Supervisor: Kyle Chard (University of Chicago)
Abstract: Scientific applications often exhibit a trade-off between cost and accuracy. However, measuring and predicting cost and accuracy in a way that users can understand these trade-offs is challenging. To address these needs, we present predictive cost and accuracy models for data-intensive genomics applications. We use these models to create a trade-off graph, which researchers can use to selectively trade-off cost and accuracy.
ACM-SRC Semi-Finalist: yes
Poster Summary: PDF
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