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EPIC: An Energy-Efficient, High-Performance GPGPU Computing Research Infrastructure

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arxiv 1912.05848 v6 pith:H74JJVY4 submitted 2019-12-12 cs.DC

classification cs.DC
keywords researchinfrastructurecomputinggpgpuresourcescomputationalepichigh-performance
verification ladder T0 review T1 audit T2 compute T3 formal

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The pursuit of many research questions requires massive computational resources. State-of-the-art research in physical processes using simulations, the training of neural networks for deep learning, or the analysis of big data are all dependent on the availability of sufficient and performant computational resources. For such research, access to a high-performance computing infrastructure is indispensable. Many scientific workloads from such research domains are inherently parallel and can benefit from the data-parallel architecture of general purpose graphics processing units (GPGPUs). However, GPGPU resources are scarce at Norway's national infrastructure. EPIC is a GPGPU enabled computing research infrastructure at NTNU. It enables NTNU's researchers to perform experiments that otherwise would be impossible, as time-to-solution would simply take too long.

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Cited by 2 Pith papers

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    A thin magnetic domain in pinwheel artificial spin ice translates one lattice step per cycle of a global field protocol, with direction set by its orientation, as shown in simulation and experiment.

  2. Energy Scalability Limits of Dissipative Solitons

    physics.optics 2024-12 conditional novelty 5.0 of 10

    The energy of a single dissipative soliton is limited by a thermodynamic instability: rising entropy and a transition to negative temperature favor breakup into multiple lower-energy pulses.

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