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UID:submissions.supercomputing.org_SC22_sess275_rpost180@linklings.com
SUMMARY:Statistical and Causal Analysis of Chimbuko Provenance Database
DESCRIPTION:Posters, Research Posters\n\nStatistical and Causal Analysis o
 f Chimbuko Provenance Database\n\nAjuwon, Teutu Talla, Kemajou-Brown, Kell
 y, Kleese Van Dam\n\nPerformance data are collected to establish how well 
 exascale applications are doing with executing their code or workflow as e
 fficiently as possible. Chimbuko, a tool specifically focused on the analy
 sis of performance data in real time, looks through these data and collect
 s performance anomalies that are detected. These anomalies are saved into 
 the Chimbuko Provenance Database, together with as much contextual informa
 tion as needed. The goal of our work is to perform statistical analysis on
  the Chimbuko Provenance Database by presenting simple visualizations and 
 determining if the information collected for each anomaly is sufficient to
  conduct a causal analysis.  Statistical methods such as Theil’s U correla
 tion analysis, Logistic regression, and K-Prototype clustering were used t
 o identify association between variables. Furthermore, feature selection w
 as conducted with Decision Tree and Random Forest.  We identified associat
 ion between call_stack and several variables, which reveals that call_stac
 k is a very important feature of the dataset.\n\nRegistration Category: Te
 ch Program Reg Pass, Exhibits Reg Pass
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