This paper proposed a topology-based GPU scheduling framework. The framework is based
on the traditional kubernetes GPU scheduling algorithm. In existing algorithms, GPU can …

Modern cluster management systems, such as Kubernetes, support heterogeneous
workloads and resources. However, existing resource schedulers in these systems do not …

Modern GPUs are gradually used by more and more cluster computing systems as the high
performance computing units due to their outstanding computational power, whereas …

In clouds and data centers, GPU servers which consist of multiple GPUs are widely
deployed. Current state-of-the-art GPU scheduling algorithm are" static" in assigning …

GPUs have been widely used in modern datacenters to accelerate emerging services such
as Graph Processing, Intelligent Personal Assistant (IPA), and Deep Learning. However …

As throughput-oriented accelerators, GPUs provide tremendous processing power by
running a massive number of threads in parallel. However, exploiting high degrees of thread …

Efficient GPU scheduling is the key to minimizing the execution time of the Deep Learning
(DL) training workloads. DL training system schedulers typically allocate a fixed number of …

Graphics Processing Unit (GPU) provides acceleration services to many applications, such
as AI, games, media transcoding, etc. Virtualization on GPU is an enabling technology which …

Heterogeneous processors with architecturally different devices (CPU and GPU) integrated
on the same die provide good performance and energy efficiency for wide range of …

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 …