Efficient and Scalable Event Tracing

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