Concurrency is pervasive and perplexing, particularly on graphics processing units (GPUs).
Current specifications of languages and hardware are inconclusive; thus programmers often …

Commodity heterogeneous systems (eg, integrated CPUs and GPUs), now support a
unified, shared memory address space for all components. Because the latency of global …

As GPUs have become increasingly general purpose, applications with more general
sharing patterns and fine-grained synchronization have started to emerge. Unfortunately …

An unambiguous and easy-to-understand memory consistency model is crucial for ensuring
correct synchronization and guiding future design of heterogeneous systems. In a widely …

With the skyrocketing advances of process technology, the increased need to process huge
amount of data, and the pivotal need for power efficiency, the usage of Graphics Processing …

Hashing is one of the most fundamental operations that provides a means for a program to
obtain fast access to large amounts of data. Despite the emergence of GPUs as many …

Traditionally GPUs focused on streaming, data-parallel applications, with little data reuse or
sharing and coarse-grained synchronization. However, the rise of general-purpose GPU …

Even the careful GPU programmer can inadvertently introduce data races while writing and
optimizing code. Currently available GPU race checking methods fall short either in terms of …

Mutual exclusion algorithms are at the heart of concurrent/parallel and distributed systems. It
is well known that such algorithms are very difficult to analyze, and in the literature, different …

Memory models allow reasoning about the correctness of multithreaded programs.
Constructing and using such models is facilitated by simulators that reveal which behaviours …