MPI-LiFE: Designing High-Performance Linear Fascicle Evaluation of Brain Connectome with MPI

XIAOYI LU; DHABALESWAR PANDA; FRANCO PESTILLI; SHASHANK GUGNANI; CESAR F. CAIAFA

Bloomington

Conferencia; Big Data Neurosciences Workshop 2017; 2017

NSF - Indiana University

We combine high-performance computing science with computational neuroscience methods to show how to speed-up cutting edge methods for mapping and evaluation of the large-scale network of brain connections. More specifically, we use a recent factorization method of the Linear Fascicle Evaluation model (LiFE) that allows for statistical evaluation of brain connectomes. The method called ENCODE uses a Sparse Tucker Decomposition approach to represent the LiFE model. We show that we can implement the optimization step of the ENCODE method using MPI and OpenMP programming paradigms. Our approach involves the parallelization of the multiplication step of the ENCODE method. In addition, we co-design the MPI runtime with the LiFE model to achieve profound speed-ups. We show that on a single node on TACC Stampede2, we can achieve speed-ups of up to 8.7x as compared to the original approach.