SSA: A Uniformly Recursive Bidirection-Sequence Systolic Sorter Array

IF 5.6 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS IEEE Transactions on Parallel and Distributed Systems Pub Date : 2024-07-26 DOI:10.1109/TPDS.2024.3434332
Teng Gao;Lan Huang;Shang Gao;Kangping Wang
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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.
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SSA:统一递归双向序列 Systolic Sorter 阵列
人们一直在探索使用具有并行计算能力的可重构电路来提高排序性能和降低功耗。然而,大多数使用专用处理器的排序算法在设计时只考虑了算法的并行化,缺乏对专用硬件结构的考虑。这就导致了一些问题,包括但不限于消耗过多的 I/O 接口资源、片上存储资源和复杂的布局布线。在本文中,我们提出了一种通过统一递归方程(URE)实现的、在数据大小、位宽和类型方面高度参数化的 Systolic Sorter Array。利用这种均匀递归结构,分拣机可以同时对两个独立序列进行分拣。此外,我们还在 FPGA 上实现了全局和局部控制模式,以达到更高的计算频率。在实验中,我们以 C++ $std$::$sort()$ 为基准,展示了 SSA 相对于其他最新排序算法(SOTA)的加速比率。SSA 继承了 Systolic Array 架构的优点,在 U200 上的计算频率高达 810 Mhz。研究结果表明,SSA 在吞吐量、加速比和计算频率方面都优于其他排序算法。
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来源期刊
IEEE Transactions on Parallel and Distributed Systems
IEEE Transactions on Parallel and Distributed Systems 工程技术-工程:电子与电气
CiteScore
11.00
自引率
9.40%
发文量
281
审稿时长
5.6 months
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.
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