Data Access Partitioning for Fine-Grain Parallelism on Multicore Architectures
Source: University of Michigan
The recent design shift towards multicore processors has spawned a significant amount of research in the area of program parallelization. The future abundance of cores on a single chip requires programmer and compiler intervention to increase the amount of parallel work possible. Much of the recent work has fallen into the areas of coarse-grain parallelization: new programming models and different ways to exploit threads and data-level parallelism. This paper focuses on a complementary direction, improving performance through automated fine-grain parallelization. The main difficulty in achieving a performance benefit from fine-grain parallelism is the distribution of data memory accesses across the data caches of each core.