Optimizing Extended Hodgkin-Huxley Neuron Model Simulations for a Xeon/Xeon Phi Node

Chatzikonstantis, George; Rodopoulos, Dimitrios; Strydis, Christos; Zeeuw, Chris I. De; Soudris, Dimitrios

doi:10.1109/tpds.2017.2686389

Cited by 6 publications

(5 citation statements)

References 36 publications

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“…Loop-optimizations unrolling, 9,17,23,24,29,50,84,90 collapsing, 4,6,7,13,20,21,44,54 splitting 22,28 Blocking (tiling) in cache, 14,15,18,[20][21][22]27,39,44,52,54,69 registers 68,69 Compile-time optimizations using pre-computed values, 35,52 specifying array and loop bounds at compile time 6,54 Compute-related optimizations Reusing intermediate variables, 22,35 using conflict-detection instruction of AVX-512, 52,85 performing redundant computation to avoid data-communication or atomic operations 52,82 Array transpose 6, 79…”

Section: Ta B L E 3 Optimization Strategiesmentioning

confidence: 99%

“…Data layouts SoA, 20,22,23,28,30,36,50,79,82,90 AoS, 9 AoSoA 57,90 Data alignment 6,9,14,18,20,24,44,45,52,53,66,79,84,90 Padding 4,7,9,20,24,44,52,53,79,82,91 Dependency disambiguation 15,28,36,82,91 Prefetching Software, 4,7,9,14,17,22,23,40,41,50,…”

Section: Ta B L E 3 Optimization Strategiesmentioning

confidence: 99%

“…,[12][13][14][16][17][18]20,21,24,[27][28][29][30][33][34][35][36][37]39,[42][43][44][45][46][47][48]50,52,55,57,59,63,66,84,86,90,92,93,95,99 IntelMKL 2,17,19,31,32,40,93,99 …”

mentioning

confidence: 99%

See 2 more Smart Citations

A survey on evaluating and optimizing performance of Intel Xeon Phi

Mittal

2020

Concurrency and Computation

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Summary Intel's Xeon Phi combines the parallel processing power of a many‐core accelerator with the programming ease of CPUs. In this paper, we present a survey of works that study the architecture of Phi and use it as an accelerator for a broad range of applications. We review performance optimization strategies as well as the factors that bottleneck the performance of Phi. We also review works that perform comparison or collaborative execution of Phi with CPUs and GPUs. This paper will be useful for researchers and developers in the area of computer‐architecture and high‐performance computing.

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Section: Ta B L E 3 Optimization Strategiesmentioning

confidence: 99%

Section: Ta B L E 3 Optimization Strategiesmentioning

confidence: 99%

See 1 more Smart Citation

A survey on evaluating and optimizing performance of Intel Xeon Phi

Mittal

2020

Concurrency and Computation

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“…Neuron Computing: The InfOli simulator is a neuron network simulator, specializing in providing accurate, biophysicallymeaningful insight into how a network comprised of human neurons operates [14]. The simulation of massive dense networks of the neuron model is very challenging, due to the high demand in floating-point operations per simulation cycle.…”

Section: Exa2pro Benchmarksmentioning

confidence: 99%

EXA2PRO programming environment

Soudris

Papadopoulos

Keßler

et al. 2018

Proceedings of the 18th International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation

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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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“…It encompasses many of the chemical processes in mathematical form and can be used as the base description from which multiple specialized and simplified cases can be derived. Using the Hodgkin Huxley model and combining it with a Leaky Integrate and fire neuron can lead to an improvement of computation speed and simulation accuracy [27] [28]. One such model was created by Izhikevich [27]…”

Section: B Snnsmentioning

confidence: 99%

Topological Evolution of Spiking Neural Networks

Slade

Zhang

2018

2018 International Joint Conference on Neural Networks (IJCNN)

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Neuro-evolution is often used to generate the parameters, topology, and rules of artificial neural networks. This technique allows for automatic configuration of a neural network. In this paper we propose a method to generate Spiking Neural Networks (SNNs) automatically called NENG (Neuro-Evolutionary Network Generation). The aim was to help alleviate the manual construction and optimization of neural network implementations. The results show the algorithm is successful at generating and improving the design of SNNs for a Classification task. After 812 generations with a population size of 20 the algorithm converges to model the Xor gate with 100% accuracy. The results show improvements to the algorithm execution time and number of neurons over time.

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Optimizing Extended Hodgkin-Huxley Neuron Model Simulations for a Xeon/Xeon Phi Node

Cited by 6 publications

References 36 publications

A survey on evaluating and optimizing performance of Intel Xeon Phi

A survey on evaluating and optimizing performance of Intel Xeon Phi

EXA2PRO programming environment

Topological Evolution of Spiking Neural Networks

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