title: Accelerating Large Scale Image Analyses on Parallel, CPU-GPU Equipped Systems.
abstract: The past decade has witnessed a major paradigm shift in high performance computing with the introduction of accelerators as general purpose processors. These computing devices make available very high parallel computing power at low cost and power consumption, transforming current high performance platforms into heterogeneous CPU-GPU equipped systems. Although the theoretical performance achieved by these hybrid systems is impressive, taking practical advantage of this computing power remains a very challenging problem. Most applications are still deployed to either GPU or CPU, leaving the other resource under- or un-utilized. In this paper, we propose, implement, and evaluate a performance aware scheduling technique along with optimizations to make efficient collaborative use of CPUs and GPUs on a parallel system. In the context of feature computations in large scale image analysis applications, our evaluations show that intelligently co-scheduling CPUs and GPUs can significantly improve performance over GPU-only or multi-core CPU-only approaches.
authors.forename: George
authors.initials: G
authors.lastname: Teodoro
authors.forename: Tahsin M
authors.initials: TM
authors.lastname: Kurc
authors.forename: Tony
authors.initials: T
authors.lastname: Pan
authors.forename: Lee A D
authors.initials: LA
authors.lastname: Cooper
authors.forename: Jun
authors.initials: J
authors.lastname: Kong
authors.forename: Patrick
authors.initials: P
authors.lastname: Widener
authors.forename: Joel H
authors.initials: JH
authors.lastname: Saltz
keywords: CPU-GPU systems; Image analysis; In Silico; Microscopy