OpenMP is an emerging industry standard for shared memory architectures. While OpenMP has advantages on its ease of use and incremental programming, message passing is today still the most widely-used programming model for distributed memory architectures. How to effectively extend OpenMP to distributed memory architectures has been a hot spot. This paper proposes an OpenMP system, called KLCoMP, for distributed memory architectures. Based on the "partially replicating shared arrays" memory model, we propose an algorithm for shared array recognition based on the inter-procedural analysis, optimization technique based on the producer/consumer relationship, and communication generation technique for nonlinear references. We evaluate the performance on nine benchmarks which cover computational fluid dynamics, integer sorting, molecular dynamics, earthquake simulation, and computational chemistry. The average scalability achieved by KLCoMP version is close to that achieved by MPI version. We compare the performance of our translated programs with that of versions generated for Omni+SCASH, LLCoMP, and OpenMP(Purdue), and find that parallel applications (especially, irregular applications) translated by KLCoMP can achieve more effective performance than other versions.

The task parallelization is proposed in the OpenMP3.0 specification, which aims to resolve the irregular parallel computing problems. This paper presents the novel irregular application task model on Cell BE processors, which could reduce the difficulty of irregular applied parallel programming. In this Model, the two kinds of optimization techniques for maximum task numbers and maximum recursive layer are realized to avoid producing a large amount of fine-grained tasks and improve effectively the program performance of parallel execution. The experimental results show that the speedup of the typical irregular application is increased to 5.3 in a single Cell processor with six SPEs.