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An open-source framework for offloading big data and AI tasks (Offload) to heterogeneous compute units

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Název: An open-source framework for offloading big data and AI tasks (Offload) to heterogeneous compute units
Autoři: Akula, Satya Sai Siva Rama Krishna
Přispěvatelé: Rahman, Mostafizur
Rok vydání: 2024
Sbírka: University of Missouri: MOspace
Témata: Big data, Artificial intelligence -- Data processing, Heterogeneous distributed computing systems, Thesis -- University of Missouri--Kansas City -- Electrical Engineering
Popis: Title from PDF of title page, viewed February 14, 2025 ; Thesis advisor: Rahman Mostafizur ; Vita ; Includes bibliographical references (pages 56-59) ; Thesis (M.S.)--Department of Computer Science and Electrical Engineering. University of Missouri--Kansas City, 2024 ; The ever-increasing demands of artificial intelligence (AI) and big data processing have spurred the rapid development of novel hardware architectures specifically designed for computationally intensive tasks. Alongside these advancements, software solutions are emerging to exploit this specialized hardware by offloading tasks. However, proprietary software often necessitates a substantial learning curve for users, hindering widespread adoption and flexibility. This paper proposes OFFLOAD, an open-source, hardware-agnostic software-hardware framework. OFFLOAD facilitates the distribution of tasks across diverse hardware units, encompassing both cutting-edge accelerators and existing system-on-chip (SoC) architectures. Our framework seamlessly integrates with popular databases and application development tools. Through the utilization of multi-level abstractions implemented at the compiler, operating system, and driver levels, OFFLOAD translates high-level code and data into hardware-optimized binary instructions. To the best of our knowledge, OFFLOAD represents a ground-breaking approach within this domain. The feasibility of OFFLOAD is demonstrably validated by its integration with prevalent tools such as MySQL, Apache Spark, and Apache Arrow within a user-friendly Python environment. Subsequently, tasks are offloaded for execution on hardware leveraging memory mapped I/O. This is exemplified by integrating OFFLOAD with Raspberry Pi devices, showcasing the entire workflow from software-based data query to hardware execution. ; Introduction -- Flow of information -- Software stack -- Hardware setup -- Hardware network and task distribution -- Software & firmware implementation -- Results -- Conclusion
Druh dokumentu: thesis
Popis souboru: x, 60 pages
Jazyk: English
Relation: https://hdl.handle.net/10355/107249
Dostupnost: https://hdl.handle.net/10355/107249
Přístupové číslo: edsbas.41C1C1B8
Databáze: BASE
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  Data: An open-source framework for offloading big data and AI tasks (Offload) to heterogeneous compute units
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  Data: Title from PDF of title page, viewed February 14, 2025 ; Thesis advisor: Rahman Mostafizur ; Vita ; Includes bibliographical references (pages 56-59) ; Thesis (M.S.)--Department of Computer Science and Electrical Engineering. University of Missouri--Kansas City, 2024 ; The ever-increasing demands of artificial intelligence (AI) and big data processing have spurred the rapid development of novel hardware architectures specifically designed for computationally intensive tasks. Alongside these advancements, software solutions are emerging to exploit this specialized hardware by offloading tasks. However, proprietary software often necessitates a substantial learning curve for users, hindering widespread adoption and flexibility. This paper proposes OFFLOAD, an open-source, hardware-agnostic software-hardware framework. OFFLOAD facilitates the distribution of tasks across diverse hardware units, encompassing both cutting-edge accelerators and existing system-on-chip (SoC) architectures. Our framework seamlessly integrates with popular databases and application development tools. Through the utilization of multi-level abstractions implemented at the compiler, operating system, and driver levels, OFFLOAD translates high-level code and data into hardware-optimized binary instructions. To the best of our knowledge, OFFLOAD represents a ground-breaking approach within this domain. The feasibility of OFFLOAD is demonstrably validated by its integration with prevalent tools such as MySQL, Apache Spark, and Apache Arrow within a user-friendly Python environment. Subsequently, tasks are offloaded for execution on hardware leveraging memory mapped I/O. This is exemplified by integrating OFFLOAD with Raspberry Pi devices, showcasing the entire workflow from software-based data query to hardware execution. ; Introduction -- Flow of information -- Software stack -- Hardware setup -- Hardware network and task distribution -- Software & firmware implementation -- Results -- Conclusion
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