Contribution to a conference proceedings/Contribution to a book

FZJ-2016-06939

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Using Directed Variance to Identify Meaningful Views in Call-path Performance Profiles

Vierjahn, T. (Corresponding author)RWTH* ; Hermanns, M.-A.FZJ* ; Mohr, B.FZJ* ; Müller, M. S.RWTH* ; Kuhlen, T. W.RWTH* ; Hentschel, B.RWTH*

2016
IEEE Press Piscataway, NJ, USA
ISBN: 978-1-5090-5226-4

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Please use a persistent id in citations: doi:10.1109/vpa.2016.7

Abstract: Understanding the performance behaviour of massively parallel high-performance computing (HPC) applications based on call-path performance profiles is a time-consuming task. In this paper, we introduce the concept of directed variance in order to help analysts find performance bottlenecks in massive performance data and in the end optimize the application. According to HPC experts' requirements, our technique automatically detects severe parts in the data that expose large variation in an application's performance behaviour across system resources. Previously known variations are effectively filtered out. Analysts are thus guided through a reduced search space towards regions of interest for detailed examination in a 3D visualization. We demonstrate the effectiveness of our approach using performance data of common benchmark codes as well as from actively developed production codes.

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Contributing Institute(s):

1. Jülich Supercomputing Center (JSC)
2. JARA - HPC (JARA-HPC)

Research Program(s):

1. 511 - Computational Science and Mathematical Methods (POF3-511) (POF3-511)
2. Scalable Performance Analysis of Large-Scale Parallel Applications (jzam11_20091101) (jzam11_20091101)
3. ATMLPP - ATML Parallel Performance (ATMLPP) (ATMLPP)

Appears in the scientific report 2016

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