We address the compiler correctness problem for many-core systems through novel
applications of fuzz testing to OpenCL compilers. Focusing on two methods from prior work …

Writing high-performance GPU implementations of graph algorithms can be challenging. In
this paper, we argue that three optimizations called throughput optimizations are key to high …

GPUs are progressively being integrated into modern society, playing a pivotal role in
Artificial Intelligence and High-Performance Computing. Programmers need a deep …

We present a technique for the formal verification of GPU kernels, addressing two classes of
correctness properties: data races and barrier divergence. Our approach is founded on a …

As GPUs have become an integral part of nearly every pro-cessor, GPU programming has
become increasingly popular. GPU programming requires a combination of extreme levels …

Concurrency errors, such as data races, make device drivers notoriously hard to develop
and debug without automated tool support. We present Whoop, a new automated approach …

GPU programming models enable and encourage massively parallel programming with
over a million threads, requiring extreme parallelism to achieve good performance. Massive …

GPUs offer parallelism as a commodity, but they are difficult to program correctly. Static
analyzers that guarantee data-race freedom (DRF) are essential to help programmers …

Newer use cases of GPU (Graphics Processing Unit) computing, eg, graph analytics, look
less like traditional bulk-synchronous GPU programs. To cater to the needs of emerging …

Previous efforts to formally verify code written for GPUs have focused solely on kernels
written within the traditional data-parallel GPU programming model. No previous work has …