PerformML: Performance Optimized Machine Learning by Platform and Content Aware Customization

We propose Perform-ML, the first Machine Learning (ML) framework for analysis of massive and dense data which customizes the algorithm to the underlying platform for the purpose of achieving optimized resource efficiency. Perform-ML creates a performance model quantifying the computational cost of iterative analysis algorithms on a pertinent platform in terms of FLOPs, communication, and memory, which characterize runtime, storage, and energy. The core of Perform-ML is a novel parametric data projection algorithm, called Elastic Dictionary (ExD), that enables versatile and sparse representations of the data which can help in minimizing performance cost. We show that Perform-ML can achieve the optimal performance objective, according to our cost model, by platform aware tuning of the ExD parameters. An accompanying API ensures automated applicability of Perform-ML to various algorithms, datasets, and platforms. Proof-of-concept evaluations of massive and dense data on different platforms demonstrate more than an order of magnitude improvements in performance compared to the state of the art, within guaranteed user-defined error bounds.