Optimizing I/O Forwarding Techniques for Extreme-Scale Event Tracing

Abstract

Programming development tools are a vital com- ponent for understanding the behavior of parallel applica- tions. Event tracing is a principal ingredient to these tools, but new and serious challenges place event tracing at risk on extreme-scale machines. As the quantity of captured events increases with concurrency, the additional data can over- load the parallel file system and perturb the application be- ing observed. In this work we present a solution for event tracing on extreme-scale machines. We enhance an I/O for- warding software layer to aggregate and reorganize log data prior to writing to the storage system, significantly reduc- ing the burden on the underlying file system. Furthermore, we introduce a sophisticated write buffering capability to limit the impact. To validate the approach, we employ the Vampir tracing toolset using these new capabilities. Our re- sults demonstrate that the approach increases the maximum traced application size by a factor of 5x to more than 200,000 processes.