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Teng Li, Vikram K. Narayana, Tarek El-Ghazawi
The past several years have witnessed significant performance improvements in High-Performance Computing (HPC), due to the incorporation of GPUs as co-processors. On one hand, GPU devices are growing significantly in terms of the available number of cores and the memory hierarchy; as a result, effective utilization of the available GPU resources while limiting the system […]
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Alina Sirbu, Ozalp Babaoglu
Power consumption is a major obstacle for High Performance Computing (HPC) systems in their quest towards the holy grail of ExaFLOP performance. Significant advances in power efficiency have to be made before this goal can be attained and accurate modeling is an essential step towards power efficiency by optimizing system operating parameters to match dynamic […]
Judit Planas Carbonell
There is a clear trend nowadays to use heterogeneous high-performance computers, as they offer considerably greater computing power than homogeneous CPU systems. Extending traditional CPU systems with specialized units (accelerators such as GPGPUs) has become a revolution in the HPC world. Both the traditional performance-per-Watt and the performance-per-Euro ratios have been increased with the use […]
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Christopher Sewell, Katrin Heitmann, Hal Finkel, George Zagaris, Suzanne T. Parete-Koon, Patricia K. Fasel, Adrian Pope, Nicholas Frontiere, Li-ta Lo, Bronson Messer, Salman Habib, James Ahrens
Large-scale simulations can produce hundreds of terabytes to petabytes of data, complicating and limiting the efficiency of work-flows. Traditionally, outputs are stored on the file system and analyzed in post-processing. With the rapidly increasing size and complexity of simulations, this approach faces an uncertain future. Trending techniques consist of performing the analysis in-situ, utilizing the […]
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Linchuan Chen
Because of the bottleneck in the increase of clock frequency, multi-cores emerged as a way of improving the overall performance of CPUs. In the recent decade, many-cores begin to play a more and more important role in scientific computing. The highly cost-effective nature of many-cores makes them extremely suitable for data-intensive computations. Specifically, many-cores are […]
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Florence Monna
More and more computers use hybrid architectures combining multi-core processors (CPUs) and hardware accelerators like GPUs (Graphics Processing Units). These hybrid parallel platforms require new scheduling strategies. This work is devoted to a characterization of this new type of scheduling problems. The most studied objective in this work is the minimization of the makespan, which […]
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A. Tarun Beri, B. Sorav Bansal, C. Subodh Kumar
We study work-stealing based scheduling on a cluster of nodes with CPUs and GPUs. In particular, we evaluate locality aware scheduling in the context of distributed shared memory style programming, where the user is oblivious to data placement. Our runtime maintains a distributed map of data resident on various nodes and uses it to estimate […]
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Terry Cojean, Abdou Guermouche, Andra Hugo, Raymond Namyst, Pierre-Andre Wacrenier
Computing platforms are now extremely complex providing an increasing number of CPUs and accelerators. This trend makes balancing computations between these heterogeneous resources performance critical. In this paper we tackle the task granularity problem and we propose aggregating several CPUs in order to execute larger parallel tasks and thus find a better equilibrium between the […]
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Glenn A. Elliott
Self-driving cars, once constrained to closed test tracks, are beginning to drive alongside human drivers on public roads. Loss of life or property may result if the computing systems of automated vehicles fail to respond to events at the right moment. We call such systems that must satisfy precise timing constraints "real-time systems." Since the […]
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Peng Zhang, Yuxiang Gao, Meikang Qiu
The rapidly-changing computer architectures, though improving the performance of computers, have been challenging the programming environments for efficiently harnessing the potential of novel architectures. In this area, though the high-density multi-GPU architecture enabled unparalleled performance advantage of dense GPUs in a single server, it has increased the difficulty for scheduling diversified and dependent tasks. We […]
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Yujie Zhang, Jiabin Yuan, Xiangwen Lu, Xingfang Zhao
General Purpose Graphics Units (GPGPUS) have seen a tremendous rise in scientific computing application. To fully utilize the powerful parallel computing ability of GPU, and combine the isolation characteristic of virtualization, a GPU virtualization method that supports dynamic scheduling and multi-user concurrency is proposed. For multi-task of GPU general computing programs in virtualization environment, the […]
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Yuki Tsujita, Toshio Endo
Recently large scale scientific computation on heterogeneous supercomputers equipped with accelerators is receiving attraction. However, traditional static job execution methods and memory management methods are insufficient in order to harness heterogeneous computing resources including memory efficiently, since they introduce larger data movement costs and lower resource usage. This paper takes the Cholesky decomposition computation, which […]
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