Scaling Irregular Parallel Codes with Minimal Programming Effort

The long foreseen goal of parallel programming models is to scale parallel code without significant programming effort. Irregular parallel applications are a particularly challenging application domain for parallel programming models, since they require domain specific data distribution and load balancing algorithms. From a performance perspective, shared-memory models still fall short of scaling as well as message-passing models in irregular applications, although they require less coding effort. We present a simple runtime methodology for scaling irregular applications parallelized with the standard OpenMP interface. We claim that our parallelization methodology requires the minimum amount of effort from the programmer and prove experimentally that it is able to scale two highly irregular codes as well as MPI, with an order of magnitude less programming effort. This is probably the first time such a result is obtained from OpenMP, more so, by keeping the OpenMP API intact.