Implications of Memory Performance for Highly Efficient Supercomputing of Scientific Applications

This paper examines the memory performance of the vector-parallel and scalar-parallel computing platforms across five applications of three scientific areas; electromagnetic analysis, CFD/heat analysis, and seismology. Our evaluation results show that the vector platforms can achieve the high computational efficiency and hence significantly outperform the scalar platforms in the areas of these applications. We did exhaustive experiments and quantitatively evaluated representative scalar and vector platforms using real applications from the viewpoint of the system designers and developers. These results demonstrate that the ratio of memory bandwidth to floating-point operation rate needs to reach 4-bytes/flop to preserve the computational performance with hiding the memory access latencies by pipelined vector operations in the vector platforms. We also confirm that the enough number of memory banks to handle stride memory accesses leads to an increase in the execution efficiency. On the scalar platforms, the cache hit rate needs to be almost 100% to achieve the high computational efficiency.