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Taming parallel I/O complexity with auto-tuning., , , , , , , and . SC, page 68:1-68:12. ACM, (2013)Techniques for modeling large-scale HPC I/O workloads., , , , , , , , , and . PMBS@SC, page 5:1-5:11. ACM, (2015)Parallel cartographic modeling: a methodology for parallelizing spatial data processing., , , , , , and . Int. J. Geogr. Inf. Sci., 30 (12): 2355-2376 (2016)A multi-level approach for understanding I/O activity in HPC applications., , , and . CLUSTER, page 1-5. IEEE Computer Society, (2013)Dynamic Model-Driven Parallel I/O Performance Tuning., , , , and . CLUSTER, page 184-193. IEEE Computer Society, (2015)Design of a Multithreaded Barnes-Hut Algorithm for Multicore Clusters., , and . IEEE Trans. Parallel Distributed Syst., 26 (7): 1861-1873 (2015)A framework for auto-tuning HDF5 applications., , , , , , , and . HPDC, page 127-128. ACM, (2013)Pattern-driven parallel I/O tuning., , , and . PDSW@SC, page 43-48. ACM, (2015)Optimizing parallel I/O performance of HPC applications. University of Illinois Urbana-Champaign, USA, (2015)Automatic generation of I/O kernels for HPC applications., , , , and . PDSW@SC, page 31-36. IEEE, (2014)