Donghyeon Cha
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Donghyeon Cha has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Dojin Choi
Changwon National University, Changwon, Republic of Korea
2 shared publications - Hyeonbyeong Lee
Mokpo National University, Mokpo, Republic of Korea
2 shared publications - Jaesoo Yoo
Chungbuk National University, Cheongju, Republic of Korea
2 shared publications - Jihyeon Choi
School of Information and Communication Engineering, Chungbuk National University Hospital, Chungbuk National University, Chungdae-ro 1, Seowon-gu, Cheongju 28644, Chungcheongbuk-do, Republic of Korea
2 shared publications - Jongtae Lim
Department of Information and Communication Engineering, Chungbuk National University, Seowon-gu, Cheongju-si, Chungcheongbuk-do, South Korea
2 shared publications - Kyoungsoo Bok
Wonkwang University, Iksan, Republic of Korea
2 shared publications
Latest Publications
- Open Access
- 14 March 2025
- Published by MDPI AG in Applied Sciences
- Vol. 15 (6) , 3172
Large-Scale Dynamic Graph Processing with Graphic Processing Unit-Accelerated Priority-Driven Differential Scheduling and Operation Reduction
Recently, there has been active research on utilizing GPUs for the efficient processing of large-scale dynamic graphs. However, challenges arise due to the repeated transmission and processing of identical data during dynamic graph operations. This paper proposes an efficient processing scheme for large-scale dynamic graphs in GPU environments with limited memory, leveraging dynamic scheduling and operation reduction. The proposed scheme partitions the dynamic graph and schedules each partition based on active and tentative active vertices, optimizing GPU utilization. Additionally, snapshots are employed to capture graph changes, enabling the detection of redundant edge and vertex modifications. This reduces unnecessary computations, thereby minimizing GPU workloads and data transmission costs. The scheme significantly enhances performance by eliminating redundant operations on the same edges or vertices. Performance evaluations demonstrate an average improvement of 280% over existing static graph processing techniques and 108% over existing dynamic graph processing schemes.
- 14 March 2025
- Published by MDPI AG in Applied Sciences
- Vol. 15 (6) , 3172
Open AccessLarge-Scale Dynamic Graph Processing with Graphic Processing Unit-Accelerated Priority-Driven Differential Scheduling and Operation Reduction
Recently, there has been active research on utilizing GPUs for the efficient processing of large-scale dynamic graphs. However, challenges arise due to the repeated transmission and processing of identical data during dynamic graph operations. This paper proposes an efficient processing scheme for large-scale dynamic graphs in GPU environments with limited memory, leveraging dynamic scheduling and operation reduction. The proposed scheme partitions the dynamic graph and schedules each partition based on active and tentative active vertices, optimizing GPU utilization. Additionally, snapshots are employed to capture graph changes, enabling the detection of redundant edge and vertex modifications. This reduces unnecessary computations, thereby minimizing GPU workloads and data transmission costs. The scheme significantly enhances performance by eliminating redundant operations on the same edges or vertices. Performance evaluations demonstrate an average improvement of 280% over existing static graph processing techniques and 108% over existing dynamic graph processing schemes.
- Open Access
- 11 February 2025
- Published by MDPI AG in Preprints.org
Large Dynamic Graph Processing with GPU-Accelerated Priority-Driven Differential Scheduling and Operation Reduction
Recently, there has been active research on utilizing GPUs for the efficient processing of large-scale dynamic graphs. However, challenges arise due to the repeated transmission and processing of identical data during dynamic graph operations. This paper proposes an efficient processing scheme for large-scale dynamic graphs in GPU environments with limited memory, leveraging dynamic scheduling and operation reduction. The proposed scheme partitions the dynamic graph and schedules each partition based on active and tentative active vertices, optimizing GPU utilization. Additionally, snapshots are employed to capture graph changes, enabling the detection of redundant edge and vertex modifications. This reduces unnecessary computations, thereby minimizing GPU workloads and data transmission costs. The scheme significantly enhances performance by eliminating redundant operations on the same edges or vertices. Performance evaluations demonstrate an average improvement of 280% over existing static graph processing techniques and 108% over existing dynamic graph processing schemes.
- 11 February 2025
- Published by MDPI AG in Preprints.org
Open AccessLarge Dynamic Graph Processing with GPU-Accelerated Priority-Driven Differential Scheduling and Operation Reduction
Recently, there has been active research on utilizing GPUs for the efficient processing of large-scale dynamic graphs. However, challenges arise due to the repeated transmission and processing of identical data during dynamic graph operations. This paper proposes an efficient processing scheme for large-scale dynamic graphs in GPU environments with limited memory, leveraging dynamic scheduling and operation reduction. The proposed scheme partitions the dynamic graph and schedules each partition based on active and tentative active vertices, optimizing GPU utilization. Additionally, snapshots are employed to capture graph changes, enabling the detection of redundant edge and vertex modifications. This reduces unnecessary computations, thereby minimizing GPU workloads and data transmission costs. The scheme significantly enhances performance by eliminating redundant operations on the same edges or vertices. Performance evaluations demonstrate an average improvement of 280% over existing static graph processing techniques and 108% over existing dynamic graph processing schemes.