| 1. |
- Soudris, Dimitrios, et al.
(författare)
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AEGLE : A Big Bio-Data Analytics Framework for Integrated Health-Care Services
- 2015
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Ingår i: Proceedings International Conference on Embedded Computer Systems - Architectures, Modeling and Simulation (SAMOS XV). - 9781467373111 ; , s. 246-253
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Konferensbidrag (refereegranskat)abstract
- AEGLE project(1) targets to build an innovative ICT solution addressing the whole data value chain for health based on: cloud computing enabling dynamic resource allocation, HPC infrastructures for computational acceleration and advanced visualization techniques. In this paper, we provide an analysis of the addressed Big Data health scenarios and we describe the key enabling technologies, as well as data privacy and regulatory issues to be integrated into AEGLE's ecosystem, enabling advanced health-care analytic services, while also promoting related research activities.
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| 2. |
- Smaragdos, G., et al.
(författare)
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BrainFrame: a node-level heterogeneous accelerator platform for neuron simulations
- 2017
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Ingår i: Journal of Neural Engineering. - : IOP Publishing. - 1741-2560 .- 1741-2552. ; 14:6
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Tidskriftsartikel (refereegranskat)abstract
- Objective: The advent of High-Performance Computing (HPC) in recent years has led to its increasing use in brain study through computational models. The scale and complexity of such models are constantly increasing, leading to challenging computational requirements. Even though modern HPC platforms can often deal with such challenges, the vast diversity of the modeling field does not permit for a homogeneous acceleration platform to effectively address the complete array of modeling requirements. Approach: In this paper we propose and build BrainFrame, a heterogeneous acceleration platform that incorporates three distinct acceleration technologies, an Intel Xeon-Phi CPU, a NVidia GP-GPU and a Maxeler Dataflow Engine. The PyNN software framework is also integrated into the platform. As a challenging proof of concept, we analyze the performance of BrainFrame on different experiment instances of a state-of-the-art neuron model, representing the Inferior-Olivary Nucleus using a biophysically-meaningful, extended Hodgkin-Huxley representation. The model instances take into account not only the neuronal-network dimensions but also different network-connectivity densities, which can drastically affect the workload's performance characteristics. Main results: The combined use of different HPC fabrics demonstrated that BrainFrame is better able to cope with the modeling diversity encountered in realistic experiments. Our performance analysis shows clearly that the model directly affects performance and all three technologies are required to cope with all the model use cases. Significance: The BrainFrame framework is designed to transparently configure and select the appropriate back-end accelerator technology for use per simulation run. The PyNN integration provides a familiar bridge to the vast number of models already available. Additionally, it gives a clear roadmap for extending the platform support beyond the proof of concept, with improved usability and directly useful features to the computational-neuroscience community, paving the way for wider adoption.
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| 3. |
- Soudris, Dimitrios, et al.
(författare)
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EXA2PRO programming environment: Architecture and Applications
- 2018
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Ingår i: 2018 INTERNATIONAL CONFERENCE ON EMBEDDED COMPUTER SYSTEMS: ARCHITECTURES, MODELING, AND SIMULATION (SAMOS XVIII). - New York, NY, USA : ASSOC COMPUTING MACHINERY. - 9781450364942 ; , s. 202-209
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Konferensbidrag (refereegranskat)abstract
- The EXA2PRO programming environment will integrate a set of tools and methodologies that will allow to systematically address many exascale computing challenges, including performance, performance portability, programmability, abstraction and reusability, fault tolerance and technical debt. The EXA2PRO tool-chain will enable the efficient deployment of applications in exascale computing systems, by integrating high-level software abstractions that offer performance portability and efficient exploitation of exascale systems heterogeneity, tools for efficient memory management, optimizations based on trade-offs between various metrics and fault-tolerance support. Hence, by addressing various aspects of productivity challenges, EXA2PRO is expected to have significant impact in the transition to exascale computing, as well as impact from the perspective of applications. The evaluation will be based on 4 applications from 4 different domains that will be deployed in JUELICH supercomputing center. The EXA2PRO will generate exploitable results in the form of a tool-chain that support diverse exascale heterogeneous supercomputing centers and concrete improvements in various exascale computing challenges.
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