{"title":"SSA: A Uniformly Recursive Bidirection-Sequence Systolic Sorter Array","authors":"Teng Gao;Lan Huang;Shang Gao;Kangping Wang","doi":"10.1109/TPDS.2024.3434332","DOIUrl":null,"url":null,"abstract":"The use of reconfigurable circuits with parallel computing capabilities has been explored to enhance sorting performance and reduce power consumption. Nonetheless, most sorting algorithms utilizing dedicated processors are designed solely based on the parallelization of the algorithm, lacking considerations of specialized hardware structures. This leads to problems, including but not limited to the consumption of excessive I/O interface resources, on-chip storage resources, and complex layout wiring. In this paper, we propose a Systolic Sorter Array, implemented by a Uniform Recurrence Equation (URE) with highly parameterised in terms of data size, bit width and type. Leveraging this uniformly recursive structure, the sorter can simultaneously sort two independent sequences. In addition, we implemented global and local control modes on the FPGA to achieve higher computational frequencies. In our experiments, we have demonstrated the speed-up ratio of SSA relative to other state of the art (SOTA) sorting algorithms using C++ \n<inline-formula><tex-math>$std$</tex-math></inline-formula>\n::\n<inline-formula><tex-math>$sort()$</tex-math></inline-formula>\n as benchmark. Inheriting the benefits from the Systolic Array architecture, the SSA reaches up to 810 Mhz computing frequency on the U200. The results of our study show that SSA outperforms other sorting algorithms in terms of throughput, speed-up ratio, and computation frequency.","PeriodicalId":13257,"journal":{"name":"IEEE Transactions on Parallel and Distributed Systems","volume":null,"pages":null},"PeriodicalIF":5.6000,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Parallel and Distributed Systems","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10612231/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
The use of reconfigurable circuits with parallel computing capabilities has been explored to enhance sorting performance and reduce power consumption. Nonetheless, most sorting algorithms utilizing dedicated processors are designed solely based on the parallelization of the algorithm, lacking considerations of specialized hardware structures. This leads to problems, including but not limited to the consumption of excessive I/O interface resources, on-chip storage resources, and complex layout wiring. In this paper, we propose a Systolic Sorter Array, implemented by a Uniform Recurrence Equation (URE) with highly parameterised in terms of data size, bit width and type. Leveraging this uniformly recursive structure, the sorter can simultaneously sort two independent sequences. In addition, we implemented global and local control modes on the FPGA to achieve higher computational frequencies. In our experiments, we have demonstrated the speed-up ratio of SSA relative to other state of the art (SOTA) sorting algorithms using C++
$std$
::
$sort()$
as benchmark. Inheriting the benefits from the Systolic Array architecture, the SSA reaches up to 810 Mhz computing frequency on the U200. The results of our study show that SSA outperforms other sorting algorithms in terms of throughput, speed-up ratio, and computation frequency.
期刊介绍:
IEEE Transactions on Parallel and Distributed Systems (TPDS) is published monthly. It publishes a range of papers, comments on previously published papers, and survey articles that deal with the parallel and distributed systems research areas of current importance to our readers. Particular areas of interest include, but are not limited to:
a) Parallel and distributed algorithms, focusing on topics such as: models of computation; numerical, combinatorial, and data-intensive parallel algorithms, scalability of algorithms and data structures for parallel and distributed systems, communication and synchronization protocols, network algorithms, scheduling, and load balancing.
b) Applications of parallel and distributed computing, including computational and data-enabled science and engineering, big data applications, parallel crowd sourcing, large-scale social network analysis, management of big data, cloud and grid computing, scientific and biomedical applications, mobile computing, and cyber-physical systems.
c) Parallel and distributed architectures, including architectures for instruction-level and thread-level parallelism; design, analysis, implementation, fault resilience and performance measurements of multiple-processor systems; multicore processors, heterogeneous many-core systems; petascale and exascale systems designs; novel big data architectures; special purpose architectures, including graphics processors, signal processors, network processors, media accelerators, and other special purpose processors and accelerators; impact of technology on architecture; network and interconnect architectures; parallel I/O and storage systems; architecture of the memory hierarchy; power-efficient and green computing architectures; dependable architectures; and performance modeling and evaluation.
d) Parallel and distributed software, including parallel and multicore programming languages and compilers, runtime systems, operating systems, Internet computing and web services, resource management including green computing, middleware for grids, clouds, and data centers, libraries, performance modeling and evaluation, parallel programming paradigms, and programming environments and tools.