scholarly journals A Parallel-Computing Approach for Vector Road-Network Matching Using GPU Architecture

2018 ◽  
Vol 7 (12) ◽  
pp. 472 ◽  
Author(s):  
Bo Wan ◽  
Lin Yang ◽  
Shunping Zhou ◽  
Run Wang ◽  
Dezhi Wang ◽  
...  
Keyword(s):  
Road Network ◽  
Large Scale ◽  
Graphics Processing Unit ◽  
Road Networks ◽  
Processing Unit ◽  
Data Partition ◽  
Matching Method ◽  
The Road ◽  
Central Processing ◽  
Relaxation Matching

The road-network matching method is an effective tool for map integration, fusion, and update. Due to the complexity of road networks in the real world, matching methods often contain a series of complicated processes to identify homonymous roads and deal with their intricate relationship. However, traditional road-network matching algorithms, which are mainly central processing unit (CPU)-based approaches, may have performance bottleneck problems when facing big data. We developed a particle-swarm optimization (PSO)-based parallel road-network matching method on graphics-processing unit (GPU). Based on the characteristics of the two main stages (similarity computation and matching-relationship identification), data-partition and task-partition strategies were utilized, respectively, to fully use GPU threads. Experiments were conducted on datasets with 14 different scales. Results indicate that the parallel PSO-based matching algorithm (PSOM) could correctly identify most matching relationships with an average accuracy of 84.44%, which was at the same level as the accuracy of a benchmark—the probability-relaxation-matching (PRM) method. The PSOM approach significantly reduced the road-network matching time in dealing with large amounts of data in comparison with the PRM method. This paper provides a common parallel algorithm framework for road-network matching algorithms and contributes to integration and update of large-scale road-networks.

scholarly journals A Hierarchical Matching Method for Vectorial Road Networks Using Delaunay Triangulation

2020 ◽  
Vol 9 (9) ◽  
pp. 509
Author(s):  
Zejun Zuo ◽  
Lin Yang ◽  
Xiaoya An ◽  
Wenjie Zhen ◽  
Haoyue Qian ◽  
...  
Keyword(s):  
Delaunay Triangulation ◽  
Road Network ◽  
Road Networks ◽  
Relaxation Method ◽  
Primary Objective ◽  
Coordinate Systems ◽  
Matching Method ◽  
Urban Road ◽  
Probabilistic Relaxation ◽  
Relaxation Matching

The primary objective of vectorial road network matching is to identify homonymous roads from two different data sources. Previous methods usually focus on matching road networks with the same coordinate system but rarely with different or unknown coordinate systems, which may lead to nontrivial and nonsystematic deviations (e.g., rotation angle) between homonymous objects. To fill this gap, this study proposes a novel hierarchical road network matching method based on Delaunay triangulation (DTRM). First, the entire urban road network is divided into three levels (L1, L2, L3) by using the principle of stroke. Then, the triangular meshes are constructed from L2, and the minimum matching unit (MMU) in the triangular mesh is used instead of the traditional “node-arc” unit to measure the similarity for the matching of L2. Lastly, a hierarchical matching solution integrating the probabilistic relaxation method and MMU similarity is yielded to identify the matching relationships of the three-level road network. Experiments conducted in Wuhan, China, and Auckland, New Zealand, show that the MMU similarity metrics can effectively calculate the similarity value with different rotation angles, and DTRM has higher precision than the benchmark probability-relaxation-matching method (PRM) and can correctly identify the most matching-relationships with an average accuracy of 89.63%. This study provides a matching framework for road networks with different or even unknown coordinate systems and contributes to the integration and updating of urban road networks.

scholarly journals NeuroGPU: Accelerating multi-compartment, biophysically detailed neuron simulations on GPUs

2019 ◽  
Author(s):  
Roy Ben-Shalom ◽  
Nikhil S. Artherya ◽  
Alexander Ladd ◽  
Christopher Cross ◽  
Hersh Sanghevi ◽  
...  
Keyword(s):  
Numerical Optimization ◽  
Large Scale ◽  
Graphics Processing Unit ◽  
Computing Time ◽  
Processing Unit ◽  
Neuron Models ◽  
Model Quality ◽  
Central Processing ◽  
Multi Scale ◽  
Graphics Processing

