Today current era of scientific computing and computational theory involves high exhaustive data computation, shifted the trend of data processing from conventional processing towards parallel processing by incorporating multiple processing hardware. Parallel hardware design can employ array processors, pipelined system which can be further extended to scalar and super scalar pipelined systems. Other hardware designs proposed, is based upon multiprocessors or they may be designed as distributed parallel cluster systems. In this paper, multi-computers are the basic hardware for cluster design over the local area network covering analysis of matrix multiplication with strassen's algorithm. The estimated results are then compared with traditional matrix multiplication algorithm. Srassen's multiplication approach reduces one multiplication out of eight by computing arithmetic additions/subtractions for each 2×2 matrix. High performance can be achieved as the idea is extended over to multi-computer cluster for large sized matrices. This work covers analysis of Strassen's ability of divide and conquers[5] to run in parallel by decomposing matrix size over cluster machines covering data parallel aspects with SIMD based computational model [4], where each cluster machine performs its own recursive divide and conquer approach as defined by strassen's methodology[9][10] to obtain partitioned matrix multiplication. Finally, the detailed distributed experiment along with connectivity interface and implementation will be discussed.

Client-server Tcp/ip Sockets Matrix Multiplication Data Parallel Aspects Space Sharing Policies