  This paper presents a tool for repairing errors in GPU kernels written in
CUDA or OpenCL due to data races and barrier divergence. Our novel extension to
prior work can also remove barriers that are deemed unnecessary for
correctness. We implement these ideas in our tool called GPURepair, which uses
GPUVerify as the verification oracle for GPU kernels. We also extend GPUVerify
to support CUDA Cooperative Groups, allowing GPURepair to perform inter-block
synchronization for CUDA kernels. To the best of our knowledge, GPURepair is
the only tool that can propose a fix for intra-block data races and barrier
divergence errors for both CUDA and OpenCL kernels and the only tool that fixes
inter-block data races for CUDA kernels. We perform extensive experiments on
about 750 kernels and provide a comparison with prior work. We demonstrate the
superiority of GPURepair through its capability to fix more kernels and its
unique ability to remove redundant barriers and handle inter-block data races.
