Computational neuroscience relies on complex mathematical models to simulate brain activity and decipher underlying biological processes.However, these simulations are computationally intensive, prompting the exploration of high-performance computing systems as a viable solution to enhance efficiency.In this work, we introduce SimHH, an extended-Hodgkin-Huxley simulator designed for versatility and high performance.Leveraging the OpenMPI library, SimHH exhibits exceptional scalability, catering to a wide spectrum of computing environments.Scalability is optimized through two distinct configurations: one that distributes all possible cell-compartment potentials among network nodes and another that shares compartment potentials only among relevant nodes, employing MPI Allgather and Alltoall.
Seamless support for CUDA, CUDA-aware MPI, and NVLink further enhances Drone performance, with communication overhead minimized through concurrent execution of compute kernels.Benchmarking against various neuron models, including the challenging Inferior-Olivary Nucleus, demonstrates SimHH’s potential, achieving remarkable results on up to 256 compute nodes.Notably, large-scale GPU clusters enable the simulation of highly biologically plausible networks exceeding 10 million cells.Comparative analyses against CPU- and FPGA-based solutions underscore SimHH’s superiority, boasting a speedup of approximately $150 imes $ over single-threaded CPU implementations, $10 imes $ over single-FPGA setups, and $10 imes $ over multi-threaded CPU configurations with 128 threads, all for a fully connected network of approximately 7,000 IO cells.Additionally, a $7 imes $ speedup is attained compared to the established Building Set NEST neurosimulator running on 32 nodes, simulating a network of 94,720 Hodgkin-Huxley neurons with gap junctions.
These findings underscore SimHH’s efficacy in advancing computational-neuroscience research by facilitating efficient and scalable simulation of complex neuronal networks.