Techniques for Automated Performance Analysis

Marcus, Ryan C.¹ 

[1]Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

关键词: COMPUTER SCIENCE;   

DOI  :  10.2172/1154980
RP-ID  :  LA-UR-14-26577
PID  :  OSTI ID: 1154980

学科分类：数学（综合）

美国|英语

来源: SciTech Connect

【 摘 要 】

The performance of a particular HPC code depends on a multitude of variables, including compiler selection, optimization flags, OpenMP pool size, file system load, memory usage, MPI configuration, etc. As a result of this complexity, current predictive models have limited applicability, especially at scale. We present a formulation of scientific codes, nodes, and clusters that reduces complex performance analysis to well-known mathematical techniques. Building accurate predictive models and enhancing our understanding of scientific codes at scale is an important step towards exascale computing.