Massive Parallel Fluid Flow Simulations Using Hierarchical Data Format Version 5 (HDF5)
More and more massive parallel codes running on several hundreds of thousands of cores enter the computational science and engineering domain, allowing high fidelity computations up to trillions of unknowns for very detailed analyses of the underlying problems. During such runs, typically gigabytes of data are being produced, hindering both efficient storage and (interactive) data exploration. Here, advanced approaches based on inherently distributed data formats such as HDF5 become necessary in order to avoid long latencies when storing the data and to support fast (random) access when retrieving the data for visual processing. Avoiding file locking and using collective buffering, we achieved write bandwiths to a single file close to the theoretical peak on a modern supercomputing cluster. The structure of our output file supports a very fast interactive visualisation and introduces additional steering functionality.
2016 15th International Symposium on Parallel and Distributed Computing (ISPDC)