The Single Instruction Multiple Data (SIMD) architecture, supported by various high-
performance computing platforms, efficiently utilizes data-level parallelism. The SIMD model …

Programs written for GPUs often contain correctness errors such as races, deadlocks, or
may compute the wrong result. Existing debugging tools often miss these errors because of …

A GPU's computing power lies in its abundant memory bandwidth and massive parallelism.
However, its hardware thread schedulers, despite being able to quickly distribute …

Regular expression pattern matching is the foundation and core engine of many network
functions, such as network intrusion detection, worm detection, traffic analysis, web …

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

Effective parallel programming for GPUs requires careful attention to several factors,
including ensuring coalesced access of data from global memory. There is a need for tools …

In the era of Exascale computing, writing efficient parallel programs is indispensable, and, at
the same time, writing sound parallel programs is very difficult. Specifying parallelism with …

Data provenance, or data lineage, describes the life cycle of data. In scientific workflows on
HPC systems, scientists often seek diverse provenance (eg, origins of data products, usage …

We present an effective technique for crosschecking a C or C++ program against an
accelerated OpenCL version, as well as a technique for detecting data races in OpenCL …

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 …