Capturing Provenance for Runtime Data Analysis in Computational Science and Engineering Applications

被引：4

作者：

Silva, Vitor ^{[1
]}

Souza, Renan ^{[1
,2
]}

Camata, Jose ^{[1
,3
]}

de Oliveira, Daniel ^{[4
]}

Valduriez, Patrick ^{[5
,6
]}

Coutinho, Alvaro L. G. A. ^{[1
]}

Mattoso, Marta ^{[1
]}

机构：

[1] Univ Fed Rio de Janeiro, COPPE, Rio De Janeiro, Brazil

[2] IBM Res, Rio De Janeiro, Brazil

[3] Univ Fed Juiz de Fora, Juiz De Fora, Brazil

[4] Fluminense Fed Univ, Niteroi, RJ, Brazil

[5] INRIA, Montpellier, France

[6] LIRMM, Montpellier, France

来源：

PROVENANCE AND ANNOTATION OF DATA AND PROCESSES, IPAW 2018 | 2018年 / 11017卷

基金：

欧盟地平线“2020”;

关键词：

Provenance; User steering; Computational Science and Engineering; HPC;

D O I：

10.1007/978-3-319-98379-0_15

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

Capturing provenance data for runtime analysis has several challenges in high performance computational science engineering applications. The main issues are avoiding significant overhead in data capture, loading and runtime query support; and coupling provenance capture mechanisms with applications built with highly efficient numerical libraries, and visualization frameworks targeted to high performance environments. This work presents DfA-prov, an approach to capture provenance data and domain data aiming at high performance applications.

引用

页码：183 / 187

页数：5

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[No title captured]

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