Auto-tuning for neural simulations on different parallel hardware

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Biologically-realistic neural simulations pose new challenges to parallel computing because of the dependency between the network activity and the total computation time, the temporal evolution of their internal connectivity and the heterogeneity of the networks. Auto-tuning techniques can help selecting optimizations at various levels (algorithmic, data structures) using code generation. In this project, we will investigate online and offline auto-tuning methods to increase the parallel efficiency of neural simulations on different hardware (shared-memory, distributed or GPU-based systems), focusing particularly on the improvement of performance measurements, the analysis of data structures, distribution of data using graph partitioning, the learning of heuristics to restrict the search space and parameterized code generation at run-time for a particular network. The resulting mechanisms will be integrated in an existing neural simulator.