Distributed data flow for in situ visualization and analysis at petascale

Abstract

We conducted a feasibility study to research modifications to data-flow architectures to enable data-flow to be distributed across multiple machines automatically. Distributed data-flow is a crucial technology to ensure that tools like the VisIt visualization application can provide in-situ data analysis and post-processing for simulations on peta-scale machines. We modified a version of VisIt to study load-balancing trade-offs between light-weight kernel compute environments and dedicated post-processing cluster nodes. Our research focused on memory overheads for contouring operations, which involves variable amounts of generated geometry on each node and computation of normal vectors for all generated vertices. Each compute node independently decided whether to send data to dedicated post-processing nodes at each stage of pipeline execution, depending on available memory. We instrumented the code to allow user settable available memory amounts to test extremely low-overhead compute environments. We performed initial testing of this prototype distributed streaming framework, but did not have time to perform scaling studies at and beyond 1000 compute-nodes.