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A GPU-based real-time processing system for frequency division multiple-input-multiple-output radar

IET RADAR SONAR AND NAVIGATION 2023年第10期17卷 1524-1537页

作者： Liu, Gaogao Bao, Yuqian Yue, Ning Wang, Sitian Wu, Hui Liu, Qiang Yang, Wenbo Xidian Univ Sch Elect & Engn Xian Peoples R China

Multiple-Input-Multiple-Output (MIMO) radar has the characteristic of multiple antenna channels, bringing on a big data volume for signal processing. Therefore, the trade-off between detection ability and computational efficiency is always considered. In this article, an optimisation system is proposed to enhance the real-time performance of frequency division MIMO radar without compromising accuracy. For the scenario of low-altitude small target detection, a signal processing acceleration method is proposed and a MIMO radar optimisation system based on graphics processing unit (GPU) architecture is built. The signal model of frequency division MIMO radar is first established, improving the classical signal processing flow efficiency from the perspective of reducing fast Fourier transform (FFT) times and windowing operation times. To achieve an advanced acceleration, the parallel architecture of ArrayFire-library in GPU is then employed. Distinct minimum parallel units are extracted by analysing the principle of digital beamforming (DBF), pulse compression, moving target detection (MTD), and constant false-alarm rate (CFAR) algorithms. And the corresponding parallel algorithms are designed to constitute a parallel acceleration system of frequency division MIMO. Simulation results indicate that the proposed method significantly improves the efficiency of MIMO system with a maximum acceleration ratio of 65 times, meeting the real-time processing requirements.

关键词： array signal processing frequency-domain analysis MIMO radar optimisation parallel algorithms

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Scalable computational kernels for mortar finite element methods

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ENGINEERING WITH COMPUTERS 2023年第5期39卷 3691-3720页

作者： Mayr, Matthias Popp, Alexander Univ Bundeswehr Munchen Inst Math & Comp Based Simulat Werner-Heisenberg-Weg 39 D-85577 Neubiberg Germany Univ Bundeswehr Munchen Data Sci & Comp Lab Werner-Heisenberg-Weg 39 D-85577 Neubiberg Germany

Targeting simulations on parallel hardware architectures, this paper presents computational kernels for efficient computations in mortar finite element methods. Mortar methods enable a variationally consistent imposition of coupling conditions at high accuracy, but come with considerable numerical effort and cost for the evaluation of the mortar integrals to compute the coupling operators. In this paper, we identify bottlenecks in parallel data layout and domain decomposition that hinder an efficient evaluation of the mortar integrals. We then propose a set of computational strategies to restore optimal parallel communication and scalability for the core kernels devoted to the evaluation of mortar terms. We exemplarily study the proposed algorithmic components in the context of three-dimensional large-deformation contact mechanics, both for cases with fixed and dynamically varying interface topology, yet these concepts can naturally and easily be transferred to other mortar applications, e.g. classical meshtying problems. To restore parallel scalability, we employ overlapping domain decompositions of the interface discretization independent from the underlying volumes and then tackle parallel communication for the mortar evaluation by a geometrically motivated reduction of ghosting data. Using three-dimensional contact examples, we demonstrate strong and weak scalability of the proposed algorithms up to 480 parallel processes as well as study and discuss improvements in parallel communication related to mortar finite element methods. For the first time, dynamic load balancing is applied to mortar contact problems with evolving contact zones, such that the computational work is well balanced among all parallel processors independent of the current state of the simulation.

关键词： Mortar methods Contact mechanics Interface problems parallel algorithms Finite elements Domain decomposition

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Evaluating the Effects of Modern Storage Devices on the Efficiency of parallel Machine Learning algorithms

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INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS 2020年第3-4期29卷

作者： Akritidis, Leonidas Fevgas, Athanasios Tsompanopoulou, Panagiota Bozanis, Panayiotis Univ Thessaly Data Structuring & Engn Lab Dept Elect & Comp Engn 37 Glavani & 28th October Str Volos 38221 Greece

