Comparative Performance and Scalability Analysis of GPUaccelerated Database Operations

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dc.contributor.authorAndersson, Carl
dc.contributor.authorNilsson, Jonathan
dc.contributor.departmentChalmers tekniska högskola / Institutionen för data och informationstekniksv
dc.contributor.departmentChalmers University of Technology / Department of Computer Science and Engineeringen
dc.contributor.examinerPetersen Moura Trancoso, Pedro
dc.contributor.supervisorPetersen Moura Trancoso, Pedro
dc.date.accessioned2023-12-20T12:24:26Z
dc.date.available2023-12-20T12:24:26Z
dc.date.issued2023
dc.date.submitted2023
dc.description.abstractThis Master’s thesis investigates the performance dynamics of database operations - V-Search, Fuzzy Search, and Join - implemented on both Central Processing Units (CPU) and Graphics Processing Units (GPU). With the ever-increasing demand for efficient data processing, it has become crucial to understand and optimize the use of different hardware platforms for executing diverse database tasks. As such, this research sheds light on the performance of each type of processing unit when running the said operations. The study first details the design and implementation of each database operation on both CPU and GPU, taking into account the different architectural characteristics and processing capabilities of each unit. The specific operations were chosen due to their wide use in the field of data management and their different processing requirements, which allows for a comprehensive performance analysis. Next, a series of benchmark tests is conducted to evaluate the relative performance of the CPU and GPU implementations. Factors such as data size, data type, and transfer time, among others are taken into account. The results show a detailed comparison of execution times between the two implementations, offering insights into the potential advantages and limitations of each. This work contributes to a better understanding of the trade-offs involved when choosing between CPU and GPU for database operations. We hope that our findings will inform future work on hardware-specific optimization for database systems, leading to more efficient and effective solutions for large-scale data processing tasks.
dc.identifier.coursecodeDATX05
dc.identifier.urihttp://hdl.handle.net/20.500.12380/307452
dc.language.isoeng
dc.setspec.uppsokTechnology
dc.subjectDatabase
dc.subjectGPU
dc.subjectCPU
dc.subjectPerformance
dc.subjectV-Search
dc.subjectFuzzy
dc.subjectJoin
dc.subjectCUDA.0
dc.titleComparative Performance and Scalability Analysis of GPUaccelerated Database Operations
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster's Thesisen
dc.type.uppsokH
local.programmeHigh-performance computer systems (MPHPC), MSc
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