An adaptive breadth-first search algorithm on integrated architectures

Zhang, Feng; Lin, Hua; Zhai, Jidong; Cheng, Jie; Xiang, Dingyi; Li, Jizhong; Chai, Yunpeng; Du, Xiaoyong

doi:10.1007/s11227-018-2525-0

Cited by 21 publications

(10 citation statements)

References 42 publications

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“…6) Choosing a single mobile robot to represent only one node mass. 2-A star search [20] 5-Breadth First Search [21] 3-Bidirectional A Star [22] 6-Depth First Search [23] Several algorithms are related to informed algorithms. In this paper, the most common algorithms were selected, which are Dijkstra, A star, and Bidirectional A star.…”

Section: A Path Planning Problemmentioning

confidence: 99%

Comparison of Path Planning between Improved Informed and Uninformed Algorithms for Mobile Robot

Amr,

Bahgat,

Rashad

et al. 2022

IJACSA

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This work is concerned with the Path Planning Algorithms (PPA), which hold an important place in Robotics navigation. Navigation has become indispensable to most modern inventions. Mobile robots have to move to a relevant task point in order to achieve the tasks assigned to them. The actions, which are planned in a structure, may restrict the task duration and even in some situations, the mission tends to be accomplished. This paper aims to study and compare six commonly used informed and uninformed algorithms. Three different maps have been created with gradually increasing difficulty levels related to a number of obstacles in the tested maps. The paper provides a detailed comparison between the algorithms under investigation of several parameters such as: Total steps, straight steps, rotation steps, and search time. The promised results were obtained when the proposed algorithms were applied to a case study.

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Section: A Path Planning Problemmentioning

confidence: 99%

Comparison of Path Planning between Improved Informed and Uninformed Algorithms for Mobile Robot

Amr,

Bahgat,

Rashad

et al. 2022

IJACSA

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show abstract

“…Thus, we adopt different strategies and the application scenarios are not the same. Some researchers have used GPUs for graph processing [7], [8], [9], [10], [11], [12], [52]. Sha et al [7] proposed a GPU-based dynamic graph storage scheme to support existing graph algorithms.…”

Section: Related Workmentioning

confidence: 99%

Automatic Irregularity-Aware Fine-Grained Workload Partitioning on Integrated Architectures

Zhang

Zhai

et al. 2019

IEEE Trans. Knowl. Data Eng.

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The integrated architecture that features both CPU and GPU on the same die is an emerging and promising architecture for fine-grained CPU-GPU collaboration. However, the integration also brings forward several programming and system optimization challenges, especially for irregular applications such as graph processing. The complex interplay between heterogeneity and irregularity leads to very low processor utilization of running irregular applications on integrated architectures. Furthermore, fine-grained co-processing on the CPU and GPU is still an open problem. Particularly, in this paper, we show that the previous workload partitioning for CPU-GPU co-processing is far from ideal in terms of resource utilization and performance. To solve this problem, we propose a system software called FinePar, which considers architectural differences of the CPU and GPU and leverages fine-grained collaboration enabled by integrated architectures. Through irregularity-aware performance modeling and online auto-tuning, FinePar partitions irregular workloads and achieves both device-level and thread-level load balance. We evaluate FinePar with eight irregular applications in graphs and sparse matrices on two integrated architectures and compare it with state-of-the-art partitioning approaches. Results show that FinePar demonstrates better resource utilization and achieves an average of 1.6X speedup over the optimal coarse-grained partitioning method.

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“…Daga et al (2014) proposed a hybrid BFS algorithm that can select appropriate algorithm and devices for iterations on heterogeneous processors. Zhang et al (2018) further developed a performance model for BFS algorithm on heterogeneous processors.…”

Section: Accelerating Irregular Applications On Heterogeneous Processorsmentioning

confidence: 99%

“…Daga et al on AMD heterogeneous processors evaluated several kernels and applications (Daga et al 2011), and optimized B+ tree search (Daga and Nutter 2012) and breadth-first search (BFS) (Daga et al 2014). Zhang et al (2018) developed faster 2 Background…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation and analysis of sparse matrix and graph kernels on heterogeneous processors

et al. 2019

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Heterogeneous processors integrate very distinct compute resources such as CPUs and GPUs into the same chip, thus can exploit the advantages and avoid disadvantages of those compute units. We in this work evaluate and analyze eight sparse matrix and graph kernels on an AMD CPU-GPU heterogeneous processor by using 956 sparse matrices. Five characteristics, i.e., load balancing, indirect addressing, memory reallocation, atomic operations, and dynamic characteristics are our major considerations. The experimental results show that although the CPU and GPU parts access the same DRAM, very different performance behaviors are observed. For example, though the GPU part in general outperforms the CPU part, it cannot achieve the best performance in all cases given by the CPU part. Moreover, the bandwidth utilization of atomic operations on heterogeneous processors can be much higher than a high-end discrete GPU.

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An adaptive breadth-first search algorithm on integrated architectures

Cited by 21 publications

References 42 publications

Comparison of Path Planning between Improved Informed and Uninformed Algorithms for Mobile Robot

Comparison of Path Planning between Improved Informed and Uninformed Algorithms for Mobile Robot

Automatic Irregularity-Aware Fine-Grained Workload Partitioning on Integrated Architectures

Performance evaluation and analysis of sparse matrix and graph kernels on heterogeneous processors

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