J. Autran, S. Uznanski, S. Martinie, P. Roche, G. Gasiot, D. Munteanu
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引用次数: 7
摘要
本工作报告了收集-扩散模型的CUDA实现，用于计算图形处理单元(GPU)上大面积和/或复杂电路的软错误率(SER)。我们详细介绍了算法中引入的时间并行化，以加速一个数量级的SER计算。在NVIDIA Tesla C1060 GPU卡上对代码性能进行了评估，计算了α粒子源辐照下65nm SRAM电路的SER。
A GPU/CUDA implementation of the collection-diffusion model to compute SER of large area and complex circuits
This work reports the CUDA implementation of the collection-diffusion model to compute the soft-error rate (SER) of large area and/or complex circuits on graphics processing units (GPU). We detail the time parallelization introduced in the algorithm to accelerate by one order of magnitude the SER calculation. Code performances are evaluated on a NVIDIA Tesla C1060 GPU card for the calculation of the SER of a 65nm SRAM circuit subjected to an alpha-particle source irradiation.
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