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bibtex.online2025
4.
윤석현,; 정재윤,; 안성배,; 오상윤,
효율적 동적그래프 처리를 위한 중요도 기반 Event 선별 기법🇰🇷 DomesticConference
2025년도 한국통신학회 동계종합학술발표회, 한국통신학회, 2025.
@conference{KICS-Winter-Conference-2025,
title = {효율적 동적그래프 처리를 위한 중요도 기반 Event 선별 기법},
author = {윤석현 and 정재윤 and 안성배 and 오상윤},
year = {2025},
date = {2025-02-06},
urldate = {2025-02-06},
booktitle = {2025년도 한국통신학회 동계종합학술발표회, 한국통신학회},
keywords = {graph},
pubstate = {published},
tppubtype = {conference}
}
2022
3.
Yoon, Daegun; Jeong, Minjoong; Oh, Sangyoon
WAVE: designing a heuristics-based three-way breadth-first search on GPUs🌏 InternationalJournal Article
In: The Journal of Supercomputing, 2022, (2).
Abstract | Links | BibTeX | 태그: breath-first search, direction-optimizing BFS, GPU, graph, push-pull
@article{Yoon2022WAVE,
title = {WAVE: designing a heuristics-based three-way breadth-first search on GPUs},
author = {Daegun Yoon and Minjoong Jeong and Sangyoon Oh},
doi = {10.1007/s11227-022-04934-1},
year = {2022},
date = {2022-11-17},
urldate = {2022-11-17},
journal = {The Journal of Supercomputing},
abstract = {Breadth-first search (BFS) is a building block for improving the performance of many iterative graph algorithms. In addition to conventional BFS (push), a novel method that traverses a graph in the reverse direction (pull) has emerged and gained popularity because of its enhanced processing performance. Several frameworks have recently used a hybrid approach known as direction-optimizing BFS, which utilizes both directions. However, these frameworks are mostly interested in optimizing the procedure in each direction, instead of designing sophisticated methods for determining the appropriate direction between push and pull at each iteration. Owing to the lack of in-depth discussion on this decision, state-of-the-art direction-optimizing BFS algorithms cannot demonstrate their comprehensive performance despite improvements in the design of each direction because they select ineffective directions at each iteration. We identified that the current frameworks suffer from high computational overheads for their decisions and make decisions that are overfitted to several graph datasets used for tuning their direction selection process. Based on observations from state-of-the-art limitations, we designed a direction-optimizing method for BFS called WAVE. WAVE minimizes the computational overhead to near zero and makes more appropriate direction selection decisions than the state-of-the-art heuristics based on the characteristics extracted from the input graph dataset. In our experiments on 20 graph benchmarks, WAVE achieved speedups of up to 4.95×, 5.79×, 46.49×, and 149.67× over Enterprise, Gunrock, Tigr, and CuSha, respectively.},
note = {2},
keywords = {breath-first search, direction-optimizing BFS, GPU, graph, push-pull},
pubstate = {published},
tppubtype = {article}
}
Breadth-first search (BFS) is a building block for improving the performance of many iterative graph algorithms. In addition to conventional BFS (push), a novel method that traverses a graph in the reverse direction (pull) has emerged and gained popularity because of its enhanced processing performance. Several frameworks have recently used a hybrid approach known as direction-optimizing BFS, which utilizes both directions. However, these frameworks are mostly interested in optimizing the procedure in each direction, instead of designing sophisticated methods for determining the appropriate direction between push and pull at each iteration. Owing to the lack of in-depth discussion on this decision, state-of-the-art direction-optimizing BFS algorithms cannot demonstrate their comprehensive performance despite improvements in the design of each direction because they select ineffective directions at each iteration. We identified that the current frameworks suffer from high computational overheads for their decisions and make decisions that are overfitted to several graph datasets used for tuning their direction selection process. Based on observations from state-of-the-art limitations, we designed a direction-optimizing method for BFS called WAVE. WAVE minimizes the computational overhead to near zero and makes more appropriate direction selection decisions than the state-of-the-art heuristics based on the characteristics extracted from the input graph dataset. In our experiments on 20 graph benchmarks, WAVE achieved speedups of up to 4.95×, 5.79×, 46.49×, and 149.67× over Enterprise, Gunrock, Tigr, and CuSha, respectively.
2.
백민석,; 오상윤,
하둡 맵리듀스와 페이지 랭크를 이용한 서울시 대중 교통 인구 이동 분석 🇰🇷 DomesticConference 📃 In press
추계학술대회 Annual Conference of KIPS (ACK 2022), 한국정보처리학회 2022, (우수논문상).
Links | BibTeX | 태그: graph, Hadoop MapReduce
@conference{백민석2022하둡,
title = {하둡 맵리듀스와 페이지 랭크를 이용한 서울시 대중 교통 인구 이동 분석 },
author = {백민석 and 오상윤},
url = {https://kiss.kstudy.com/Detail/Ar?key=3988407},
year = {2022},
date = {2022-11-04},
urldate = {2022-11-04},
booktitle = {추계학술대회 Annual Conference of KIPS (ACK 2022)},
organization = {한국정보처리학회 },
note = {우수논문상},
keywords = {graph, Hadoop MapReduce},
pubstate = {published},
tppubtype = {conference}
}
2021
1.
Yoon, Daegun; Oh, Sangyoon
Traversing Large Road Networks on GPUs with Breadth-First Search🌏 InternationalConference 📃 In press
The 7th International Conference on Next Generation Computing, 2021.
Abstract | BibTeX | 태그: breath-first search, graph, graphics processing units, road network
@conference{Yoon2021Traversing,
title = {Traversing Large Road Networks on GPUs with Breadth-First Search},
author = {Daegun Yoon and Sangyoon Oh},
year = {2021},
date = {2021-11-05},
urldate = {2021-11-05},
booktitle = {The 7th International Conference on Next Generation Computing},
journal = {The 7th International Conference on Next Generation Computing 2021},
abstract = {Breadth-first search (BFS) is one of the most used graph kernels, and substantially affects the overall performance when processing various graphs. Since graph data are frequently used in real life for example road networks in navigation systems, high performance graph processing becomes more critical. In this study, we aim to process BFS algorithm efficiently on road network data. We propose BARON, a BFS algorithm that copes with road networks. To accelerate graph traversal, BARON reduce the occurrence of branch and memory divergences by exploiting warp-cooperative work sharing and atomic operations. With this design approach, BARON outperforms the other BFS kernels of state-of-the-art graph processing frameworks executed stably on the latest GPU architectures. For various graphs, BARON yields speedups of up to 2.88x and 5.43x over Gunrock and CuSha, respectively.},
keywords = {breath-first search, graph, graphics processing units, road network},
pubstate = {published},
tppubtype = {conference}
}
Breadth-first search (BFS) is one of the most used graph kernels, and substantially affects the overall performance when processing various graphs. Since graph data are frequently used in real life for example road networks in navigation systems, high performance graph processing becomes more critical. In this study, we aim to process BFS algorithm efficiently on road network data. We propose BARON, a BFS algorithm that copes with road networks. To accelerate graph traversal, BARON reduce the occurrence of branch and memory divergences by exploiting warp-cooperative work sharing and atomic operations. With this design approach, BARON outperforms the other BFS kernels of state-of-the-art graph processing frameworks executed stably on the latest GPU architectures. For various graphs, BARON yields speedups of up to 2.88x and 5.43x over Gunrock and CuSha, respectively.