FPBOXer: Efficient Input-Generation for Targeting Floating-Point Exceptions in GPU Programs

Numerical programs that generate floating-point exceptions, such as NaNs, are inherently unreliable, as these programs can produce meaningless outputs or affect control flow. When these programs run on GPUs, one cannot rely on hardware traps to handle the exceptions, as most GPUs do not support them. Unfortunately, we must also employ black-box testing for many such GPU programs, as they are supplied as binary code only. While previous work has shown that black-box testing for triggering floating-point exceptions can be approached using Bayesian Optimization, their approach cannot handle programs with more than three inputs. We contribute a new tool, FPBOXer, which pushes up the capabilities of BO to handle over 20 inputs---this makes our contribution capable of handling realistic HPC program functions. In addition to delivering an overall 90x speedup over the previous methods, FPBOXer does not suffer from "self-inflicted" exceptions that are caused by the BO algorithm itself---something that previous tools did. This is achieved through parallel deployments of asynchronous BO searches, which has the beneficial side-effect of improving the GPU-utilization. By using FPBOXer, developers can, for the first time, find exception-causing inputs in realistic HPC programs, as we demonstrate when we apply FPBOXer to NAS, Lampps, CFD, ExaMiniMD, HPCCG, MiniFE, and BDCSVD.