We present a technique for performance analysis that helps users understand the communication behavior of their message passing applications. Our method automatically classifies individual communication operations and it reveals the cause of communication inefficiencies in the application. This classification allows the developer to focus quickly on the culprits of truly inefficient behavior, rather than manually foraging through massive amounts of performance data. Specifically, we trace the message operations of MPI applications and then classify each individual communication event using decision tree classification, a supervised learning technique. We train our decision tree using rnicrobenchmarks that demonstrate both efficient and inefficient cotnmunication. Since our technique adapts to the target system's configuration through these microbenchmarks, we can simultaneously automate the performance analysis process and improve classification accuracy. Our experiments on four applications demonstrate that our technique can improve the accuracy of performance analysis, and dramatically reduce the amount of data that users must encounter.
Vetter automatically classifies MPI communication inefficiencies such as late receivers and senders using decision tree classification, a machine learning approach REF .
Performance analysis of distributed applications using automatic classification of communication inefficiencies
ICS '00
Computer Science