faimGraph: High Performance Management of Fully-Dynamic Graphs Under Tight Memory Constraints on the GPU

Winter, Martin; Mlakar, Daniel; Zayer, Rhaleb; Seidel, Hans‐Peter; Steinberger, Markus

doi:10.1109/sc.2018.00063

Cited by 32 publications

(11 citation statements)

References 14 publications

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“…This paper presented a linear algebraic solution for the "Social Media" case study of the 2018 Transformation Tool Contest. While the presented solution already exhibits good performance and scalability compared to the reference implementation, a number of optimizations could be applied as future work: (1) using updatable compressed matrix representation formats such as faimGraph [10] or Hornet [2] and (2) running an incremental connected components algorithm [4] in Step 8 of Q2. Additionally, it would be interesting to investigate the performance of the solution in the presence of more realistic update operations, including both insertions and removals.…”

Section: Discussionmentioning

confidence: 99%

An incremental GraphBLAS solution for the 2018 TTC Social Media case study

Elekes

Szárnyas

2020

2020 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)

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Graphs are increasingly important for modelling and analysing connected data sets. Traditionally, graph analytical tools targeted global fixed-point computations, while graph databases focused on simpler transactional read operations such as retrieving the neighbours of a node. However, recent applications of graph processing (such as financial fraud detection and serving personalized recommendations) often necessitate a mix of the two workload profiles. A potential approach to tackle these complex workloads is to formulate graph algorithms in the language of linear algebra. To this end, the recent GraphBLAS standard defines a linear algebraic graph computational model and an API for implementing such algorithms. To investigate its usability and efficiency, we have implemented a GraphBLAS solution for the "Social Media" case study of the 2018 Transformation Tool Contest. This paper presents our solution along with an incrementalized variant to improve its runtime for repeated evaluations. Preliminary results show that the GraphBLAS-based solution is competitive but implementing it requires significant development efforts.

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Section: Discussionmentioning

confidence: 99%

An incremental GraphBLAS solution for the 2018 TTC Social Media case study

Elekes

Szárnyas

2020

2020 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)

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“…Moreover, Hornet uses a factor of 5 to 10 less memory than cuSTINGER. In contrast to previous approaches, faimGraph due to Winter et al [245] is able to deal with a changing number of vertices. Awad et al [18] noted that the experiments performed by Busato et al are missing true dynamism that is expected in real world scenarios and proposed a dynamic graph structure that uses one hash table per vertex to store adjacency lists.…”

Section: Dynamic Graph Systemsmentioning

confidence: 99%

Recent Advances in Fully Dynamic Graph Algorithms

Hanauer¹,

Henzinger²,

Schulz³

2021

Preprint

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In recent years, significant advances have been made in the design and analysis of fully dynamic algorithms. However, these theoretical results have received very little attention from the practical perspective. Few of the algorithms are implemented and tested on real datasets, and their practical potential is far from understood. Here, we survey recent engineering and theory results in the area of fully dynamic graph algorithms.

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“…Nonetheless we believe that our data structure, with its focus on mesh partitioning, is well suited for an evolution toward dynamic capability because parallelizing across partitions helps ease the problems with concurrency. We are further encouraged by recent advances in mesh subdivision on the GPU [Mlakar et al 2020] and dynamic GPU graph data structures [Awad et al 2020;Winter et al 2018].…”

Section: Limitations and Future Workmentioning

confidence: 99%

RXMesh

2021

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We propose a new static high-performance mesh data structure for triangle surface meshes on the GPU. Our data structure is carefully designed for parallel execution while capturing mesh locality and confining data access, as much as possible, within the GPU's fast "shared memory." We achieve this by subdividing the mesh into patches and representing these patches compactly using a matrix-based representation. Our patching technique is decorated with ribbons , thin mesh strips around patches that eliminate the need to communicate between different computation thread blocks, resulting in consistent high throughput. We call our data structure RXMesh : Ribbon-matriX Mesh. We hide the complexity of our data structure behind a flexible but powerful programming model that helps deliver high performance by inducing load balance even in highly irregular input meshes. We show the efficacy of our programming model on common geometry processing applications---mesh smoothing and filtering, geodesic distance, and vertex normal computation. For evaluation, we benchmark our data structure against well-optimized GPU and (single and multi-core) CPU data structures and show significant speedups.

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faimGraph: High Performance Management of Fully-Dynamic Graphs Under Tight Memory Constraints on the GPU

Cited by 32 publications

References 14 publications

An incremental GraphBLAS solution for the 2018 TTC Social Media case study

An incremental GraphBLAS solution for the 2018 TTC Social Media case study

Recent Advances in Fully Dynamic Graph Algorithms

RXMesh

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