AbstractThe membrane potential of individual neurons depends on a large number of interacting biophysical processes operating on spatial-temporal scales spanning several orders of magnitude. The multi-scale nature of these processes dictates that accurate prediction of membrane potentials in specific neurons requires utilization of detailed simulations. Unfortunately, constraining parameters within biologically detailed neuron models can be difficult, leading to poor model fits. This obstacle can be overcome partially by numerical optimization or detailed exploration of parameter space. However, these processes, which currently rely on central processing unit (CPU) computation, often incur exponential increases in computing time for marginal improvements in model behavior. As a result, model quality is often compromised to accommodate compute resources. Here, we present a simulation environment, NeuroGPU, that takes advantage of the inherent parallelized structure of graphics processing unit (GPU) to accelerate neuronal simulation. NeuroGPU can simulate most of biologically detailed models 800x faster than traditional simulators when using multiple GPU cores, and even 10-200 times faster when implemented on relatively inexpensive GPU systems. We demonstrate the power of NeuoGPU through large-scale parameter exploration to reveal the response landscape of a neuron. Finally, we accelerate numerical optimization of biophysically detailed neuron models to achieve highly accurate fitting of models to simulation and experimental data. Thus, NeuroGPU enables the rapid simulation of multi-compartment, biophysically detailed neuron models on commonly used computing systems accessible by many scientists.

scholarly journals Critical Segments Identification for Link Travel Speed Prediction in Urban Road Network

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Xiaolei Ru ◽  
Xiangdong Xu ◽  
Yang Zhou ◽  
Chao Yang
Keyword(s):  
Information Acquisition ◽  
Prediction Accuracy ◽  
Road Network ◽  
Large Scale ◽  
Road Networks ◽  
Route Planning ◽  
Travel Speed ◽  
Traffic Information ◽  
Operational Conditions ◽  
The Road

Predicting traffic operational condition is crucial to urban transportation planning and management. A large variety of algorithms were proposed to improve the prediction accuracy. However, these studies were mainly based on complete data and did not discuss the vulnerability of massive data missing. And applications of these algorithms were in high-cost under the constraints of high quality of traffic data collecting in real-time on the large-scale road networks. This paper aims to deduce the traffic operational conditions of the road network with a small number of critical segments based on taxi GPS data in Xi’an city of China. To identify these critical segments, we assume that the states of floating cars within different road segments are correlative and mutually representative and design a heuristic algorithm utilizing the attention mechanism embedding in the graph neural network (GNN). The results show that the designed model achieves a high accuracy compared to the conventional method using only two critical segments which account for 2.7% in the road networks. The proposed method is cost-efficient which generates the critical segments scheme that reduces the cost of traffic information collection greatly and is more sensible without the demand for extremely high prediction accuracy. Our research has a guiding significance on cost saving of various information acquisition techniques such as route planning of floating car or sensors layout.

scholarly journals The carbon footprint of bioinformatics

2021 ◽  
Author(s):  
Jason Grealey ◽  
Loïc Lannelongue ◽  
Woei-Yuh Saw ◽  
Jonathan Marten ◽  
Guillaume Meric ◽  
...  
Keyword(s):  
Carbon Footprint ◽  
Large Scale ◽  
Graphics Processing Unit ◽  
Association Studies ◽  
Processing Unit ◽  
Genome Wide Association Studies ◽  
Central Processing ◽  
Bioinformatic Tools ◽  
Software Upgrades ◽  
Efficient Data

