Gene ◽  
System Configurations

IBMs Blue Gene supercomputer architecture has evolved through three successive generations each providing increased levels of power-efficiency and system densities. From the original Blue Gene/L to P to Q, a higher level of integration has enabled higher single-core performance, larger concurrency per compute node, and a higher level of system integration. Although these changes have brought with them a higher overall system peak-performance, no study has examined in detail the evolution of performance across system generations. In this work we make two significant contributions that of providing a comparative performance analysis across Blue Gene generations using a consistent set of tests, and also in providing a validated performance model of the NEK-Bone proxy application from the DOE CESAR Exascale Co-Design Center. The combination of empirical analysis and the predictive capabilities of the NEK-Bone performance model enable us to not only directly compare measured performance but also allow for a comparison of system configurations that cannot currently be measured. We provide insights into how the changing architectural performance characteristics of Blue Gene have impacted on the application performance, as well as providing insight into what future systems may be able to achieve.

scholarly journals A Cross-Platform Infrastructure for Scalable Runtime Application Performance Analysis

2005 ◽  
Author(s):  
Jack Dongarra ◽  
Shirley Moore ◽  
Jeffrey Hollingsworth Bart Miller ◽  
Tracy Rafferty
Keyword(s):  
Performance Analysis ◽  
Application Performance ◽  
Cross Platform
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