Brain modeling has been presenting significant challenges to the world of high-performance computing (HPC) over the years. The field of computational neuroscience has been developing a demand for physiologically plausible neuron models, that feature increased complexity and thus, require greater computational power. We explore Intel’s newest generation of Xeon Phi computing platforms, named Knights Landing (KNL), as a way to match the need for processing power and as an upgrade over the previous generation of Xeon Phi models, the Knights Corner (KNC). Our neuron simulator of choice features a Hodgkin-Huxley-based (HH) model which has been ported on both generations of Xeon Phi platforms and aggressively draws on both platforms’ computational assets. The application uses the OpenMP interface for efficient parallelization and the Xeon Phi’s vectorization buffers for Single-Instruction Multiple Data (SIMD) processing. In this study we offer insight into the efficiency with which the application utilizes the assets of the two Xeon Phi generations and we evaluate the merits of utilizing the KNL over its predecessor. In our case, an out-of-the-box transition on Knights Landing, offers on average 2.4x speed up while consuming 48% less energy than KNC.

Additional Metadata
Keywords Computational neuroscience, Intel xeon phi, Knights landing
Persistent URL dx.doi.org/10.1007/978-3-319-67630-2_27, hdl.handle.net/1765/102724
Series Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
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Grant This work was funded by the European Commission 7th Framework Programme; grant id h2020/687628 - Versatile Integrated Accelerator-based Heterogeneous Data Centres (VINEYARD)
Citation
Chatzikonstantis, G, Jiménez, D. (Diego), Meneses, E. (Esteban), Strydis, C, Sidiropoulos, H. (Harry), & Soudris, D. (2017). From knights corner to landing: A case study based on a hodgkin-huxley neuron simulator. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). doi:10.1007/978-3-319-67630-2_27