Understanding software approaches for GPGPU reliability

Even though graphics processors (GPUs) are becoming increasingly popular for general purpose computing, current (and likely near future) generations of GPUs do not provide hardware support for detecting soft/hard errors in computation logic or memory storage cells since graphics applications are inherently fault tolerant. As a result, if an error occurs in GPUs during program execution, the results could be silently corrupted, which is not acceptable for general purpose computations. To improve the fidelity of general purpose computation on GPUs (GPGPU), we investigate software approaches to perform redundant execution. In particular, we propose and study three different, application-level techniques. The first technique simply executes the GPU kernel program twice, and thus achieves roughly half of the throughput of a non-redundant execution. The next two techniques interleave redundant execution with the original code in different ways to take advantage of the parallelism between th...

• Type: Conference
• Year: 2009
• Where: ASPLOS
• Authors: Martin Dimitrov, Mike Mantor, Huiyang Zhou
• Keywords: ASPLOS 2009, General Purpose Computation, General Purpose Computations, Programming Languages, Redundant Execution