scholarly journals Bottleneck identification and scheduling in multithreaded applications

2012 ◽  
Vol 47 (4) ◽  
pp. 223-234 ◽  
Author(s):  
José A. Joao ◽  
M. Aater Suleman ◽  
Onur Mutlu ◽  
Yale N. Patt
Keyword(s):  
Bottleneck Identification

scholarly journals Let Trajectories Speak Out the Traffic Bottlenecks

2022 ◽  
Vol 13 (1) ◽  
pp. 1-21
Author(s):  
Hui Luo ◽  
Zhifeng Bao ◽  
Gao Cong ◽  
J. Shane Culpepper ◽  
Nguyen Lu Dang Khoa
Keyword(s):  
Traffic Volume ◽  
Trajectory Data ◽  
Trade Offs ◽  
Bottleneck Identification ◽  
Spread Model ◽  
Efficiency And Effectiveness ◽  
Historical Trajectory ◽  
Algorithmic Approaches ◽  
Compare And Contrast ◽  
Road Segments

Traffic bottlenecks are a set of road segments that have an unacceptable level of traffic caused by a poor balance between road capacity and traffic volume. A huge volume of trajectory data which captures realtime traffic conditions in road networks provides promising new opportunities to identify the traffic bottlenecks. In this paper, we define this problem as trajectory-driven traffic bottleneck identification : Given a road network R , a trajectory database T , find a representative set of seed edges of size K of traffic bottlenecks that influence the highest number of road segments not in the seed set. We show that this problem is NP-hard and propose a framework to find the traffic bottlenecks as follows. First, a traffic spread model is defined which represents changes in traffic volume for each road segment over time. Then, the traffic diffusion probability between two connected segments and the residual ratio of traffic volume for each segment can be computed using historical trajectory data. We then propose two different algorithmic approaches to solve the problem. The first one is a best-first algorithm BF , with an approximation ratio of 1-1/ e . To further accelerate the identification process in larger datasets, we also propose a sampling-based greedy algorithm SG . Finally, comprehensive experiments using three different datasets compare and contrast various solutions, and provide insights into important efficiency and effectiveness trade-offs among the respective methods.

A Framework for Identifying Recurring Bottlenecks in the State Planning Process in Colorado

2018 ◽  
Vol 2672 (51) ◽  
pp. 20-30
Author(s):  
Oana Deselnicu ◽  
Jason Wallis
Keyword(s):  
Transportation Planning ◽  
Planning Process ◽  
The State ◽  
State Planning ◽  
Planning Time ◽  
Time Index ◽  
Bottleneck Identification ◽  
Different Types ◽  
Limited Usefulness

Traditional methods used in transportation planning to identify bottlenecks and mobility issues, such as the Volume-to-Capacity Ratio and the Planning Time Index, have limited usefulness in identifying the exact location and extent of bottlenecks. Moreover, existing bottleneck identification tools flag all bottleneck types without distinction, despite the fact that strategies and resources used to address each type are different. This research first proposes a taxonomy of congestion to distinguish between different types of bottlenecks. It then describes a new methodology for identifying the location of recurring bottlenecks. Recurring bottleneck locations must experience a reduction in speed, an upstream accumulation of vehicles, and recurrence at the same location over three consecutive months. The methodology is currently used in long-term transportation planning and project selection in Colorado to identify and address the most severe bottleneck locations in the state.

Revealing the Immune Population Hierarchy Using Novel Interactive Visualization Tools, a Comparison of Methods: Sunburst, viSNE, and Spade on Mass Cytometry Data

Blood ◽  
2014 ◽  
Vol 124 (21) ◽  
pp. 5122-5122
Author(s):  
Tiffany J Chen ◽  
Nikesh Kotecha
Keyword(s):  
New Technologies ◽  
Dimensional Space ◽  
Cell Populations ◽  
Employment Equity ◽  
Computational Tools ◽  
Bottleneck Identification ◽  
Equity Ownership ◽  
Visualization Tools ◽  
Immune Subset

Abstract The development of new technologies for high-parameter data has resulted in a critical bottleneck: identification of immune subsets is restricted to expert-based analysis, focusing on post-acquisition characterization of cell populations. Identification of cell subsets in flow cytometry has primarily focused on manual analysis, despite the fact that computational tools have proven useful for high-parameter and cross-sample comparisons. Sharing well-annotated data improves transparency and facilitates vital reproduction of results by external groups. Adoption of these new tools for immune subset discovery requires thorough collaborative investigation and validation of identified cell populations. To this end, in this study we compare the ease of discovery of immune subsets by comparing analysis through the use of three visualization tools: the sunburst hierarchy, the SPADE tree, and dimensionality reduction using viSNE. The sunburst hierarchy is a visual and interactive representation of traditional manual gating, whereas the SPADE tree is a semi-automated clustering and visualization tool for identification of cell subsets. viSNE allows interaction with high parameter data in the context of two-dimensional space where gating can be accomplished. In this study, we demonstrate the ability to automatically elucidate many immune subsets using Cytobank via an iterative analytic approach, combining computational tools (viSNE and SPADE) to recapitulate manually derived cell subsets. Disclosures Chen: Cytobank, Inc: Employment, Equity Ownership. Kotecha:Cytobank, Inc: Employment, Equity Ownership.

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