Matrix computing is one of the work that GPUs are good at. NVIDIA provides a linear algebra library cuBLAS
in CUDA for the calculations related with matrices and vectors. However
GPU is vulnerable to the problem of bit inversion due to electromagnetic interference or cosmic rays
resulting in silent data corruption (SDC) errors. In order to resolve this problem
a general CUDA library function with fault tolerance for General Matrix Multiplication (GEMM) is implemented by using the algorithm-based fault-tolerant method (ABFT). The principle and implementation of the algorithm
as well as the judging mechanism in the process of error correction and detection are fully discussed.The GEMM library function with fault tolerance is evaluated on two embedded GPU platforms
and its error correction and error detection capabilities are consistent with expectations
and the additional performance overhead can be controlled within 50%. It is proved that this GEMM function with fault-tolerant can well perform detection and correction of the GEMM computing with lower performance overhead. In some result-key high performance computing applications
this function has more practical application value.
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references
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