Section: New Results

Loop-based Modeling of Parallel Communication Traces

Parallel communication traces are traces of the various actions performed by parallel programs (typically written using MPI or some such library). The traces usually contain actions like message sending and receiving, and entering and exiting collective operations. The goal of this project is to build a model of the parallel program from the traces of the various processes that form the program. Consolidating on our previous work on sequential traces, we have developed an algorithm that takes the traces of the individual processes and merges them into a global model.

The main characteristics of our algorithm is that the result takes the form of loops enclosing various parallel constructs and communication actions. The driving goal of this work is to use the model for various analyzes, mainly to draw qualitative conclusions on the program (like the affinity of the various processes involved), but also to extract quantitative information (like communication matrices). A long term goal is to use the parallel loops to suggest program optimizations.

As of today, our algorithm has been evaluated on several applications. The most obvious is trace compression, with spectacular results because of the underlying loop-nest model (as was already the case for our sequential trace analysis algorithm). Another application is replay, where the program's (actual, i.e., traced) behavior can be simulated on a different parallel architecture. The last application is to build a lightweight model from a subset of trace data, and use the model to index into potentially massive quantitative data associated to the various events.

It turns out that it is difficult to publish such algorithms without evaluating them in “realistic” settings, on applications running on massively parallel hardware, something we don't have easy access to. Also, there are currently a few algorithms that provide similar solutions to practitioners, in a way that we think are fundamentally inferior to our proposition but that seem to be good enough for their current use. Waiting for better opportunities to illustrate the power of our method, we have published a research report summarizing our work [26] .