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pi-Lisco: parallel and Incremental Stream-Based Point-Cloud Clustering 22

pi-Lisco: Parallel and Incremental Stream-Based Point-Cloud ...

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37th annual acm symposium on Applied Computing

作者： Najdataei, Hannaneh Gulisano, Vincenzo Tsigas, Philippas Papatriantafilou, Marina Chalmers Univ Technol Gothenburg Sweden

ISBN: (纸本)9781450387132

Point-cloud clustering is a key task in applications like autonomous vehicles and digital twins, where rotating LiDAR sensors commonly generate point-cloud measurements in data streams. The state-ofthe-art algorithms, Lisco and its parallel equivalent P-Lisco, define a single-pass distance-based clustering. However, while outperforming other batch-based techniques, they cannot incrementally cluster point-clouds from consecutive LiDAR rotations, as they cannot exploit result-similarity between rotations. The simplicity of Lisco, along with the potential of improvements through utilization of computational overlaps, form the motivation of a more challenging objective studied here. We propose parallel and Incremental Lisco (pi-Lisco), which, with a simple yet efficient approach, clusters LiDAR data in streaming sliding windows, reusing the results from overlapping portions of the data, thus, enabling single-window (i.e., in-place) processing. Moreover, pi-Lisco employs efficient work-sharing among threads, facilitated by the ScaleGate data structure, and embeds a customised version of the STINGER concurrent data structure. Through an orchestration of these key ideas, pi-Lisco is able to lead to significant performance improvements. We complement with an evaluation of pi-Lisco, using the Ford Campus real-world extensive data-set, showing (i) the computational benefits from incrementally processing the consecutive point-clouds;and (ii) the fact that pi-Lisco' parallelization leads to continuously increasing sustainable rates with increasing number of threads, shifting the saturation point of the baseline.

关键词： Clustering Data-stream processing Point-cloud analysis

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Dynamic Graph algorithms with Batch Updates in the Massively parallel Computation Model 32

Dynamic Graph Algorithms with Batch Updates in the Massively...

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32nd annual acm-SIAM symposium on Discrete algorithms (SODA)

作者： Nowicki, Krzysztof Onak, Krzysztof Univ Wroclaw Wroclaw Poland IBM Res Hawthorne NY USA

ISBN: (纸本)9781611976465

We study dynamic graph algorithms in the Massively parallel Computation model, which was inspired by practical data processing systems. Our goal is to provide algorithms that can efficiently handle large batches of edge insertions and deletions. We show algorithms that require fewer rounds to update a solution to problems such as Minimum Spanning Forest, 2-Edge Connected Components, and Maximal Matching than would be required by their static counterparts to compute it from scratch. They work in the most restrictive memory regime, in which local memory per machine is strongly sublinear in the number of graph vertices. Improving on the size of the batch they can handle efficiently would improve on the round complexity of known static algorithms on sparse graphs. Our algorithms can process batches of updates of size Theta(S), for Minimum Spanning Forest and 2-Edge Connected Components, and Theta(S1-epsilon), for Maximal Matching, in O(1) rounds, where S is the local memory of a single machine.

关键词： Scheduling algorithms

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cuPSO: GPU parallelization for Particle Swarm Optimization algorithms 22

cuPSO: GPU Parallelization for Particle Swarm Optimization A...

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37th annual acm symposium on Applied Computing

作者： Wang, Chuan-Chi Ho, Chun-Yen Tu, Chia-Heng Hung, Shih-Hao Natl Taiwan Univ Taipei Taiwan Natl Cheng Kung Univ Tainan Taiwan

ISBN: (纸本)9781450387132

Particle Swarm Optimization (PSO) is a stochastic technique for solving the optimization problem. Attempts have been made to shorten the computation times of PSO based algorithms with massive threads on GPUs (graphic processing units), where thread groups are formed to calculate the information of particles and the computed outputs for the particles are aggregated and analyzed to find the best solution. In particular, the reduction-based method is considered as a common approach to handle the data aggregation and analysis for the calculated particle information. Nevertheless, based on our analysis, the reduction-based method would suffer from excessive memory accesses and thread synchronization overheads. In this paper, we propose a novel algorithm to alleviate the above overheads with the atomic functions. The threads within a thread group update the calculated results atomically to the intra-group data queue conditionally, which prevents the frequent accesses to the memory as done by the parallel reduction operations. Furthermore, we develop an enhanced version of the algorithm to alleviate the synchronization barrier among the thread groups, which is achieved by allowing the thread groups to run asynchronously and updating to the global, lock-protected variables occasionally if necessary. Our experimental results show that our proposed algorithm running on the Nvidia GPU is about 200 times faster than the serial version executed by the Intel Xeon CPU. Moreover, the novel algorithm outperforms the state-of-the-art method (the parallel reduction approach) by a factor of 2.2.

关键词： Particle Swarm Optimization (PSO) parallel programming General Purpose Graphic Processing Unit (GPGPU) coalesced memory access Compute Unified Device Architecture (CUDA) cuRAND atomic function

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Run-of-Mine Stockyard Recovery Scheduling and Optimisation for Multiple Reclaimers 22

Run-of-Mine Stockyard Recovery Scheduling and Optimisation f...

