Exploiting Parallelism in Linear Algebra Kernels through Dataflow Execution

Abstract

Linear Algebra Kernels have an important role in many petroleum reservoir simulators, extensively used by the industry. The growth in problem size, specially in pre-salt exploration, has caused an increase in execution time of those kernels, thus requiring parallel programming to improve performance and make the simulation viable. On the other hand, exploiting parallelism in systems with an ever increasing number of cores may be an arduous task, as the programmer has to manage threads and care about synchronization issues. Current work on parallel programming models show that Dataflow Execution exploits parallelism in a natural way, allowing the programmer to focus solely on describing dependencies between portions of code. This work consists in implementing parallel Linear Algebra Kernels using the Dataflow model. The Trebuchet Dataflow Virtual Machine and the Sucuri Dataflow Library were used to evaluate the solutions with real inputs from reservoir simulators. Results have been compared with OpenMP and Intel Math Kernel Library and show that coarser-grained tasks are needed to hide the overheads of dataflow runtime environments. Therefore, level 2 and 3 linear algebra operations, such as Vector-Matrix and Matrix-Matrix products, presented the most promising results.