GPU-Qin: A Methodology for Evaluating the Error Resilience of GPGPU Applications

While Graphics Processing Units (GPUs) have gained wide adoption as accelerators for General-Purpose applications (GPGPU), the end-to-end reliability implications of their use have not been quantified. Fault injection is a widely used method for evaluating the reliability of applications. However, building a fault injector for GPGPU applications is challenging due to their massive parallelism, which makes it difficult to achieve representativeness while being time-efficient. This paper makes three key contributions. First, it presents the design of a fault-injection methodology to evaluate end-to-end reliability properties of application kernels running on GPUs.

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University of Bristol