2019
Fully-Asynchronous Cache-Efficient Simulation of Detailed Neural Networks
Magalhães B, Sterling T, Hines M, Schürmann F. Fully-Asynchronous Cache-Efficient Simulation of Detailed Neural Networks. Lecture Notes In Computer Science 2019, 11538: 421-434. DOI: 10.1007/978-3-030-22744-9_33.Peer-Reviewed Original ResearchRuntime systemNeural networkScientific applicationsLarge-scale scientific applicationsAsynchronous runtime systemHPX runtime systemParalleX execution modelOverlap of computationLinear data structuresBetter cache localityCompute architecturesExecution modelParallel executionCore kernelsData structureCache localitySynchronous executionMemory spaceCache levelsNode levelBenchmark resultsExecutionLower timeNumber of timestepsNetwork
2016
Leveraging a Cluster-Booster Architecture for Brain-Scale Simulations
Kumbhar P, Hines M, Ovcharenko A, Mallon D, King J, Sainz F, Schürmann F, Delalondre F. Leveraging a Cluster-Booster Architecture for Brain-Scale Simulations. Lecture Notes In Computer Science 2016, 9697: 363-380. DOI: 10.1007/978-3-319-41321-1_19.Peer-Reviewed Original ResearchCluster-Booster architectureComplex scientific workflowsBrain-scale simulationsIntel MIC platformIntel Xeon Phi coprocessorXeon Phi coprocessorType of architectureSupercomputing architecturesScalable partScientific workflowsIntel MICStampede supercomputerCompute EngineCompute kernelsMIC platformData structureDevelopment workflowImplementation detailsScientific applicationsArchitecture performanceCore simulatorArchitectureEntry platformDeep platformPlatform