Big Data analytics is presently one of the most emerging areas of research for both organizations and enterprises. The requirement for deployment of efficient machine learning algorithms over huge amounts of data led to the development of parallelization frameworks and of specialized libraries (like Mahout and MLlib) which implement the most important among these algorithms. Moreover, the recent advances in storage technology resulted in the introduction of high-performing devices, broadly known as Solid State Drives (SSDs). Compared to the traditional Hard Drives (HDDs), SSDs offer considerably higher performance and lower power consumption. Motivated by these appealing features and the growing necessity for efficient large-scale data processing, we compared the performance of several machine learning algorithms on MapReduce clusters whose nodes are equipped with HDDs, SSDs, and devices which implement the latest 3D XPoint technology. In particular, we evaluate several dataset preprocessing methods like vectorization and dimensionality reduction, two supervised classifiers, Naive Bayes and Linear Regression, and the popular k-Means clustering algorithm. We use an experimental cluster equipped with the three aforementioned storage devices under different configurations, and two large datasets, Wikipedia and HIGGS. The experiments showed that the benefits which derive from the usage of SSDs depend on the cluster setup and the nature of the applied algorithms.

关键词： Machine learning data mining MapReduce performance parallel algorithms flash memory SSDs 3D XPoint Distributed Storage

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On the convergence analysis of asynchronous SGD for solving consistent linear systems

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LINEAR ALGEBRA AND ITS APPLICATIONS 2023年第1期663卷 1-31页

作者： Sahu, Atal Narayan Dutta, Aritra Tiwari, Aashutosh Richtarik, Peter King Abdullah Univ Sci & Technol KAUST Extreme Comp Res Ctr ECRC Thuwal Saudi Arabia KAUST Visual Comp Ctr VCC Thuwal Saudi Arabia Univ Southern Denmark SDU Dept Math & Comp Sci IMADA Odense Denmark Pioneer Ctr AI P1 Odense Denmark Indian Inst Technol Kanpur India

parallel and distributed stochastic algorithms have drawn significant attention in the realm of big data and machine learning. While the synchronous versions of these algorithms are well understood in terms of their convergence, the convergence analyses of their asynchronous counterparts are not widely studied. In this paper, we propose and analyze a distributed, asynchronous parallel algorithm to solve an arbitrary, consistent linear system by reformulating the system into a stochastic optimization problem as Richtarik and Takac tilde in [1]. We compare the convergence rates of our asynchronous algorithm with the synchronous parallel algorithm proposed by Richtarik and Takac in [1] under different choices of the hyperparameters-the stepsize, the damping factor, the number of processors, and the delay factor. We show that our asynchronous parallel algorithm enjoys a global linear convergence rate, similar to the synchronous parallel algorithm in [1] under the same setup. We also show that our asynchronous algorithm improves upon the synchronous algorithm in [1] with a better convergence rate when the number of processors is larger than four. Furthermore, our asynchronous parallel algorithm performs asymptotically better than its synchronous counterpart for certain linear systems. Finally, we compute the minimum number of processors required for those systems for which our asynchronous algorithm is better and find that this number can be as low as two for some ill-conditioned problems. (c) 2022 Elsevier Inc. All rights reserved.

关键词： Linear systems Distributed optimization Stochastic optimization Asynchronous communication parallel algorithms Iterative methods

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Sparse Symmetric Format for Tucker Decomposition

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IEEE TRANSACTIONS ON parallel AND DISTRIBUTED SYSTEMS 2023年第6期34卷 1743-1756页

作者： Shivakumar, Shruti Li, Jiajia Kannan, Ramakrishnan Aluru, Srinivas Georgia Inst Technol Atlanta GA 30332 USA North Carolina State Univ Raleigh NC 27695 USA Oak Ridge Natl Lab Oak Ridge TN 37830 USA

Tensor-based methods are receiving renewed attention in recent years due to their prevalence in diverse real-world applications. There is considerable literature on tensor representations and algorithms for tensor decompositions, both for dense and sparse tensors. Many applications in hypergraph analytics, machine learning, psychometry, and signal processing result in tensors that are both sparse and symmetric, making them an important class for further study. Similar to the critical Tensor Times Matrix chain operation (TTMC) in general sparse tensors, the Sparse Symmetric Tensor Times Same Matrix chain (S-3 TTMC) operation is compute and memory intensive due to high tensor order and the associated factorial explosion in the number of non-zeros. We present the novel Compressed Sparse Symmetric (CSS) format for sparse symmetric tensors, along with an efficient parallel algorithm for the S-3 TTMC operation. We theoretically establish that S-3 TTMC on CSS achieves a better memory versus run-time trade-off compared to state-of-the-art implementations, and visualize the variation of the performance gap over the parameter space. We demonstrate experimental findings that confirm these results and achieve up to 2.72x speedup on synthetic and real datasets. The scaling of the algorithm on different test architectures is also show-cased to highlight the effect of machine characteristics on algorithm performance.

