An Efficient and Experimentally Tuned Software-Based Hardening Strategy for Matrix Multiplication on GPUs

摘要

Neutron radiation experiment results on matrix multiplication on graphic processing units (GPUs) show that multiple errors are detected at the output in more than 50% of the cases. In the presence of multiple errors, the available hardening strategies may become ineffective or inefficient. Analyzing radiation-induced error distributions, we developed an optimized and experimentally tuned software-based hardening strategy for GPUs. With fault-injection simulations, we compare the performance and correcting capabilities of the proposed technique with the available ones.