The mechanical properties of metal materials largely depend on their intrinsic internal microstructures. To develop engineering materials with the expected properties, predicting patterns in solidified metals would be indispensable. The phase-field simulation is the most powerful method known to simulate the micro-scale dendritic growth during solidification in a binary alloy. To evaluate the realistic description of solidification, however, phase-field simulation requires computing a large number of complex nonlinear terms over a fine-grained grid. Due to such heavy computational demand, previous work on simulating three-dimensional solidification with phase-field methods was successful only in describing simple shapes. Our new simulation techniques achieved scales unprecedentedly large, sufficient for handling complex dendritic structures required in material science. Our simulations on the GPU-rich TSUBAME 2.0 supercomputer at the Tokyo Institute of Technology have demonstrated good weak scaling and achieved 1.017 PFlops in single precision for our largest configuration, using 4,000 GPUs along with 16,000 CPU cores.

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Abstract-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 kernels in the CUDA SDK and the Rodinia benchmark suite. Our regression model achieves highly accurate estimates for many of the tested kernels, where the average error ratio is 4.7%. However, we also find that it fails to yield accurate estimates for kernels with texture reads because of the lack of performance counters for monitoring texture accesses, resulting in significant underestimation for such kernels.

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