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

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Název: Performance testing of ML and HDC : parallelized applications on top of RISC-V architecture ; Performance testing of python libraries
Autoři: Vergés Boncompte, Pere
Přispěvatelé: Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Badia Sala, Rosa Maria, Nicolau, Alexandru, Veidenbaum, Alex
Informace o vydavateli: Universitat Politècnica de Catalunya
Rok vydání: 2022
Sbírka: Universitat Politècnica de Catalunya, BarcelonaTech: UPCommons - Global access to UPC knowledge
Témata: Àrees temàtiques de la UPC::Informàtica::Arquitectura de computadors, RISC microprocessors, Machine learning, RISC-V, Hyperdimensional Computing, Performance, High-Performance Computing, Task-based programming model, RISC (Microprocessadors), Aprenentatge automàtic
Popis: 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.
Druh dokumentu: master thesis
Popis souboru: application/pdf
Jazyk: English
Relation: https://hdl.handle.net/2117/372818; 169630
Dostupnost: https://hdl.handle.net/2117/372818
Rights: Open Access
Přístupové číslo: edsbas.D52C0D1E
Databáze: BASE
Popis
Abstrakt: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.