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We present a statistical approach for estimating power consumption of GPU kernels. We use the GPU performance counters that are exposed for CUDA applications, and train a linear regression model where performance counters are used as independent variables and power consumption is the dependent variable. For model training and evaluation, we use publicly available CUDA applications, consisting of 49...
We discuss hardware and software aspects of GPGPU, specifically focusing on NVIDIA cards and CUDA, from the viewpoints of parallel computing. The major weak points of GPU against newest supercomputers are identified to be and summarized as only four points: large SIMD vector length, small memory, absence of fast L2 cache, and high register spill penalty. As software concerns, we derive optimal scheduling...
Most GPU performance ldquohypesrdquo have focused around tightly-coupled applications with small memory bandwidth requirements e.g., N-body, but GPUs are also commodity vector machines sporting substantial memory bandwidth; however, effective programming methodologies thereof have been poorly studied. Our new 3-D FFT kernel, written in NVIDIA CUDA, achieves nearly 80 GFLOPS on a top-end GPU, being...
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