A Fast Parallel Particle Filter for Shared Memory Systems

Varsi, Alessandro; Taylor, Jack; Kekempanos, Lykourgos; Knapp, Edward; Maskell, Simon

doi:10.1109/lsp.2020.3014035

Cited by 18 publications

(5 citation statements)

References 19 publications

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“…i−1 ) in parallel across the C cores (we are also aware of techniques for implementing the multinomial sampling in parallel (eg see [16,17]) but have not found this component of the algorithm to be a computational bottleneck for the application considered herein). Once the algorithm has converged, we keep the samples drawn from the multinomial distribution.…”

Section: Sampling Using Multinomial Distribution (Sumd)mentioning

confidence: 99%

Parallel Approaches to Accelerate Bayesian Decision Trees

Drousiotis¹,

Spirakis²,

Maskell³

2023

Preprint

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Markov Chain Monte Carlo (MCMC) is a well-established family of algorithms primarily used in Bayesian statistics to sample from a target distribution when direct sampling is challenging. Existing work on Bayesian decision trees uses MCMC. Unforunately, this can be slow, especially when considering large volumes of data. It is hard to parallelise the accept-reject component of the MCMC. None-the-less, we propose two methods for exploiting parallelism in the MCMC: in the first, we replace the MCMC with another numerical Bayesian approach, the Sequential Monte Carlo (SMC) sampler, which has the appealing property that it is an inherently parallel algorithm; in the second, we consider data partitioning. Both methods use multi-core processing with a High-Performance Computing (HPC) resource. We test the two methods in various study settings to determine which method is the most beneficial for each test case. Experiments show that data partitioning has limited utility in the settings we consider and that the use of the SMC sampler can improve run-time (compared to the sequential implementation) by up to a factor of 343.

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Section: Sampling Using Multinomial Distribution (Sumd)mentioning

confidence: 99%

Parallel Approaches to Accelerate Bayesian Decision Trees

Drousiotis¹,

Spirakis²,

Maskell³

2023

Preprint

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“…where x k−1 and p k−1 are the initial position and momentum, and x k and −p k are the position and negative momentum after a NUTS iteration. The right numerator can be evaluated from either (13) or (15) and the right denominator can be evaluated from the initial momentum distribution. Algorithm 1 shows how the near-optimal L-kernel is used within SMC-NUTS for N samples over a total of T iterations.…”

Section: A Non-linear Transform Of the Proposal Distributionmentioning

confidence: 99%

“…Algorithm 3 in [5] can be used to generate new samples for the NUTS step. For the sub-optimal symmetric L-kernel, steps 10 and 11 are replaced by (13). For the resampling step, several methods may be employed, see [13] and references therein for a discussion on resampling in the context of particle filters (which may be applied here).…”

Section: A Non-linear Transform Of the Proposal Distributionmentioning

confidence: 99%

The No-U-Turn Sampler as a Proposal Distribution in a Sequential Monte Carlo Sampler with a Near-Optimal L-Kernel

Devlin,

Horridge,

Green

et al. 2021

Preprint

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Markov Chain Monte Carlo (MCMC) is a powerful method for drawing samples from non-standard probability distributions and is utilized across many fields and disciplines. Methods such as Metropolis-Adjusted Langevin (MALA) and Hamiltonian Monte Carlo (HMC), which use gradient information to explore the target distribution, are popular variants of MCMC. The Sequential Monte Carlo (SMC) sampler is an alternative sampling method which, unlike MCMC, can readily utilise parallel computing architectures and also has tuning parameters not available to MCMC. One such parameter is the L-kernel which can be used to minimise the variance of the estimates from an SMC sampler. In this letter, we show how the proposal used in the No-U-Turn Sampler (NUTS), an advanced variant of HMC, can be incorporated into an SMC sampler to improve the efficiency of the exploration of the target space. We also show how the SMC sampler can be optimized using both a near-optimal L-kernel and a Hamiltonian proposal.

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“…Localization is extremely important for autonomous vehicles (Lu et al, 2022), where it is necessary to determine the position of the vehicle for safe and efficient operation. Optimization of navigation algorithms and methods can contribute to environmental and economic development, as autonomous vehicles can reduce fuel costs and ensure efficient use of infrastructure (Varsi et al, 2021).…”

mentioning

confidence: 99%

“…2. Algorithmic Speed (Varsi et al, 2020): The swift execution of navigation tasks such as localization, route planning, and obstacle detection is imperative for autonomous cars. Parallel computing facilitates the distribution of tasks across numerous processors or cores, resulting in rapid responses and reduced information processing durations.…”

mentioning

confidence: 99%

Implementation and analysis of a parallel kalman filter algorithm for lidar localization based on CUDA technology

Mochurad

2024

Front. Robot. AI

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Introduction: Navigation satellite systems can fail to work or work incorrectly in a number of conditions: signal shadowing, electromagnetic interference, atmospheric conditions, and technical problems. All of these factors can significantly affect the localization accuracy of autonomous driving systems. This emphasizes the need for other localization technologies, such as Lidar.Methods: The use of the Kalman filter in combination with Lidar can be very effective in various applications due to the synergy of their capabilities. The Kalman filter can improve the accuracy of lidar measurements by taking into account the noise and inaccuracies present in the measurements.Results: In this paper, we propose a parallel Kalman algorithm in three-dimensional space to speed up the computational speed of Lidar localization. At the same time, the initial localization accuracy of the latter is preserved. A distinctive feature of the proposed approach is that the Kalman localization algorithm itself is parallelized, rather than the process of building a map for navigation. The proposed algorithm allows us to obtain the result 3.8 times faster without compromising the localization accuracy, which was 3% for both cases, making it effective for real-time decision-making.Discussion: The reliability of this result is confirmed by a preliminary theoretical estimate of the acceleration rate based on Ambdahl’s law. Accelerating the Kalman filter with CUDA for Lidar localization can be of significant practical value, especially in real-time and in conditions where large amounts of data from Lidar sensors need to be processed.

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A Fast Parallel Particle Filter for Shared Memory Systems

Cited by 18 publications

References 19 publications

Parallel Approaches to Accelerate Bayesian Decision Trees

Parallel Approaches to Accelerate Bayesian Decision Trees

The No-U-Turn Sampler as a Proposal Distribution in a Sequential Monte Carlo Sampler with a Near-Optimal L-Kernel

Implementation and analysis of a parallel kalman filter algorithm for lidar localization based on CUDA technology

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