Fault Tolerant One-Sided Matrix Decompositions on Heterogeneous Systems with GPUs

Authors: Jieyang Chen (University of California, Riverside), Hongbo Li (University of California, Riverside), Sihuan Li (University of California, Riverside), Xin Liang (University of California, Riverside), Panruo Wu (University of Houston), Dingwen Tao (University of Alabama), Kaiming Ouyang (University of California, Riverside), Yuanlai Liu (University of California, Riverside), Kai Zhao (University of California, Riverside), Qiang Guan (Kent State University), Zizhong Chen (University of California, Riverside)

Abstract: Current algorithm-based fault tolerance (ABFT) approach for one-sided matrix decomposition on heterogeneous systems with GPUs have following limitations: (1) they do not provide sufficient protection as most of them only maintain checksum in one dimension; (2) their checking scheme is not efficient due to redundant checksum verifications; (3) they fail to protect PCIe communication; (4) the checksum calculation based on a special type of matrix multiplication is far from efficient. By overcoming the above limitations, we design an efficient ABFT approach providing stronger protection for one-sided matrix decomposition methods on heterogeneous systems. First, we provide full matrix protection by using checksums in two dimensions. Second, our checking scheme is more efficient by prioritizing the checksum verification according to the sensitivity of matrix operations to soft errors. Third, we protect PCIe communication by reordering checksum verifications and decomposition steps. Fourth, we accelerate the checksum calculation by 1.7x via better utilizing GPUs.




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