Abstract

OpenMP has gained wide popularity as an API for parallel programming on shared memory and distributed shared memory platforms. It is also a promising candidate to exploit the emerging multicore, multithreaded processors. In addition, there is an increasing trend to combine OpenMP with MPI to take full advantage of mainstream supercomputers consisting of clustered SMPs. All of these require that attention be paid to the quality of the compiler’s translation of OpenMP and the flexibility of runtime support. Many compilers and runtime libraries have an internal cost model that helps evaluate compiler transformations, guides adaptive runtime systems, and helps achieve load balancing. But existing models are not sufficient to support OpenMP, especially on new platforms. In this paper, we present our experience adapting the cost models in OpenUH, a branch of Open64, to estimate the execution cycles of parallel OpenMP regions using knowledge of both software and hardware. Our OpenMP cost model reuses major components from Open64, along with extensions to consider more OpenMP details. Preliminary evaluations of the model are presented using kernel benchmarks. The challenges and possible extensions for modeling OpenMP on multicore platforms are also discussed.

Citations