AbstractBioinformatic research relies on large-scale computational infrastructures which have a non-zero carbon footprint. So far, no study has quantified the environmental costs of bioinformatic tools and commonly run analyses. In this study, we estimate the bioinformatic carbon footprint (in kilograms of CO2 equivalent units, kgCO2e) using the freely available Green Algorithms calculator (www.green-algorithms.org). We assess (i) bioinformatic approaches in genome-wide association studies (GWAS), RNA sequencing, genome assembly, metagenomics, phylogenetics and molecular simulations, as well as (ii) computation strategies, such as parallelisation, CPU (central processing unit) vs GPU (graphics processing unit), cloud vs. local computing infrastructure and geography. In particular, for GWAS, we found that biobank-scale analyses emitted substantial kgCO2e and simple software upgrades could make GWAS greener, e.g. upgrading from BOLT-LMM v1 to v2.3 reduced carbon footprint by 73%. Switching from the average data centre to a more efficient data centres can reduce carbon footprint by ~34%. Memory over-allocation can be a substantial contributor to an algorithm’s carbon footprint. The use of faster processors or greater parallelisation reduces run time but can lead to, sometimes substantially, greater carbon footprint. Finally, we provide guidance on how researchers can reduce power consumption and minimise kgCO2e. Overall, this work elucidates the carbon footprint of common analyses in bioinformatics and provides solutions which empower a move toward greener research.

scholarly journals Modeling of Emergency Supply Scheduling Problem Based on Reliability and Its Solution Algorithm under Variable Road Network after Sudden-Onset Disasters

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-15 ◽  
Author(s):  
Tinggui Chen ◽  
Shiwen Wu ◽  
Jianjun Yang ◽  
Guodong Cong ◽  
Gongfa Li
Keyword(s):  
Road Network ◽  
Road Networks ◽  
Sudden Onset ◽  
The Road ◽  
Bee Colony ◽  
Proposed Model ◽  
Effectiveness And Efficiency ◽  
In Transit ◽  
Emergency Supply

It is common that many roads in disaster areas are damaged and obstructed after sudden-onset disasters. The phenomenon often comes with escalated traffic deterioration that raises the time and cost of emergency supply scheduling. Fortunately, repairing road network will shorten the time of in-transit distribution. In this paper, according to the characteristics of emergency supplies distribution, an emergency supply scheduling model based on multiple warehouses and stricken locations is constructed to deal with the failure of part of road networks in the early postdisaster phase. The detailed process is as follows. When part of the road networks fail, we firstly determine whether to repair the damaged road networks, and then a model of reliable emergency supply scheduling based on bi-level programming is proposed. Subsequently, an improved artificial bee colony algorithm is presented to solve the problem mentioned above. Finally, through a case study, the effectiveness and efficiency of the proposed model and algorithm are verified.

scholarly journals Numerical simulation of flattened heat pipe with double heat sources for CPU and GPU cooling application in laptop computers

2020 ◽  
Author(s):  
Wisoot Sanhan ◽  
Kambiz Vafai ◽  
Niti Kammuang-Lue ◽  
Pradit Terdtoon ◽  
Phrut Sakulchangsatjatai
Keyword(s):  
Experimental Data ◽  
Heat Pipe ◽  
Graphics Processing Unit ◽  
Processing Unit ◽  
Heat Sources ◽  
Final Thickness ◽  
Laptop Computers ◽  
Central Processing ◽  
Graphics Processing ◽  
Good Agreement

Abstract An investigation of the effect of the thermal performance of the flattened heat pipe on its double heat sources acting as central processing unit and graphics processing unit in laptop computers is presented in this work. A finite element method is used for predicting the flattening effect of the heat pipe. The cylindrical heat pipe with a diameter of 6 mm and the total length of 200 mm is flattened into three final thicknesses of 2, 3, and 4 mm. The heat pipe is placed under a horizontal configuration and heated with heater 1 and heater 2, 40 W in combination. The numerical model shows good agreement compared with the experimental data with the standard deviation of 1.85%. The results also show that flattening the cylindrical heat pipe to 66.7 and 41.7% of its original diameter could reduce its normalized thermal resistance by 5.2%. The optimized final thickness or the best design final thickness for the heat pipe is found to be 2.5 mm.

scholarly journals Road Networks Winter Risk Estimation Using On-Board Uncooled Infrared Camera for Surface Temperature Measurements over Two Lanes

2011 ◽  
Vol 2011 ◽  
pp. 1-8 ◽  
Author(s):  
M. Marchetti ◽  
M. Moutton ◽  
S. Ludwig ◽  
L. Ibos ◽  
V. Feuillet ◽  
...  
Keyword(s):  
Data Analysis ◽  
Road Network ◽  
Risk Estimation ◽  
Risk Index ◽  
Road Networks ◽  
Infrared Camera ◽  
Atmospheric Parameters ◽  
The Road ◽  
Measurement Rate

