Creating science driven system architectures for large scale science

Application scientists have been frustrated by a trend of stagnating application performance, despite dramatic increases in claimed peak performance of high-performance computing systems. This trend is sometimes referred to as the "divergence problem" and often has been assumed that the ever-increasing gap between theoretical peak and sustained performance was unavoidable. However, recent results from the Earth Simulator (ES) in Japan clearly demonstrate that a close collaboration with a vendor to develop a science-driven architectural solution can produce a system that achieves a significant fraction of peak performance for critical scientific applications. This paper discusses the issues contributing to divergence problem, suggests a new approach to address the problem and documents some early successes for this approach.