Avoiding Soft Error-Induced Illegal Memory Accesses in GPU with Inter-Thread Communication

A soft error caused by terrestrial neutrons poses a threat to the reliability of safety-critical systems, such as self-driving applications. These applications, often comprised of neural networks, rely on graphic processing units (GPUs) due to their requirement for massive parallel computation. While neural networks inherently include redundant computation and possess a certain level of error tolerance, detectable unrecoverable errors (DUEs) can be more detrimental than silent data corruption (SDC), as they can result in temporary service unavailability. This study specifically focuses on addressing illegal memory access, a primary cause of DUEs, and proposes a programming method that can detect illegal addresses. In the single instruction, multiple threads (SIMT) scheme, the data address is regularly calculated based on the thread ID, and this regularity is exploited to identify illegal addresses through inter-thread communication. To evaluate the effectiveness of the proposed method, fault injection campaigns were conducted for matrix multiplication, vector addition, and transposition. The experimental results indicate that the proposed method resulted in a reduction of the DUE rate by 17.3%, 86.8%, and 87.1% for these respective operations.