Production high-performance computing (HPC) systems are adopting and integrating GPUs
into their design to accommodate artificial intelligence (AI), machine learning, and data …

While Moore's Law has continued to provide smaller semiconductor devices, the effective
end of uniprocessor performance scaling has (finally) instigated mainstream computing to …

As the demand for computing power from High-Performance Computing (HPC) and Deep
Learning (DL) applications increase, there is a growing trend of equipping modern exascale …

General purpose GPUs are now ubiquitous in high-end supercomputing. All but one (the
Japanese Fugaku system, which is based on ARM processors) of the announced (pre-) …

GPUs have become immensely important computational units on embedded and mobile
devices. However, GPGPU developers are often not able to exploit the compute power …

With the unprecedented development of compute capability and extension of memory
bandwidth on modern GPUs, parallel communication and synchronization soon becomes a …

The demands of high-performance computing (HPC) and machine learning (ML) workloads
have resulted in the rapid architectural evolution of GPUs over the last decade. The growing …

To port applications to GPUs, developers need to express computational tasks as highly
parallel executions with tens of thousands of threads to fill the GPU's compute resources …

GPUs are widely used to accelerate data-parallel applications. However, while the GPU
processing capability is enhanced in each generation, the CPU computing power is also …

GPU accelerators have successfully established themselves in modern HPC clusters due to
their high performance and energy efficiency. To increase the GPU computational power in …