GPUs are critical for maximizing the throughput-per-Watt of deep neural network (DNN)
applications. However, DNN applications often underutilize GPUs, even when using large …

This paper explores the possibility of efficiently executing a single application using
multicores simultaneously with multiple GPU accelerators under a parallel task …

Graph Neural Networks (GNN) have evolved as powerful models for graph representation
learning. Many works have been proposed to support GNN training efficiently on GPU …

Rapid advances in the performance and programmability of graphics accelerators have
made GPU computing a compelling solution for a wide variety of application domains …

Managing GPGPU resources in cloud systems is challenging as workloads with various
resource usage patterns coexist. To determine the co-location of workloads, previous …

As the de facto high-throughput accelerators, graphics processing units (G PU s) are now
used in a wide spec-trum of fields, including artificial intelligence, high performance …

Accelerating processors can often be more cost and energy effective for a wide range of
data-parallel computing problems than general-purpose processors. For graphics processor …

Recently, GPU expands application domains from graphic processing to various kinds of
parallel workloads. However, current GPU systems focus on the maximization of each …

The proliferation of machine learning applications has promoted both CUDA Cores and
Tensor Cores' integration to meet their acceleration demands. While studies have shown …

Nowadays, Graphics Processing Unit (GPU), as a kind of massive parallel processor, has
been widely used in general purposed computing tasks. Although there have been mature …