Efficient data race detection for distributed memory parallel programs

In this paper we present a precise data race detection tech-nique for distributed memory parallel programs. Our tech-nique, which we call Active Testing, builds on our previous work on race detection for shared memory Java and C pro-grams and it handles programs written using shared memory approaches as well as bulk communication. Active testing works in two phases: in the first phase, it performs an im-precise dynamic analysis of an execution of the program and finds potential data races that could happen if the program is executed with a different thread schedule. In the second phase, active testing re-executes the program by actively controlling the thread schedule so that the data races re-ported in the first phase can be confirmed. A key highlight of our technique is that it can scalably handle distributed programs with bulk communication and single- and split-phase barriers. Another key feature of our technique is that it is precise—a data race confirmed by active testing is an actual data race present in the program; however, being a testing approach, our technique can miss actual data races. We implement the framework for the UPC programming lan-guage and demonstrate scalability up to a thousand cores for programs with both fine-grained and bulk (MPI style) communication. The tool confirms previously known bugs and uncovers several unknown ones. Our extensions cap-ture constructs proposed in several modern programming languages for High Performance Computing, most notably non-blocking barriers and collectives. 1