Tutorial: Parallelware Analyzer: Data race detection for GPUs using OpenMP and OpenACC

Dr. Manuel Arenaz & Javier Novo Rodríguez, Appentra Solutions

The development and maintenance of parallel software is far more complex than sequential software. Bugs related to parallelism are difficult to find and fix because a buggy parallel code might run correctly 99% of the time and fail just the remaining 1%. This is also true for Graphical Processing Units (GPUs). In order to take advantage of the performance promised by GPUs, developers must write bug-free parallel code using the C/C++ and Fortran programming languages. This talk presents a new innovative approach to parallel programming based on two pillars: first, an open catalog of rules and recommendations that leverage parallel programming best practices; and second, the automation of quality assurance for parallel programming through new static code analysis tools specializing in parallelism that integrate seamlessly into professional software development tools. We also present new Parallelware Analyzer capabilities for data race detection for GPUs using OpenMP and OpenACC.