GPGPUs are useful for many types of compute-intensive workloads from scientific simulations to cloud-focused applications like machine learning and graph analytics. However, unlike CPUs they do not allow for software-controlled sharing of resources. This leads to underutilization, unfair use and reduced programmability. This thesis looks at three different areas, 1) in situ analysis in scientific workflows, 2) multi tenancy in cloud computing environments, and 3) network sharing between evolving distributed GPU frameworks. The thesis presents four distinct software-scheduling based constructs to handle problems in each of these spaces.Ph.D
Machine learning is increasingly being used to solve problems in many domains. This results in a su...
As GPUs evolved into popular computing platforms in the cloud, GPU virtualization has become a highl...
GPUs are being increasingly adopted as compute accelerators in many domains, spanning environments f...
Accelerator-based systems are making rapid inroads into becoming platforms of choice for both high e...
Recent advances in hardware, such as systems with multiple GPUs and their availability in the cloud,...
Graphic Processing Units (GPUs) are currently widely used in High Performance Computing (HPC) applic...
GPU-based clusters are widely chosen for accelerating a variety of scientific applications in high-e...
This paper describes GPUSync, which is a framework for managing graphics processing units (GPUs) in ...
© 2017 IEEE. GPU-based clusters are widely chosen for accelerating a variety of scientific applicati...
Recent advances in GPUs (graphics processing units) lead to mas-sively parallel hardware that is eas...
[EN] GPUs in High-Performance Computing systems remain under-utilised due to the unavailability of s...
Cloud computing is offering new approaches for High Performance Computing (HPC) as it provides dynam...
The ever increasing complexity of scientific applications has led to utilization of new HPC paradigm...
Cloud technology is an attractive infrastructure solution that provides customers with an almost unl...
Abstract—Recently, we have witnessed that cloud providers start to offer heterogeneous computing env...
Machine learning is increasingly being used to solve problems in many domains. This results in a su...
As GPUs evolved into popular computing platforms in the cloud, GPU virtualization has become a highl...
GPUs are being increasingly adopted as compute accelerators in many domains, spanning environments f...
Accelerator-based systems are making rapid inroads into becoming platforms of choice for both high e...
Recent advances in hardware, such as systems with multiple GPUs and their availability in the cloud,...
Graphic Processing Units (GPUs) are currently widely used in High Performance Computing (HPC) applic...
GPU-based clusters are widely chosen for accelerating a variety of scientific applications in high-e...
This paper describes GPUSync, which is a framework for managing graphics processing units (GPUs) in ...
© 2017 IEEE. GPU-based clusters are widely chosen for accelerating a variety of scientific applicati...
Recent advances in GPUs (graphics processing units) lead to mas-sively parallel hardware that is eas...
[EN] GPUs in High-Performance Computing systems remain under-utilised due to the unavailability of s...
Cloud computing is offering new approaches for High Performance Computing (HPC) as it provides dynam...
The ever increasing complexity of scientific applications has led to utilization of new HPC paradigm...
Cloud technology is an attractive infrastructure solution that provides customers with an almost unl...
Abstract—Recently, we have witnessed that cloud providers start to offer heterogeneous computing env...
Machine learning is increasingly being used to solve problems in many domains. This results in a su...
As GPUs evolved into popular computing platforms in the cloud, GPU virtualization has become a highl...
GPUs are being increasingly adopted as compute accelerators in many domains, spanning environments f...
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