A Performance Prediction Framework for Data Intensive Applications on Large Scale Parallel Machines
University of Maryland College Park United States
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This paper presents a simulation-based performance prediction framework for large scale data-intensive applications on large scale machines. Our framework consists of two components application emulators and a suite of simulators. Application emulators provide a parameterized model of data access and computation patterns of the applications and enable changing of critical application components input data partitioning, data declustering, processing structure, etc. easily and flexibly. Our suite of simulators model the IO and communication subsystems with good accuracy and execute quickly on a high-performance workstation to allow performance prediction of large scale parallel machine configurations. The key to efficient simulation of very large scale configurations is a technique called loosely-coupled simulation where the processing structure of the application is embedded in the simulator, while preserving data dependencies and data distributions. We evaluate our performance prediction tool using a set of three data-intensive applications.
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