Abstract
Reconfigurable computing system is a class of parallel architecture with the ability of computing in hardware to increase performance, while remaining much of flexibility of a software solution. This architecture is particularly suitable for running regular and compute-intensive tasks, nevertheless, most compute-intensive tasks spend most of their running time in nested loops. Polyhedron model is a powerful tool to give a reasonable transformation on such nested loops. In this paper, a number of issues are addressed towards the goal of optimization of affine loop nests for reconfigurable cell array (RCA), such as approach to make the most use of processing elements (PE) while minimizing the communication volume by loop transformation in polyhedron model, determination of tilling form by the intra-statement dependence analysis and determination of tilling size by the tilling form and the RCA size. Experimental results on a number of kernels demonstrate the effectiveness of the mapping optimization approaches developed. Compared with DFG-based optimization approach, the execution performances of 1-d jacobi and matrix multiplication are improved by 28% and 48.47%. Lastly, the run-time complexity is acceptable for the practical cases.
