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

SHASHANK GUGNANI; XIAOYI LU; FRANCO PESTILLI; CESAR F. CAIAFA; DHABALESWAR K. (DK) PANDA

Jaipur

Conferencia; 24TH IEEE INTERNATIONAL CONFERENCE ON HIGH PERFORMANCE COMPUTING, DATA, AND ANALYTICS; 2017

IEEE

In this paper, we combine high-performance com- puting 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 (i.e., LiFE [1], [2]) that allows for statistical evaluation of brain connectomes. The method called ENCODE [3], [4] 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. We model our design theoretically and demonstrate empirically that the design can be used to identify optimal configurations for the LiFE model optimization via ENCODE method on different hardware platforms. In addition, we co-design the MPI runtime with the LiFE model to achieve profound speed-ups. Extensive evaluation of our designs on multiple clusters corroborate our theoretical model. 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.