Minisymposium Presentation

We introduce a large-scale biophysical model for dynamic visual target selection, mimicking human gaze behavior, optimized using Julia programming on multiple GPUs. Our dynamic mean-field model sequentially generates visual targets, accommodating network sizes up to 25600 neural populations, with connectivity matrices reaching up to 25600x25600 neural connections, totaling over 600 million connections. To achieve human-like behavior, we employ Bayesian optimization for parameter tuning, enabling efficient optimization through iterative updates of a probabilistic surrogate model. This enables the model to generate temporally-accurate visual targets to relevant scene locations. Optimization procedures are executed in parallel on 96 instances of the network via GPU supercomputing, simulating over 60 billion neural connections. One iteration of optimizing the largest model takes 70 seconds using 96 GPUs with 99% parallel efficiency. Implementation relies on Julia programming, accessing highly optimized vendor libraries for matrix-vector operations and fast Fourier transformations (CUBLAS and CUFFT for Nvidia GPUs), and utilizing ParallelStencil.jl for stencil computations. MPI enables distributed memory parallelization without communication during function evaluation. We unify the codebase using ParallelStenci.jl to enable both single CPU prototyping and large-scale GPU or CPU runs. This multi-GPU application achieves near-optimal performance and scales efficiently to thousands of NVIDIA Tesla P100 GPUs at CSCS.