RDS: A cloud-based metaservice for detecting data races in parallel programs

Abstract

Data races are notorious concurrency bugs which can cause severe problems, including random crashes and corrupted execution results. However, existing data race detection tools are still challenging for users to use. It takes a significant amount of effort for users to install, configure and properly use a tool. A single tool often cannot find all the bugs in a program. Requiring users to use multiple tools is often impracticable and not productive because of the differences in tool interfaces and report formats. In this paper, we present a cloud-based, service-oriented design and implementation of a race detection service (RDS)1 to detect data races in parallel programs. RDS integrates multiple data race detection tools into a single cloud-based service via a REST API. It defines a standard JSON format to represent data race detection results, facilitating producing user-friendly reports, aggregating output of multiple tools, as well as being easily processed by other tools. RDS also defines a set of policies for aggregating outputs from multiple tools. RDS significantly simplifies the workflow of using data race detection tools and improves the report quality and productivity of performing race detection for parallel programs. Our evaluation shows that RDS can deliver more accurate results with much less effort from users, when compared with the traditional way of using any individual tools. Using four selected tools and DataRaceBench, RDS improves the Adjusted F-1 scores by 8.8% and 12.6% over the best and the average scores, respectively. For the NAS Parallel Benchmark, RDS improves 35% of the adjusted accuracy compared to the average of the tools. Our work studies a new approach of composing software tools for parallel computing via a service-oriented architecture. The same approach and framework can be used to create metaservice for compilers, performance tools, auto-tuning tools, and so on.