Return of experience on the mean-shift clustering for heterogeneous architecture use case

The exponential increment in data size poses new challenges for computer scientists, giving rise to a new set of methodologies under the term Big Data. Many efficient algorithms for machine learning have been proposed, facing up time and memory requirements. Nevertheless, with hardware acceleration, multiple software instructions can be integrated and executed into a single hardware die. Current researches aim at eliminating the burden for the user in using multiple processor types. In this paper we propose our return of experience on a new way of implementing machine learning algorithms on heterogeneous hardware. To explore our vision, we use a parallel Mean-shift algorithm, developed at LIPN as our case study to investigate issues in building efficient Machine Learning libraries for heterogeneous systems. The ultimate goal is to provide a core set of building blocks for Machine Learning programming that could serve either to build new applications on heterogeneous architectures or to control the evolution of the underlying platform. We thus examine the difficulties encountered during the implementation of the algorithm with the aim to discover methodologies for building systems based on heterogeneous hardware. We also discover issues and building blocks for solving concrete machine learning (ML) problems on the Chisel software stack we use for this purpose.