Opportunities for Cost Savings with In-transit Visualization
Big Data Analytics
Performance Analysis and Optimization
Visualization & Virtual Reality
TimeMonday, June 22nd4:30pm - 5:00pm
DescriptionWe analyze the opportunities for in-transit visualization to provide cost savings compared to in-line visualization. We begin by developing a cost model that includes factors related to both in-line and in-transit which allows comparisons to be made between the two methods. We begin by developing a cost model, which includes the factors that can cause in-transit visualization to be slower or faster than
in-line. We then run a series of studies to create a corpus of data for our model. We run two different visualization algorithms, one that is computation heavy and one that is communication heavy with concurrencies up to 32,768 cores. Our primary results are in exploring the cost model within the
context of our corpus. Our findings show that in-transit consistently achieves significant cost efficiencies by running visualization algorithms at lower concurrency, and that in many cases these efficiencies are enough to offset other costs (transfer, blocking, and additional nodes) to be cost effective overall. Finally, this work informs future studies, which can focus on choosing ideal configurations for in-transit processing that can consistently achieve cost efficiencies.