关键词： Tensors Symmetric matrices Sparse matrices Indexes Signal processing algorithms Matrix decomposition parallel algorithms Compressed storage sparse tensors symmetric tensors tensor times matrix chain

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An Asynchronous parallel Algorithm Framework for Decentralized Pose Graph Optimization 3

An Asynchronous Parallel Algorithm Framework for Decentraliz...

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3rd International Conference on Computer, Control and Robotics, ICCCR 2023

作者： Guo, Guanghui Yi, Peng Tongji University Department of Control Science and Engineering Shanghai China

ISBN: (纸本)9781665492126

This paper proposes an asynchronous parallel algorithm framework for decentralized pose graph optimization (PGO) in collaborative simultaneous localization and mapping. Our framework combines and generalizes the superiority of the existing works on PGO. Specifically, we decompose the objective function of decentralized PGO into the sum of multiple sub-problems to be solved in a fully distributed fashion, and reformulate the sub-problems with the rank-restricted relaxation strategy to reduce the size of the search space and provide a computational certifiable. Moreover, the proposed framework is Gauss-Seidel like without compromising convergence and accelerates the optimization inference with an asynchronous parallel scheme. Finally, we experimentally evaluate the performance of our framework embedded with different optimizers on bench-mark PGO datasets under bounded communication delays. The experiment results demonstrate that, compared to the existing methods, our framework has a faster converge without additional parameter turning and a strong resilience for communication delays. © 2023 IEEE.

关键词： parallel algorithms

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A parallel Connected Region Algorithm Optimized for CPU-GPU Heterogeneous Platforms

A Parallel Connected Region Algorithm Optimized for CPU-GPU ...

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2023 IEEE International Conference on Electrical, Computer and Energy Technologies, ICECET 2023

作者： Troendle, David Jang, Byunghyun The University of Mississippi Computer and Information Science UniversityMS United States

ISBN: (纸本)9798350327816

In recent years, GPU has become an important component of the system by accelerating applications with data parallelism. While traditional discrete GPU cooperates with CPU over I/O bus, more recent processor design trends feature fine-grained synchronization and inexpensive data sharing between CPU and GPU processors by tightly coupling them at a cache level. However, designing algorithms that effectively exploit these new architectural features has proved challenging. In this paper we present a novel parallel algorithm called Parafill that identifies connected regions of arbitrary topolo-gies in an image very efficiently on CPU -GPU heterogeneous platforms. Parafill is a greedy algorithm using dynamic programming memoization techniques. Essential to its efficiency is a data structure called the workload manager, which dynamically manages the complexity of work in each pass. We evaluate the performance of Parafill across two different heterogeneous platforms: a traditional discrete GPU system where the CPU and GPU are connected through I/O bus, and a single-chip processor where the CPU and GPU are tightly coupled at a cache level. We also compare Parafill to a CPU-optimized connected region algorithm using applications with different parallelism to quantify its scalability. Our experimental results show that minimizing GPU overhead and managing pass-by-pass workload are especially important for sparsely parallel applications. Tightly coupled GPUs, although less powerful than discrete GPUs, com-pete well in sparsely parallel applications because of their reduced overhead. Finally, we present two applications of Parafill: traditional flood filling and the absorption of unconnected interior regions into a containing connected region. © 2023 IEEE.

