Data Race Detection for Parallel Programs Using a Virtual Platform

Abstract: Data races are highly destructive bugs found in concurrent programs. Because of unordered thread interleavings, data races can randomly appear and disappear during the debugging process which makes them difficult to find and reproduce. A data race exists when multiple threads or processes concurrently access a shared memory address, with at least one of the accesses being a write. Such a scenario can cause data corruption, memory leaks, crashes, or incorrect execution. It is therefore important that data races are absent from production software. This thesis explores dynamic data race detection in programs running on Ericsson’s System Virtualization Platform (SVP), a SystemC/TLM-2.0-based virtual platform used for running software on simulated hardware. SVP is a bit-accurate simulator of Ericsson Many-Core Architecture (EMCA) hardware, enabling software and hardware to be developed in parallel, as well as providing unique insight into software execution. This latter property of SVP has been utilized to implement SVPracer, a proof-of-concept dynamic data race detector. SVPracer is based on a happens-before algorithm similar to Google’s ThreadSanitizer v2, but is significantly different in implementation as it relies entirely on instrumenting binary code during runtime without requiring code modification during build time. A set of test programs exhibiting various data races were written and compiled for EOS, the operating system (OS) running on EMCA Digital Signal Processors (DSPs). Similar programs were created for Linux using POSIX APIs, to compare SVPracer against ThreadSanitizer v2. Both SVPracer and ThreadSanitizer v2 correctly detect the data races present in the respective test programs. Further work must be done in SVPracer to eliminate some false positive results, caused by missing support for some OS functionality such as semaphores. Still, the present state of SVPracer is sufficient proof that dynamic data race detection is possible using a virtual platform. Future work could involve exploring other data race detection algorithms as well as implementing deadlock/livelock detection in virtual platforms.