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Graphics Processing Units (GPUs) are widely used by various applications in abroad variety of fields to accelerate their computation but remain susceptibleto transient hardware faults (soft errors) that can easily compromiseapplication output. By taking advantage of a general purpose GPU applicationhierarchical organization in threads, warps, and cooperative thread arrays, wepropose a methodology that identifies the resilience of threads and aims to mapthreads with the same resilience characteristics to the same warp. This allowsengaging partial replication mechanisms for error detection/correction at thewarp level. By exploring 12 benchmarks (17 kernels) from 4 benchmark suites, weillustrate that threads can be remapped into reliable or unreliable warps withonly 1.63% introduced overhead (on average), and then enable selectiveprotection via replication to those groups of threads that truly need it.Furthermore, we show that thread remapping to different warps does notsacrifice application performance. We show how this remapping facilitates warpreplication for error detection and/or correction and achieves an averagereduction of 20.61% and 27.15% execution cycles, respectively comparing tostandard duplication/triplication.

Enabling Software Resilience in GPGPU Applications via Partial Thread Protection