Efficient technique for partitioning and programming linear algebra algorithms on concurrent VLSI architectures

An efficient technique for partitioning and programming linear algebra algorithms on concurrent architectures is described and applied to 2-D wavefront arrays. The mapping of the computational elements (processes) to processors is based on the concept of folding. The mapping pattern on the 2-D full-size mesh of processes is composition of symmetric tiles of size 2√(N)×2√(N), N being the number of processors. The algorithm can be partitioned according to a globally sequential, locally parallel scheme. The code optimisation is performed by programming a few different types of tile, according to the algorithm. When the size of the problem is much larger than the size of the mesh of processors, a linear speed-up is achieved independently of the number of processors. Experimental results are presented for matrix multiplication, LU decomposition and the solution of triangular system equations on 2-D meshes of transputers programmed in Occam