Parallel and Efficient Sensitivity Analysis of Microscopy Image Segmentation Workflows in Hybrid Systems

• Type of Material: Conference Paper
• Authors: William Barreiros, University of Brasília; George Teodoro, University of Brasília; Tahsin Kurc, Stony Brook University; Jun Kong, Emory University; Alba C.M.A. Melo, University of Brasília; Joel Saltz, Stony Brook University
• Language: English
• Date: 2017-01-01
• Publisher: Emory University Libraries
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2017 IEEE.
• Final Published Version (URL): http://dx.doi.org/10.1109/CLUSTER.2017.28
• Title of Journal or Parent Work: 2017 IEEE INTERNATIONAL CONFERENCE ON CLUSTER COMPUTING (CLUSTER)
• Conference or Event Name: IEEE International Conference on Cluster Computing (CLUSTER)
• Volume: 2017-September
• Start Page: 25
• End Page: 35
• Grant/Funding Information: This work was supported in part by 1U24CA180924-01A1 from the NCI, R01LM011119-01 and R01LM009239 from the NLM, CNPq, Capes/Brazil grant PROCAD-183794, and NIH K25CA181503; This research used resources of the XSEDE Science Gateways program under grant TG-ASC130023.
• Keywords: Science & Technology; Technology; Engineering; Computer Science; Engineering, Electrical & Electronic; Computer Science, Theory & Methods; Computer Science, Hardware & Architecture
• Research Categories: Computer Science; Biology, Bioinformatics

Abstract

We investigate efficient sensitivity analysis (SA) of algorithms that segment and classify image features in a large dataset of high-resolution images. Algorithm SA is the process of evaluating variations of methods and parameter values to quantify differences in the output. A SA can be very compute demanding because it requires re-processing the input dataset several times with different parameters to assess variations in output. In this work, we introduce strategies to efficiently speed up SA via runtime optimizations targeting distributed hybrid systems and reuse of computations from runs with different parameters. We evaluate our approach using a cancer image analysis workflow on a hybrid cluster with 256 nodes, each with an Intel Phi and a dual socket CPU. The SA attained a parallel efficiency of over 90% on 256 nodes. The cooperative execution using the CPUs and the Phi available in each node with smart task assignment strategies resulted in an additional speedup of about 2×. Finally, multi-level computation reuse lead to an additional speedup of up to 2.46× on the parallel version. The level of performance attained with the proposed optimizations will allow the use of SA in large-scale studies.