Graphics Processing Units (GPUs) exploit large amounts of threadlevel parallelism to
provide high instruction throughput and to efficiently hide long-latency stalls. The resulting …

As graphics processing units (GPUs) are broadly adopted, running multiple applications on
a GPU at the same time is beginning to attract wide attention. Recent proposals on …

Network Function Virtualization (NFV) virtualizes software network functions to offer flexibility
in their design, management and deployment. Although GPUs have demonstrated their …

General Purpose Graphics Processing Units (GPGPUs) are present in most modern
computing platforms. They are also increasingly integrated as a computational resource on …

Human brain network analysis based on machine learning has been paid much attention in
the field of neuroimaging, where the application of convolutional neural network (CNN) is …

GPUs have been widely adopted in data centers to provide acceleration services to many
applications. Sharing a GPU is increasingly important for better processing throughput and …

Machine learning (ML) inference workloads present significantly different challenges than
ML training workloads. Typically, inference workloads are shorter running and under-utilize …

As GPUs are becoming widely deployed in the cloud infrastructure to support different
application domains, the security concerns of GPUs are becoming increasingly important. In …

Modern GPUs require tens of thousands of concurrent threads to fully utilize the massive
amount of processing resources. However, thread concurrency in GPUs can be diminished …

Graphics Processing Units (GPUs) are widely-used accelerators for data-parallel
applications. In many GPU applications, GPU memory bandwidth bottlenecks performance …