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The extensive use of HPC infrastructures and frameworks for runningdata-intensive applications has led to a growing interest in data partitioningtechniques and strategies. In fact, finding an effective partitioning, i.e. asuitable size for data blocks, is a key strategy to speed-up paralleldata-intensive applications and increase scalability. This paper describes amethodology for data block size estimation in HPC applications, which relies onsupervised machine learning techniques. The implementation of the proposedmethodology was evaluated using as a testbed dislib, a distributed computinglibrary highly focused on machine learning algorithms built on top of thePyCOMPSs framework. We assessed the effectiveness of our solution through anextensive experimental evaluation considering different algorithms, datasets,and infrastructures, including the MareNostrum 4 supercomputer. The results weobtained show that the methodology is able to efficiently determine a suitableway to split a given dataset, thus enabling the efficient execution ofdata-parallel applications in high performance environments.

Block size estimation for data partitioning in HPC applications using machine learning techniques