Identifying Inter-task Communication in Shared Memory Programming Models

Modern computers often use multi-core architectures, covering clusters of homogeneous cores for high performance computing, to heterogeneous architectures typically found in embedded systems. To efficiently program such architectures, it is important to be able to partition and map programs onto the cores of the architecture. We believe that communication patterns need to become explicit in the source code to make it easier to analyze and partition parallel programs. Extraction of these patterns are difficult to automate due to limitations in compiler techniques when determining the effects of pointers. In this paper, we propose an OpenMP extension which allows programmers to explicitly declare the pointer based data-sharing between coarse-grain program parts. We present a dependency directive, expressing the input and output relation between program parts and pointers to shared data, as well as a set of runtime operations which are necessary to enforce declarations made by the programmer. The cost and scalability of the runtime operations are evaluated using micro-benchmarks and a benchmark from the NAS parallel benchmark suite. The measurements show that the overhead of the runtime operations is small. In fact, no performance degradation is found when using the runtime operations in the benchmark from the NAS parallel benchmark suite.