  Efficient execution of parameter sensitivity analysis (SA) is critical to
allow for its routinely use. The pathology image processing application
investigated in this work processes high-resolution whole-slide cancer tissue
images from large datasets to characterize and classify the disease. However,
the application is parameterized and changes in parameter values may
significantly affect its results. Thus, understanding the impact of parameters
to the output using SA is important to draw reliable scientific conclusions.
The execution of the application is rather compute intensive, and a SA requires
it to process the input data multiple times as parameter values are
systematically varied. Optimizing this process is then important to allow for
SA to be executed with large datasets. In this work, we employ a distributed
computing system with novel computation reuse optimizations to accelerate SA.
The new computation reuse strategy can maximize reuse even with limited memory
availability where previous approaches would not be able to fully take
advantage of reuse. The proposed solution was evaluated on an environment with
256 nodes (7168 CPU-cores) attaining a parallel efficiency of over 92%, and
improving the previous reuse strategies in up to 2.8x.