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37th annual acm symposium on Applied Computing

作者： Assimi, Hirad Koch, Ben Garcia, Chris Wagner, Markus Neumann, Frank Univ Adelaide Sch Comp Sci Adelaide SA Australia EKA Software Solut Adelaide SA Australia

ISBN: (纸本)9781450387132

Stockpiles are essential in the mining value chain, assisting in maximising value and production. Quality control of taken minerals from the stockpiles is a major concern for stockpile managers where failure to meet some requirements can lead to losing money. This problem was recently investigated using a single reclaimer, and basic assumptions. This study extends the approach to consider multiple reclaimers in preparing for short and long-term deliveries. The engagement of multiple reclaimers complicates the problem in terms of their interaction in preparing a delivery simultaneously and safety distancing of reclaimers. We also consider more realistic settings, such as handling different minerals with different types of reclaimers. We propose methods that construct a solution step by step to meet precedence constraints for all reclaimers in the stockyard. We study various instances of the problem using greedy algorithms, Ant Colony Optimisation (ACO), and propose an integrated local search method determining an efficient schedule. We fine-tune and compare the algorithms and show that the ACO combined with local search can yield efficient solutions.

关键词： Stockpile Ant colony optimisation Greedy algorithm parallel processing Iterative local search

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annual acm symposium on parallelism in algorithms and architectures

Annual ACM Symposium on Parallelism in Algorithms and Archit...

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31st acm symposium on parallelism in algorithms and architectures, SPAA 2019

ISBN: (纸本)9781450361842

The proceedings contain 45 papers. The topics discussed include: the price of clustering in bin-packing with applications to bin-packing with delays;faster matrix multiplication via sparse decomposition;NC algorithms for computing a perfect matching, the number of perfect matchings, and a maximum flow in one-crossing-minor-free graphs;improved MPC algorithms for edit distance and ulam distance;brief announcement: scalable diversity maximization via small-size composable core-sets;brief announcement: eccentricities via parallel set cover;dynamic algorithms for the massively parallel computation model;massively parallel computation via remote memory access;and brief announcement: ultra-fast asynchronous randomized rumor spreading.

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A Time-Optimal Randomized parallel Algorithm for MIS 32

A Time-Optimal Randomized Parallel Algorithm for MIS

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32nd annual acm-SIAM symposium on Discrete algorithms (SODA)

作者： Ghaffari, Mohsen Haeupler, Bernhard Swiss Fed Inst Technol Zurich Switzerland Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9781611976465

We present a randomized parallel algorithm, in the Exclusive-Read Exclusive-Write (EREW) PRAM model, that computes a Maximal Independent Set (MIS) in O(log n) time and using O(m log(2) n) work, with high probability. Thus, MIS is an element of RNC1. This time complexity is optimal and it improves on the celebrated O(log(2) n) time algorithms of Luby [STOC'85] and Alon, Babai, and Itai [JALG'86], which had remained the state of the art for the past 35 years.

关键词： parallel algorithms

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Efficient parallel determinacy race detection for structured futures 21

Efficient parallel determinacy race detection for structured...

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33rd acm symposium on parallelism in algorithms and architectures, SPAA 2021

作者： Xu, Yifan Agrawal, Kunal Lee, I-Ting Angelina Washington University in St. Louis St. LouisMO United States

ISBN: (纸本)9781450380706

In task-parallel code, a determinancy race occurs when two logically parallel instructions access the same memory location in a conflicting way. A determinacy race tends to be a bug as it leads to non-deterministic program behaviors. Researchers have studied algorithms for detecting determinacy races in task-parallel code, with most prior work focuses on computations with nice structural properties (e.g., fork-join or pipeline parallelism). For such computations, one can devise provably efficient algorithms with constant overhead, leading to a asymptotically optimal running time - - O(T_1 / P + T_∞) on P cores for a computation with T_1 work and T_∞ span. More recently, researchers have begun to address the problem of race detecting computations with less structural properties, such as ones that arise from the use of futures. Due to the lack of structural properties, the race detection algorithm incurs higher overhead. % Given a computation with work T_1 and span T_∞, the state-of-the-art parallel algorithm for race detecting programs with futures runs in time O((T_1 lgk + k^2) / P + T_∞(lg k + lgr lgk )) on P cores (Xu γ•l, 2020), where k is the total number of futures used, k is the maximum number of future operations per "future task,'' and r is the maximum number of readers between two consecutive writes to a given memory location. Interestingly, it has been shown that when one imposes certain restrictions on the use of futures, referred to as the structured futures, although the restrictions do not entirely eliminate arbitrary dependences among subcomputations, one can race detect such programs more efficiently than that for programs with general futures (i.e., no restrictions). The improved efficiency has only been demonstrated for a sequential algorithm (Utterback γ•l, 2019) that race detects while executing the computation sequentially, however. The algorithm requires sequential execution, because the correctness of the algorithm relies on updating the neces

关键词： parallel algorithms

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String matching with wildcards in the massively parallel computation model 21

String matching with wildcards in the massively parallel com...

