Date of Award
Bachelor of Science (B.S.) in Computer Science and University Honors
High performance computing, Time-series analysis, Databases, Trace analysis
In this work, I demonstrate that a time series database can be utilized to store Open Trace Format 2 (OTF2) file metadata for common trace events efficiently and scalably. This paper examines the efficacy of storing event trace data in a time series database, and investigates associated performance overhead compared to the state of the art method using OTF2 trace files. The sample traces used in this project are generated from a parallel hydrodynamic modeling code, Lulesh, developed at Lawrence Livermore National Laboratory. In my approach, I first cache common event trace metadata in InfluxDB, a contemporary time series database. Next, I compare the runtime performance of various metrics by executing InfluxQL queries on InfluxDB, and using corresponding one-pass algorithms on the OTF2 trace files. My results reflect an exponential performance improvement benefitting the InfluxDB technique.
Dikkala, Rupika, "Efficient and Scalable Event Tracing" (2019). University Honors Theses. Paper 762.