A General Framework for Accelerating Swarm Intelligence Algorithms on FPGAs, GPUs and Multi-Core CPUs
Swarm intelligence algorithms (SIAs) have demonstrated excellent performance when solving optimization problems including many real-world problems. However, because of their expensive computational cost for some complex problems, SIAs need to be accelerated effectively for better performance. This p...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2018-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8540814/ |
| _version_ | 1819170257275715584 |
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| author | Dalin Li Lan Huang Kangping Wang Wei Pang You Zhou Rui Zhang |
| author_facet | Dalin Li Lan Huang Kangping Wang Wei Pang You Zhou Rui Zhang |
| author_sort | Dalin Li |
| collection | DOAJ |
| description | Swarm intelligence algorithms (SIAs) have demonstrated excellent performance when solving optimization problems including many real-world problems. However, because of their expensive computational cost for some complex problems, SIAs need to be accelerated effectively for better performance. This paper presents a high-performance general framework to accelerate SIAs (FASI). Different from the previous work which accelerates SIAs through enhancing the parallelization only, FASI considers both the memory architectures of hardware platforms and the dataflow of SIAs, and it reschedules the framework of SIAs as a converged dataflow to improve the memory access efficiency. FASI achieves higher acceleration ability by matching the algorithm framework to the hardware architectures. We also design deep optimized structures of the parallelization and convergence of FASI based on the characteristics of specific hardware platforms. We take the quantum behaved particle swarm optimization algorithm as a case to evaluate FASI. The results show that FASI improves the throughput of SIAs and provides better performance through optimizing the hardware implementations. In our experiments, FASI achieves a maximum of 290.7 Mb/s throughput which is higher than several existing systems, and FASI on FPGAs achieves a better speedup than that on GPUs and multi-core CPUs. FASI is up to 123 times and not less than 1.45 times faster in terms of optimization time on Xilinx Kintex Ultrascale xcku040 when compares to Intel Core i7-6700 CPU/NVIDIA GTX1080 GPU. Finally, we compare the differences of deploying FASI on hardware platforms and provide some guidelines for promoting the acceleration performance according to the hardware architectures. |
| first_indexed | 2024-12-22T19:32:31Z |
| format | Article |
| id | doaj.art-72c91f81539546e99c49ee82456798cc |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T19:32:31Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-72c91f81539546e99c49ee82456798cc2022-12-21T18:15:04ZengIEEEIEEE Access2169-35362018-01-016723277234410.1109/ACCESS.2018.28824558540814A General Framework for Accelerating Swarm Intelligence Algorithms on FPGAs, GPUs and Multi-Core CPUsDalin Li0https://orcid.org/0000-0002-3086-9087Lan Huang1Kangping Wang2Wei Pang3You Zhou4Rui Zhang5College of Computer Science and Technology, Jilin University, Changchun, ChinaCollege of Computer Science and Technology, Jilin University, Changchun, ChinaCollege of Computer Science and Technology, Jilin University, Changchun, ChinaDepartment of Computing Science, University of Aberdeen, Aberdeen, U.K.College of Computer Science and Technology, Jilin University, Changchun, ChinaCollege of Computer Science and Technology, Jilin University, Changchun, ChinaSwarm intelligence algorithms (SIAs) have demonstrated excellent performance when solving optimization problems including many real-world problems. However, because of their expensive computational cost for some complex problems, SIAs need to be accelerated effectively for better performance. This paper presents a high-performance general framework to accelerate SIAs (FASI). Different from the previous work which accelerates SIAs through enhancing the parallelization only, FASI considers both the memory architectures of hardware platforms and the dataflow of SIAs, and it reschedules the framework of SIAs as a converged dataflow to improve the memory access efficiency. FASI achieves higher acceleration ability by matching the algorithm framework to the hardware architectures. We also design deep optimized structures of the parallelization and convergence of FASI based on the characteristics of specific hardware platforms. We take the quantum behaved particle swarm optimization algorithm as a case to evaluate FASI. The results show that FASI improves the throughput of SIAs and provides better performance through optimizing the hardware implementations. In our experiments, FASI achieves a maximum of 290.7 Mb/s throughput which is higher than several existing systems, and FASI on FPGAs achieves a better speedup than that on GPUs and multi-core CPUs. FASI is up to 123 times and not less than 1.45 times faster in terms of optimization time on Xilinx Kintex Ultrascale xcku040 when compares to Intel Core i7-6700 CPU/NVIDIA GTX1080 GPU. Finally, we compare the differences of deploying FASI on hardware platforms and provide some guidelines for promoting the acceleration performance according to the hardware architectures.https://ieeexplore.ieee.org/document/8540814/Field programmable gate arraysmulticore processingparallel programmingparticle swarm optimizationpipeline processing |
| spellingShingle | Dalin Li Lan Huang Kangping Wang Wei Pang You Zhou Rui Zhang A General Framework for Accelerating Swarm Intelligence Algorithms on FPGAs, GPUs and Multi-Core CPUs IEEE Access Field programmable gate arrays multicore processing parallel programming particle swarm optimization pipeline processing |
| title | A General Framework for Accelerating Swarm Intelligence Algorithms on FPGAs, GPUs and Multi-Core CPUs |
| title_full | A General Framework for Accelerating Swarm Intelligence Algorithms on FPGAs, GPUs and Multi-Core CPUs |
| title_fullStr | A General Framework for Accelerating Swarm Intelligence Algorithms on FPGAs, GPUs and Multi-Core CPUs |
| title_full_unstemmed | A General Framework for Accelerating Swarm Intelligence Algorithms on FPGAs, GPUs and Multi-Core CPUs |
| title_short | A General Framework for Accelerating Swarm Intelligence Algorithms on FPGAs, GPUs and Multi-Core CPUs |
| title_sort | general framework for accelerating swarm intelligence algorithms on fpgas gpus and multi core cpus |
| topic | Field programmable gate arrays multicore processing parallel programming particle swarm optimization pipeline processing |
| url | https://ieeexplore.ieee.org/document/8540814/ |
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