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33rd acm symposium on parallelism in algorithms and architectures, SPAA 2021

作者： Hajiaghayi, Mohammadtaghi Saleh, Hamed Seddighin, Saeed Sun, Xiaorui University of Maryland College ParkMD United States Toyota Technological Institute ChicagoIL United States University of Illinois ChicagoIL United States

ISBN: (纸本)9781450380706

We study distributed algorithms for string matching problem in presence of wildcard characters. Given a string T (a text), we look for all occurrences of another string P (a pattern) as a substring of string T. Each wildcard character in the pattern matches a specific class of strings based on its type. String matching is one of the most fundamental problems in computer science, especially in the fields of bioinformatics and machine learning. Persistent effort has led to a variety of algorithms for the problem since 1960s. With rise of big data and the inevitable demand to solve problems on huge data sets, there have been many attempts to adapt classic algorithms into the MPC framework to obtain further efficiency. MPC is a recent framework for parallel computation of big data, which is designed to capture the MapReduce-like algorithms. In this paper, we study the string matching problem in the MPC model and consider three types of wildcards: '?' wildcard: In this setting, the pattern is allowed to contain special '?' characters or don't cares that match any character of the text. String matching with don't cares could be solved by fast convolutions, and we give a constant round MPC algorithm for which by showing FFT could be implemented in a constant number of MPC rounds. '+' wildcard: '+' wildcard is a special character that allows for arbitrary repetitions of a character. When the pattern contains '+' wildcard characters, our algorithm runs in a constant number of MPC rounds by a reduction from subset matching problem. '∗' wildcard: '∗' is a special character that matches with any substring of the text. When '∗' is allowed in the pattern, we solve two special cases of the problem in logarithmic rounds. © 2021 acm.

关键词： Pattern matching

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Minimizing speculation overhead in a parallel recognizer for regular texts 25

Minimizing speculation overhead in a parallel recognizer for...

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Proceedings of the 30th acm SIGPLAN annual symposium on Principles and Practice of parallel Programming

作者： Angelo Borsotti Luca Breveglieri Angelo Morzenti Stefano Crespi Reghizzi Politecnico di Milano Milano Italy Politecnico di Milano and CNR-IEIIT Milano Italy

ISBN: (纸本)9798400714436

Speculative data-parallel algorithms for language recognition have been widely experimented for various types of finitestate automata (FA), deterministic (DFA) and nondeterministic (NFA), often derived fromregular expressions (RE). Such an algorithm cuts the input string into chunks, independently recognizes each chunk in parallel by means of identical FAs, and at last joins the chunk results and checks the overall consistency. In chunk recognition, it is necessary to speculatively start the FAs in any state, thus causing an overhead that reduces the speedup over a serial algorithm. The existing data-parallel DFA-based recognizers suffer from an excessive number of starting states, and the NFA-based ones suffer from the number of nondeterministic transitions.

关键词： data-parallel recognition algorithm

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Low-span parallel algorithms for the binary-forking model 21

Low-span parallel algorithms for the binary-forking model

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33rd acm symposium on parallelism in algorithms and architectures, SPAA 2021

作者： Ahmad, Zafar Chowdhury, Rezaul Das, Rathish Ganapathi, Pramod Gregory, Aaron Javanmard, Mohammad Mahdi Stony Brook University Stony BrookNY United States University of Waterloo WaterlooON Canada

ISBN: (纸本)9781450380706

The binary-forking model is a parallel computation model, formally defined by Blelloch et al., in which a thread can fork a concurrent child thread, recursively and asynchronously. The model incurs a cost of (log n) to spawn or synchronize n tasks or threads. The binary-forking model realistically captures the performance of parallel algorithms implemented using modern multithreaded programming languages on multicore shared-memory machines. In contrast, the widely studied theoretical PRAM model does not consider the cost of spawning and synchronizing threads, and as a result, algorithms achieving optimal performance bounds in the PRAM model may not be optimal in the binary-forking model. Often, algorithms need to be redesigned to achieve optimal performance bounds in the binary-forking model and the non-constant synchronization cost makes the task challenging. In this paper, we show that in the binary-forking model we can achieve optimal or near-optimal span with negligible or no asymptotic blowup in work for comparison-based sorting, Strassen's matrix multiplication (MM), and the Fast Fourier Transform (FFT). Our major results are as follows: (1) A randomized comparison-based sorting algorithm with optimal O(log n) span and O(nlog n) work, both w.h.p. in n. (2) An optimal O(log n) span algorithm for Strassen's matrix multiplication (MM) with only a loglog n -factor blow-up in work as well as a near-optimal O(log n loglog log n) span algorithm with no asymptotic blow-up in work. (3) A near-optimal O(log n logloglog n) span Fast Fourier Transform (FFT) algorithm with less than a log n-factor blow-up in work for all practical values of n (i.e., n le 10 ^10,000 ). © 2021 acm.

关键词： Fast Fourier transforms

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