Containerization technology offers lightweight OS-level virtualization, and enables
portability, reproducibility, and flexibility by packing applications with low performance …

To accelerate the training of Deep Learning (DL) models, clusters of machines equipped
with hardware accelerators such as GPUs are leveraged to reduce execution time. State-of …

With the fast growing trend in deep learning driven AI services over the past decade, deep
learning, especially the resource-intensive and time-consuming training jobs, have become …

This paper tackles the challenges of obtaining more efficient data center computing while
maintaining low latency, low cost, programmability, and the potential for workload …

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 …

Data centers are the infrastructure that underpins modern distributed service-oriented
systems. They are complex systems-of-systems, with many interacting elements, that …

Recent advances in hardware, such as systems with multiple GPUs and their availability in
the cloud, are enabling deep learning in various domains including health care …

The compute capability of edge devices is expanding owing to the wide adoption of edge
computing for various application scenarios and specialized hardware explicitly developed …

Contemporary GPUs are widely used for throughput-oriented data-parallel workloads and
increasingly are being considered for latency-sensitive applications in datacenters …

Field-Programmable Gate Arrays (FPGAs) are more energy efficient and cost effective than
CPUs for a wide variety of datacenter applications. Yet, for latency-sensitive and bursty …