John B. Drake

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We describe the design of a parallel global atmospheric circulation model, PCCM2. This parallel model is functionally equivalent to the National Center for Atmospheric Research's Community Climate Model, CCM2, but is struc-tured to exploit distributed memory multicomputers. PCCM2 incorporates parallel spectral transform, semi-Lagrangian transport, and load(More)
We present a scalable parallel Strassen’s matrix multiplication algorithm for distributed-memory, messagepassing computers. Strassen’s algorithm to multiply two N x N matrices reduces the asymptotic operation count from O(N3) of the traditional algorithm to O(N2.*1). In a sequential implementation the Strassen’s algorithm offers better performance even for(More)
Generating credible climate change and extremes projections remains a high-priority challenge, especially since recent observed emissions are above the worst-case scenario. Bias and uncertainty analyses of ensemble simulations from a global earth systems model show increased warming and more intense heat waves combined with greater uncertainty and large(More)
The Community Climate System Model (CCSM) is a computer model for simulating the Earth’s climate. The CCSM is built from four individual component models for the atmosphere, ocean, land surface, and sea ice. The notion of a physical/dynamical component of the climate system translates directly to the software component structure. Software design of the CCSM(More)
The Parallel Community Climate Model (PCCM) is a message-passing parallelization of version 2.1 of the Community Climate Model (CCM) developed by researchers at Argonne and Oak Ridge National Laboratories and at the National Center for Atmospheric Research in the early to mid 1990s. In preparation for use in the Department of Energy’s Parallel Climate Model(More)
Community models for global climate research, such as the Community Atmospheric Model, must perform well on a variety of computing systems. Supporting diverse research interests, these computationally demanding models must be efficient for a range of problem sizes and processor counts. In this paper we describe the data structures and associated(More)