关键词： Connected Regions Flood Fill GPU Programming parallel algorithms

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A Theoretical Bound Which Improves the Performance of Compilation-Based Multi-Agent Path Finding

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IEEE Access 2025年 13卷 86133-86143页

作者： López, Rodrigo Asín-Achá, Roberto Baier, Jorge A. Pontificia Universidad Católica de Chile Department of Computer Science Santiago8320165 Chile Universidad Técnica Federico Santa María Departamento de Informática Santiago8940897 Chile Instituto Milenio Fundamentos de los Datos Santiago8320000 Chile

A well-known approach to optimally solving Multi-Agent Path Finding (MAPF) is by compilation to Boolean Satisfiability or Answer Set Programming. Such compilation-based approaches to MAPF are superior to others on dense, relatively small instances. During solving, the underlying solver is invoked multiple times, each with an encoding of the same instance for a different makespan. The runtime of the last solver invocation, whose input is the instance encoded with a theoretical upper bound of the makespan of the optimal solution, is critical to performance. This paper proposes a new theoretical upper bound for such a last invocation, which we prove is correct. Unlike the previously known bound, given a MAPF instance, our bound requires computing a semi-relaxed solution, which is the union of cost-optimal solutions for partitions of such an instance. The computation of our new bound requires optimally solving partitions, which requires more computational resources than those needed for computing the bound currently used by state-of-the-art solvers. We propose a recursive parallel approach that, we call ReBo, which despite additional overhead in upper bound computation, obtains substantially better overall results by exploiting the new bound. ReBo uses a heuristic to select an appropriate partition for bound computation, which does not guarantee that the solution returned is optimal. However, in our benchmarks, composed of 2,890 problems over warehouses and random instances, ReBo never finds a suboptimal solution. In addition, we found that the new bound is significantly tighter than the previously known, on average, 21.2% smaller than the previous bound. This allows us to generate encodings that are around 33.45% smaller, allowing ReBo to solve 4.9% more instances than a state-of-the-art solver in our benchmark set. © 2013 IEEE.

关键词： parallel algorithms

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Faster parallel Exact Density Peaks Clustering (Abstract)

Faster Parallel Exact Density Peaks Clustering (Abstract)

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2023 ACM Workshop on Highlights of parallel Computing, HOPC 2023

作者： Huang, Yihao Yu, Shangdi Shun, Julian Phillips Academy AndoverMA United States Massachusetts Institute of Technology CambridgeMA United States

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A Low-Memory Community Detection Algorithm With Hybrid Sparse Structure and Structural Information for Large-Scale Networks

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IEEE TRANSACTIONS ON parallel AND DISTRIBUTED SYSTEMS 2023年第10期34卷 2671-2683页

作者： Zhu, Weiguo Sun, Yongqi Fang, Rongqiang Xu, Baomin Beijing Jiaotong Univ Key Lab Big Data & Artificial Intelligence Transpo Minist Educ Beijing 100044 Peoples R China Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing 100044 Peoples R China

Community detection plays an essential role in the domains of social, bioinformatics, and e-commerce. The innovative structural information theory (SInfo, introduced by Li et al.) has achieved excellent performance for network analysis. Nevertheless, similar to traditional network algorithms, the SInfo algorithm will exhaust all system memory resources when processing large-scale networks based on adjacency data structures. Moreover, due to the irregular and sequential characteristics, the SInfo algorithm is challenging to parallelize. In this article, we propose a hybrid sparse data structure and design a low-memory community detection algorithm based on structural information theory (HSSInfo), which shrinks memory requirements and achieves high parallelism. Specifically, we first propose a general quantization method to quantify the information change in community transformation, with which community inner and outer connection complexity can be semantically interpreted. Second, we design a general hybrid sparse structure to store network data among CPU and GPU, which can reduce memory resource consumption for community detection on large-scale networks. Finally, we develop an HSSInfo algorithm based on the quantization method and hybrid sparse structure, in which parallelism intersection and community fusion algorithms are employed to improve parallel scalability. We execute the comparison experiments for HSSInfo and eleven baseline algorithms on nine real-world datasets. Empirically, HSSInfo can achieve orders of magnitude speedups and extend structural information theory to large-scale datasets of billions of edges. Meanwhile, compared with various community detection algorithms, HSSInfo can achieve higher or similar accuracy with up to 19x memory shrinkage ratio.

关键词： Index Terms-Community detection hybrid sparse structure structural information theory social networks parallel algorithms

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