Shengzhao Wu

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We present the design and evaluation of a scalable tridiagonal solver targeted for GPU architectures. We observed that two distinct steps are required to solve a large tridiagonal system in parallel: 1) breaking down a problem into multiple sub problems each of which is independent of other, and 2) solving the sub problems using an efficient algorithm. We(More)
Many-core accelerators, e.g. GPUs, are widely used for accelerating general-purpose compute kernels. With the SIMT execution model, GPUs can hide memory latency through massive multithreading for many regular applications. To support more applications with irregular memory access pattern, cache hierarchy is introduced to GPU architecture to capture input(More)
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