Yaozu Dong, Mochi Xue, Xiao Zheng, Jiajun Wang, Zhengwei Qi, Haibing Guan
The increasing adoption of Graphic Process Unit (GPU) to computation-intensive workloads has stimulated a new computing paradigm called GPU cloud (e.g., Amazon’s GPU Cloud), which necessitates the sharing of GPU resources to multiple tenants in a cloud. However, state-of-the-art GPU virtualization techniques such as gVirt still suffer from non-trivial performance overhead for graphics memory-intensive workloads […]
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Christian Pinto
During the last few decades an unprecedented technological growth has been at the center of the embedded systems design paramount, with Moore’s Law being the leading factor of this trend. Today in fact an ever increasing number of cores can be integrated on the same die, marking the transition from state-of-the-art multi-core chips to the […]
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Adrian Castello, Rafael Mayo, Enrique S. Quintana-Orti, Antonio J. Pena, Pavan Balaji
OpenACC is an application programming interface (API) that aims to unleash the power of heterogeneous systems composed of CPUs and accelerators such as graphic processing units (GPUs) or Intel Xeon Phi coprocessors. This directive-based programming model is intended to enable developers to accelerate their application’s execution with much less effort. Coprocessors offer significant computing power […]
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Wenhao Jia
In response to the ever growing demand for computing power, heterogeneous parallelism has emerged as a widespread computing paradigm in the past decade or so. In particular, massively parallel processors such as graphics processing units (GPUs) have become the prevalent throughput computing elements in heterogeneous systems, offering high performance and power efficiency for general-purpose workloads. […]
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Amrit Panda
Stream processing has emerged as an important model of computation especially in the context of multimedia and communication sub-systems of embedded System-on-Chip (SoC) architectures. The dataflow nature of streaming applications allows them to be most naturally expressed as a set of kernels iteratively operating on continuous streams of data. The kernels are computationally intensive and […]
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Antonio J. Pena, Carlos Reano, Federico Silla, Rafael Mayo, Enrique S. Quintana-Orti, Jose Duato
In this paper we detail the key features, architectural design, and implementation of rCUDA, an advanced framework to enable remote and transparent GPGPU acceleration in HPC clusters. rCUDA allows decoupling GPUs from nodes, forming pools of shared accelerators, which brings enhanced flexibility to cluster configurations. This opens the door to configurations with fewer accelerators than […]
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Lan Vu, Hari Sivaraman, Rishi Bidarkar
Graphics Processing Units (GPU) have become important components in high performance computing (HPC) systems for their massively parallel computing capability and energy efficiency. Virtualization technologies are increasingly applied to HPC to reduce administration costs and improve system utilization. However, virtualizing the GPU to support general purpose computing presents many challenges because of the complexity of […]
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Yusuke Suzuki, Shinpei Kato, Hiroshi Yamada, Kenji Kono
Graphics processing units (GPUs) provide orders-of-magnitude speedup for compute-intensive data-parallel applications. However, enterprise and cloud computing domains, where resource isolation of multiple clients is required, have poor access to GPU technology. This is due to lack of operating system (OS) support for virtualizing GPUs in a reliable manner. To make GPUs more mature system citizens, […]
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Yousuf Sait I, Vijayalakshmi R
With the dawn of virtualization and Infrastructureas-a-Service (IaaS), the comprehensive technical computing community is in view of the use of clouds for their technical computing needs. This is due to the relative scalability, ease of use, advanced user milieu customization abilities clouds provide, as well as many novel computing archetypes available for data-intensive applications. However, […]
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Andrew J. Younge, John Paul Walters, Stephen Crago, Geoffrey C. Fox
With the advent of virtualization and Infrastructure-as-a-Service (IaaS), the broader scientific computing community is considering the use of clouds for their technical computing needs. This is due to the relative scalability, ease of use, advanced user environment customization abilities clouds provide, as well as many novel computing paradigms available for data-intensive applications. However, there is […]
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John Paul Walters, Andrew J. Younge, Dong-In Kang, Ke-Thia Yao, Mikyung Kang, Stephen P. Crago, Geoffrey C. Fox
As more scientific workloads are moved into the cloud, the need for high performance accelerators increases. Accelerators such as GPUs offer improvements in both performance and power efficiency over traditional multi-core processors; however, their use in the cloud has been limited. Today, several common hypervisors support GPU passthrough, but their performance has not been systematically […]
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A. Castello, J. Duato, R. Mayo, A. J. Pena, E. S. Quintana-Orti, V. Roca, F. Silla
Many current high-performance clusters include one or more GPUs per node in order to dramatically reduce application execution time, but the utilization of these accelerators is usually far below 100%. In this context, remote GPU virtualization can help to reduce acquisition costs as well as the overall energy consumption. In this paper, we investigate the […]
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Free GPU computing nodes at hgpu.org

Registered users can now run their OpenCL application at hgpu.org. We provide 1 minute of computer time per each run on two nodes with two AMD and one nVidia graphics processing units, correspondingly. There are no restrictions on the number of starts.

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