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 …

Despite the conceptual simplicity of sequential consistency (SC), the semantics of SC atomic
operations and fences in the C11 and OpenCL memory models is subtle, leading to …

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 …

We present the systematic design of a testing environment that uses stressing and fuzzing to
reveal errors in GPU applications that arise due to weak memory effects. We evaluate our …

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 …

Despite the growing popularity of GPGPU programming, there is not yet a portable and
formally-specified barrier that one can use to synchronise across workgroups. Moreover, the …

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

Graphic processing units (GPUs) are routinely used for general purpose computations to
improve performance. To achieve the sought performance gains, care must be invested in …

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 …