A Hybrid Task Graph Scheduler for High Performance Image Processing Workflows

Published

December 16, 2015

Author(s)

Timothy J. Blattner, Walid Keyrouz, Milton Halem, Shuvra S. Bhattacharyya, Mary C. Brady

Abstract

The scalability of applications is a key requirement to improving performance in hybrid and cluster computing. Scheduling code to utilize parallelism is difficult, particularly when dealing with dependencies, memory management, data motion, and processor occupancy. The Hybrid Task Graph Scheduler (HTGS) increases programmer productivity to implement hybrid workflows that scale to multi-GPU systems. HTGS is capable of managing dependencies between tasks, represents CPU and GPU memories independently, overlaps computations with disk I/O and memory transfers, keeps multiple GPUs occupied, and uses all available compute resources. We present a prototype of HTGS and implement hybrid microscopy image stitching. Code size is reduced by ≈25% and shows favorable performance compared to a similar hybrid work- flow implementation without HTGS. Computational functions are reused and requires no modification.

Proceedings Title

Symposium on Signal Processing on Graphics Processing Units and Multicores

Conference Dates

December 14-16, 2015

Conference Location

Orlando, FL

Pub Type

Conferences

Download Paper

https://doi.org/10.1109/GlobalSIP.2015.7418273

Keywords

hybrid, task graph, hybrid workflows

Citation

Blattner, T. , Keyrouz, W. , Halem, M. , Bhattacharyya, S. and Brady, M. (2015), A Hybrid Task Graph Scheduler for High Performance Image Processing Workflows, Symposium on Signal Processing on Graphics Processing Units and Multicores, Orlando, FL, [online], https://doi.org/10.1109/GlobalSIP.2015.7418273 (Accessed November 21, 2024)