GPU technology has been improving at an expedited pace in terms of size and performance,
empowering HPC and AI/ML researchers to advance the scientific discovery process …

As machine learning (ML) becomes more and more popular, datacenter operators use
hardware accelerators such as GPUs to tackle the high computation demand of ML …

With the sustained technological advances in machine learning (ML) and the availability of
massive datasets recently, tech companies are deploying large ML-as-a-Service (MLaaS) …

Graphics Processing Units (GPUs) are increasingly becoming part of HPC clusters.
Nevertheless, cloud computing services and resource management frameworks targeting …

While GPUs have become prominent both in high performance computing and in online or
cloud services, they still appear as explicitly selected'devices' rather than as first class …

As GPUs now contribute the majority of computing power for HPC and data centers,
improving GPU utilization becomes an important research problem. Sharing GPU among …

High performance multi-GPU computing becomes an inevitable trend due to the ever-
increasing demand on computation capability in emerging domains such as deep learning …

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

Modern distributed machine learning (ML) training workloads benefit significantly from
leveraging GPUs. However, significant contention ensues when multiple such workloads are …

GPU-based clusters are increasingly being deployed in HPC environments to accelerate a
variety of scientific applications. Despite their growing popularity, the GPU devices …