ArticleProceedings. IEEE International Conference on Cluster Computing2017
Parallel and Efficient Sensitivity Analysis of Microscopy Image Segmentation Workflows in Hybrid Systems.
Article in Proceedings. IEEE International Conference on Cluster Computing, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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Who cites it
3 citing papers in PubMed, 8 citations in OpenAlex.
- Optimizing parameter sensitivity analysis of large-scale microscopy image analysis workflows with multilevel computation reuse.Concurrency and computation : practice & experience · 2020Article
- Multi-objective Parameter Auto-tuning for Tissue Image Segmentation Workflows.Journal of digital imaging · 2019Article
- Sensitivity analysis in digital pathology: Handling large number of parameters with compute expensive workflows.Computers in biology and medicine · 2019Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
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.
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Registered trials
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