Recent generations of GPUs and their corresponding APIs provide means for sharing
compute resources among multiple applications with greater efficiency than ever. This …

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 …

Graphics processing units (GPUs) are attractive to the generalpurpose computing (GPGPU)
beyond the graphics purpose. Sharing GPUs among such GPGPU applications is a key …

GPUs are being increasingly adopted as compute accelerators in many domains, spanning
environments from mobile systems to cloud computing. These systems are usually running …

Multi-stage user-facing applications on GPUs are widely-used nowa-days, and are often
implemented to be microservices. Prior re-search works are not applicable to ensuring QoS …

As GPUs make headway in the computing landscape spanning mobile platforms,
supercomputers, cloud and virtual desktop platforms, supporting concurrent execution of …

Many modern supercomputers such as ORNL's Summit, LLNL's Sierra, and LBL's upcoming
Perlmutter offer or will offer multiple, eg, 4 to 8, GPUs per node for running computational …

Studies show that non-graphics programs can be less optimized for the GPU hardware,
leading to significant resource under-utilization. Sharing the GPU among multiple programs …

Modern computing platforms tend to deploy multiple GPUs on a single node to boost
performance. GPUs have large computing capacities and are an expensive resource …

Execution of GPGPU workloads consists of different stages including data I/O on the CPU,
memory copy between the CPU and GPU, and kernel execution. While GPU can remain idle …