Clustered Workflow Execution of Retargeted Data Analysis Scripts

Supercomputing advances have enabled computational science data volumes to grow at ever increasing rates, commonly resulting in more data produced than can be practically analyzed. Whole-dataset download costs have grown to impractical heights, even with multi-Gbps networks, forcing scientists to rely on server-side subsetting and limiting the scope of data they can analyze on a workstation. Our system supplements existing scientific data services with lightweight computational capability, providing a means of safely relocating analysis from the desktop to the server where clustered execution can be coordinated, exploiting data locality, reducing unnecessary data transfer, and providing end-users with results several times faster. We show how dataflow and other compiler-inspired analyses of shell scripts of scientists' most common analysis tools enables parallelization and optimizations in disk and network I/O bandwidth. We benchmark using an actual geo-science analysis script, illustrating the crucial performance gains of extracting workflows defined in scripts and optimizing their execution. Current results quantify significant improvements in performance, showing the promise of bringing transparent high-performance analysis to the scientist's desktop.