Dynamic Graphs on the GPU

Awad, Muhammad A.; Ashkiani, Saman; Porumbescu, Serban D.; Owens, John D.

doi:10.1109/ipdps47924.2020.00081

Cited by 22 publications

(10 citation statements)

References 6 publications

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“…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. The system achieves speedups between 5.8 to 14.8 compared to Hornet and 3.4 to 5.4 compared to faimGraph for batched edge insertions (and slightly smaller speedups for batched edge deletions).…”

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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Section: Dynamic Graph Systemsmentioning

confidence: 99%

Recent Advances in Fully Dynamic Graph Algorithms

Hanauer¹,

Henzinger²,

Schulz³

2021

Preprint

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“…The result is increased warp efficiency due to better load balance when compared to traditional per-thread work assignment and processing, where branch and memory divergence may significantly inhibit high performance. Such an approach has been previously used in high-performance hash tables [Ashkiani et al 2018] and graph data structures [Awad et al 2020] on the GPU.…”

Section: Queriesmentioning

confidence: 99%

“…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

Self Cite

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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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“…In addition to the libraries and frameworks summarized in Section 2.1, LLAMA [31] uses a data structure that resembles variable block linked lists to store entries of a dynamic graph's adjacency matrix, except that each block can store entries corresponding to multiple rows. There are also various GPU libraries and frameworks that store dynamic graphs using variants of either the data structures shown in Figure 3 [3,52] or other data structures that can be expressed using our proposed abstractions [10]. Furthermore, a number of works [20,43] have explored using array-based data structures, including packed memory arrays [35,44], to store dynamic graphs.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Sparse Tensor Algebra Compilation

Chou¹,

Amarasinghe²

2021

Preprint

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This paper shows how to generate efficient tensor algebra code that compute on dynamic sparse tensors, which have sparsity structures that evolve over time. We propose a language for precisely specifying recursive, pointer-based data structures, and we show how this language can express a wide range of dynamic data structures that support efficient modification, such as linked lists, binary search trees, and B-trees. We then describe how, given high-level specifications of such data structures, a compiler can generate code to efficiently iterate over and compute with dynamic sparse tensors that are stored in the aforementioned data structures. Furthermore, we define an abstract interface that captures how nonzeros can be inserted into dynamic data structures, and we show how this abstraction guides a compiler to emit efficient code that store the results of sparse tensor algebra computations in dynamic data structures.We evaluate our technique and find that it generates efficient dynamic sparse tensor algebra kernels. Code that our technique emits to compute the main kernel of the PageRank algorithm is 1.05× as fast as Aspen, a state-of-the-art dynamic graph processing framework. Furthermore, our technique outperforms PAM, a parallel ordered (key-value) maps library, by 7.40× when used to implement element-wise addition of a dynamic sparse matrix to a static sparse matrix.

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Dynamic Graphs on the GPU

Cited by 22 publications

References 6 publications

Recent Advances in Fully Dynamic Graph Algorithms

Recent Advances in Fully Dynamic Graph Algorithms

RXMesh

Dynamic Sparse Tensor Algebra Compilation

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