Efficient Dynamic MaxFlow Computation on GPUs Article Swipe
Maxflow is a fundamental problem in graph theory and combinatorial optimisation, used to determine the maximum flow from a source node to a sink node in a flow network. It finds applications in diverse domains, including computer networks, transportation, and image segmentation. The core idea is to maximise the total flow across the network without violating capacity constraints on edges and ensuring flow conservation at intermediate nodes. The rapid growth of unstructured and semi-structured data has motivated the development of parallel solutions to compute MaxFlow. However, due to the higher computational complexity, computing Maxflow for real-world graphs is time-consuming in practice. In addition, these graphs are dynamic and constantly evolve over time. In this work, we propose two Push-Relabel based algorithms for processing dynamic graphs on GPUs. The key novelty of our algorithms is their ability to efficiently handle both increments and decrements in edge capacities together when they appear in a batch. We illustrate the efficacy of our algorithms with a suite of real-world graphs. Overall, we find that for small updates, dynamic recomputation is significantly faster than a static GPU-based Maxflow.
Related Topics
Concepts
Computer science
Computation
Suite
Theoretical computer science
Maximum flow problem
Node (physics)
Flow network
Novelty
Graph
Dynamic programming
Minimum-cost flow problem
Key (lock)
Algorithm
Graph algorithms
Graph theory
Flow (mathematics)
Enhanced Data Rates for GSM Evolution
Distributed computing
Parallel computing
Control flow graph
Graph partition
Dynamic network analysis
Graph drawing
Data flow diagram
Graph Layout
Network topology
Core (optical fiber)
Data-flow analysis
Dynamic problem
Metadata
- Type
- article
- Landing Page
- http://arxiv.org/abs/2511.05895
- https://arxiv.org/pdf/2511.05895
- OA Status
- green
- OpenAlex ID
- https://openalex.org/W7105506213
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W7105506213Canonical identifier for this work in OpenAlex
- Title
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Efficient Dynamic MaxFlow Computation on GPUsWork title
- Type
-
articleOpenAlex work type
- Publication year
-
2025Year of publication
- Publication date
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2025-11-08Full publication date if available
- Authors
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Kannappan, Shruthi, Kumar, Ashwina, Nasre, RupeshList of authors in order
- Landing page
-
https://arxiv.org/abs/2511.05895Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2511.05895Direct link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2511.05895Direct OA link when available
- Concepts
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Computer science, Computation, Suite, Theoretical computer science, Maximum flow problem, Node (physics), Flow network, Novelty, Graph, Dynamic programming, Minimum-cost flow problem, Key (lock), Algorithm, Graph algorithms, Graph theory, Flow (mathematics), Enhanced Data Rates for GSM Evolution, Distributed computing, Parallel computing, Control flow graph, Graph partition, Dynamic network analysis, Graph drawing, Data flow diagram, Graph Layout, Network topology, Core (optical fiber), Data-flow analysis, Dynamic problemTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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| abstract_inverted_index.updates, | 172 |
| abstract_inverted_index.GPU-based | 181 |
| abstract_inverted_index.addition, | 102 |
| abstract_inverted_index.computing | 92 |
| abstract_inverted_index.determine | 13 |
| abstract_inverted_index.including | 35 |
| abstract_inverted_index.motivated | 76 |
| abstract_inverted_index.networks, | 37 |
| abstract_inverted_index.practice. | 100 |
| abstract_inverted_index.solutions | 81 |
| abstract_inverted_index.violating | 55 |
| abstract_inverted_index.algorithms | 120, 132, 159 |
| abstract_inverted_index.capacities | 145 |
| abstract_inverted_index.constantly | 108 |
| abstract_inverted_index.decrements | 142 |
| abstract_inverted_index.illustrate | 154 |
| abstract_inverted_index.increments | 140 |
| abstract_inverted_index.processing | 122 |
| abstract_inverted_index.real-world | 95, 164 |
| abstract_inverted_index.complexity, | 91 |
| abstract_inverted_index.constraints | 57 |
| abstract_inverted_index.development | 78 |
| abstract_inverted_index.efficiently | 137 |
| abstract_inverted_index.fundamental | 3 |
| abstract_inverted_index.Push-Relabel | 118 |
| abstract_inverted_index.applications | 31 |
| abstract_inverted_index.conservation | 63 |
| abstract_inverted_index.intermediate | 65 |
| abstract_inverted_index.unstructured | 71 |
| abstract_inverted_index.combinatorial | 9 |
| abstract_inverted_index.computational | 90 |
| abstract_inverted_index.optimisation, | 10 |
| abstract_inverted_index.recomputation | 174 |
| abstract_inverted_index.segmentation. | 41 |
| abstract_inverted_index.significantly | 176 |
| abstract_inverted_index.time-consuming | 98 |
| abstract_inverted_index.semi-structured | 73 |
| abstract_inverted_index.transportation, | 38 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.81421852 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |