Treffer: Performance testing of ML and HDC : parallelized applications on top of RISC-V architecture ; Performance testing of python libraries

The economic impact that proprietary ISA has on the market increased the interest in using Open Source ISA. More specifically RISC-V has been getting a lot of traction in the research community. The Open Source environment allowed for the development of software and hardware stack for Exascale computations. To take advantage of these resources and allow for executions of large and complex applications, task-based programming models have become more popular, thanks to their ease when handling composite workflows that require a large amount of data and computation time. Moreover, most of the applications being developed nowadays are related to Machine Learning in general, and in the context of RISC-V, there is a lot of interest in developing applications for Embedded Systems, where the framework of Hyperdimensional Computing is becoming more popular. For these reasons in we present this study in the scope of the MareNostrum Experimental Exascale Platform (MEEP), which is a flexible FPGA-based emulation platform designed for future RISC-V supercomputers. This study evaluates Machine Learning algorithms, classical Linear Algebra algorithms used for ML, and Hyperdimensional Computing Algorithms using COMPSs, a task-based programming model for the development of applications for distributed infrastructures, in different RISC-V boards being developed in the MEEP project and different mathematical libraries.