Thermal mapping has been implemented since the late eighties to establish the susceptibility of road networks to ice occurrence with measurements from a radiometer and some atmospheric parameters. They are usually done before dawn during wintertime when the road energy is dissipated. The objective of this study was to establish if an infrared camera could improve the determination of ice road susceptibility, to build a new winter risk index, to improve the measurements rate, and to analyze its consistency with seasons and infrastructures environment. Data analysis obtained from the conventional approved radiometer sensing technique and the infrared camera has shown great similarities. A comparison was made with promising perspectives. The measurement rate to analyse a given road network could be increased by a factor two.

scholarly journals A lightweight approach to performance portability with targetDP

2016 ◽  
Vol 32 (2) ◽  
pp. 288-301
Author(s):  
Alan Gray ◽  
Kevin Stratford
Keyword(s):  
Particle Physics ◽  
Message Passing ◽  
Graphics Processing Units ◽  
High Performance ◽  
Large Scale ◽  
Message Passing Interface ◽  
Graphics Processing Unit ◽  
Processing Unit ◽  
Performance Portability ◽  
Graphics Processing

Leading high performance computing systems achieve their status through use of highly parallel devices such as NVIDIA graphics processing units or Intel Xeon Phi many-core CPUs. The concept of performance portability across such architectures, as well as traditional CPUs, is vital for the application programmer. In this paper we describe targetDP, a lightweight abstraction layer which allows grid-based applications to target data parallel hardware in a platform agnostic manner. We demonstrate the effectiveness of our pragmatic approach by presenting performance results for a complex fluid application (with which the model was co-designed), plus separate lattice quantum chromodynamics particle physics code. For each application, a single source code base is seen to achieve portable performance, as assessed within the context of the Roofline model. TargetDP can be combined with Message Passing Interface (MPI) to allow use on systems containing multiple nodes: we demonstrate this through provision of scaling results on traditional and graphics processing unit-accelerated large scale supercomputers.

Make More Connections: Urban Traffic Flow Forecasting with Spatiotemporal Adaptive Gated Graph Convolution Network

2022 ◽  
Vol 13 (2) ◽  
pp. 1-25
Author(s):  
Bin Lu ◽  
Xiaoying Gan ◽  
Haiming Jin ◽  
Luoyi Fu ◽  
Xinbing Wang ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Road Network ◽  
Large Scale ◽  
Transportation Systems ◽  
Urban Traffic ◽  
Propagation Mechanism ◽  
Urban Road ◽  
The Road ◽  
Traffic Flow Forecasting ◽  
Gating Mechanism

Urban traffic flow forecasting is a critical issue in intelligent transportation systems. Due to the complexity and uncertainty of urban road conditions, how to capture the dynamic spatiotemporal correlation and make accurate predictions is very challenging. In most of existing works, urban road network is often modeled as a fixed graph based on local proximity. However, such modeling is not sufficient to describe the dynamics of the road network and capture the global contextual information. In this paper, we consider constructing the road network as a dynamic weighted graph through attention mechanism. Furthermore, we propose to seek both spatial neighbors and semantic neighbors to make more connections between road nodes. We propose a novel Spatiotemporal Adaptive Gated Graph Convolution Network ( STAG-GCN ) to predict traffic conditions for several time steps ahead. STAG-GCN mainly consists of two major components: (1) multivariate self-attention Temporal Convolution Network ( TCN ) is utilized to capture local and long-range temporal dependencies across recent, daily-periodic and weekly-periodic observations; (2) mix-hop AG-GCN extracts selective spatial and semantic dependencies within multi-layer stacking through adaptive graph gating mechanism and mix-hop propagation mechanism. The output of different components are weighted fused to generate the final prediction results. Extensive experiments on two real-world large scale urban traffic dataset have verified the effectiveness, and the multi-step forecasting performance of our proposed models outperforms the state-of-the-art baselines.

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