Guaranteeing Quality-of-Service (QoS) of latency-sensitive applications while improving
server utilization through application co-location is important yet challenging in modern …

Exploiting data locality in GPUs is critical to making more efficient use of the existing caches
and the NUMA-based memory hierarchy expected in future GPUs. While modern GPU …

To exploit parallelism and scalability of multiple GPUs in a system, it is critical to place
compute and data together. However, two key techniques that have been used to hide …

Organizing computation as asynchronous tasks with data-driven dependencies is a simple
and efficient model for single-and multi-GPU programs. Sequential Task Flow (STF) is such …

High-level domain-specific languages for array processing on the GPU are increasingly
common, but they typically only run on a single GPU. As computational power is distributed …

Heterogeneous computing systems, eg, those with accelerators than the host CPUs, offer
the accelerated performance for a variety of workloads. However, most parallel …

The computational power and the physical memory size of a single GPU device are often
insufficient for large-scale problems. Using CUDA, the user must explicitly partition such …

The computational power and the physical memory size of a single GPU device are often
insufficient for large-scale problems. Using CUDA, the user must explicitly partition such …

Programmability, performance portability, and resource efficiency have emerged as critical
challenges in harnessing complex and diverse architectures today to obtain high …

A new viewing device is being constructed which will allow a physician to examine multiple
scan sections simultaneously in their proper orientation in all three dimensions. Test images …