This document proposes a multi-site strategy for creating a new class of computing capability for the U.S. by undertaking the research and development necessary to build supercomputers optimized for science in partnership with the American computer industry.

Lawrence Berkeley National Laboratory (Berkeley Lab) proposes to create a National Facility for Advanced Computational Science (NFACS) and to establish a new partnership between the American computer industry and a national consortium of laboratories, universities, and computing facilities. NFACS will provide leadership-class scientific computing capability to scientists and engineers nationwide, independent of their institutional affiliation or source of funding. This partnership will bring into existence a new class of computational capability in the United States that is optimal for science and will create a sustainable path towards petaflops performance.

Over the past several years, computational scientists have observed a frustrating trend of stagnating application performance despite dramatic increases in peak performance of high performance computers. In 2002, researchers at Lawrence Berkeley National Laboratory, Argonne National Laboratory, and IBM proposed a new process to reverse this situation [1]. This strategy is based on new types of development partnerships with computer vendors based on the concept of science-driven computer system design. This strategy will engage applications scientists well before an architecture is available for commercialization. The process is already producing results, and has further potential for dramatically improving system efficiency. This paper documents the progress to date and the potential for future benefits. An example of this process is discussed, using IBM Power architecture with a computer architecture design that can lead to a sustained performance of 50 to 100 Tflo p/s on a broad spectrum of applications in 2006 for a reasonable cost. This partnership will establish a collaborative approach to modifying computer architecture to enable heretofore unrealized achievements in computer capability-limited fields such as nanoscience, combustion modeling, fusion, climate modeling, and astrophysics.