Scheduling Dynamic Parallelism on Accelerators

Date: 
Wednesday, April 13, 2011 - 16:30
Location: 
TH 331
Presenter: 
Filip Blagojevic (Lawrence Berkeley National Laboratory)
Abstract: 
Resource management on accelerator based systems is complicated by the disjoint nature of the main CPU and accelerators, which involves separate memory hierarhcies, different degrees of parallelism, and relatively high cost of communicating between them. This study addresses the problem of orchestrating and scheduling parallelism at multiple levels of granularity. We present mechanisms and policies for adaptive exploitation and scheduling of layered parallelism on the accelerator-based architectures. Our policies combine event-driven task scheduling with malleable loop-level parallelism, which is exploited from the runtime system whenever task-level parallelism leaves idle cores. We focus on the IBM Cell processor - a representative of accelerator-based architectures. We investigate performance with RAxML - a bioinformatics application which infers large phylogenetic trees, using the Maximum Likelihood method. Our experiments show that the accelerator-based architectures benefit significantly from dynamic methods that selectively exploit the layers of parallelism in the system, in response to workload fluctuation. Our scheduler outperforms the MPI version of RAxML, scheduled by the Linux kernel, by up to a factor of 2.6.
Bio: 

Filip Blagojevic is a Research Scientist in the Future Technologies Group, Lawrence Berkeley National Laboratory. He received a PhD degree in Computer Science from Virginia Tech in 2008, for the research performed in the area of "Scheduling for Asymmetric Architectures". Prior to obtaining a Ph.D. degree, Dr. Blagojevic received an M.S. Degree in Computer Science from the College of William and Mary (2005) and a B.S. degree in Mathematics from the University of Belgrade (2002). His professional interests include, but are not limited to the following: Adaptive Scheduling for Asymmetric
Systems, Power-Aware/Energy-Aware Execution, Emerging Accelerator-Based Architectures: Cell BE, GPGPU, PGAS Languages.