Accelerating Graph Analytics by Utilising the Memory Locality of Graph Partitioning

Sun, Jiawen; Vandierendonck, Hans; Nikolopoulos, Dimitrios S.

doi:10.1109/icpp.2017.27

Cited by 11 publications

(20 citation statements)

References 31 publications

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“…Figure 1 shows the processing time for each of 384 partitions when executing one iteration of the PageRank algorithm. The graph is represented using the coordinate format (COO) and edges are sorted in the access order of a Hilbert space filling curve in order to improve memory locality [11], [12]. Each partition is processed sequentially by one thread.…”

Section: Motivationmentioning

confidence: 99%

VEBO: A Vertex- and Edge-Balanced Ordering Heuristic to Load Balance Parallel Graph Processing

Sun,

Vandierendonck,

Nikolopoulos

2018

Preprint

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Graph partitioning drives graph processing in distributed, disk-based and NUMA-aware systems. A commonly used partitioning goal is to balance the number of edges per partition in conjunction with minimizing the edge or vertex cut. While this type of partitioning is computationally expensive, we observe that such topology-driven partitioning nonetheless results in computational load imbalance.We propose Vertex-and Edge-Balanced Ordering (VEBO): balance the number of edges and the number of unique destinations of those edges. VEBO optimally balances edges and vertices for graphs with a powerlaw degree distribution. Experimental evaluation on three shared-memory graph processing systems (Ligra, Polymer and GraphGrind) shows that VEBO achieves excellent load balance and improves performance by 1.09x over Ligra, 1.41x over Polymer and 1.65x over GraphGrind, compared to their respective partitioning algorithms, averaged across 8 algorithms and 7 graphs.

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Section: Motivationmentioning

confidence: 99%

VEBO: A Vertex- and Edge-Balanced Ordering Heuristic to Load Balance Parallel Graph Processing

Sun,

Vandierendonck,

Nikolopoulos

2018

Preprint

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“…Starting from [18], blocking techniques have been widely used to achieve different goals. GraphGrind [34] and Graptor [38] apply vertical blocking in their push traversals in order to prevent race conditions made by concurrent updates.…”

Section: Blocking Strategiesmentioning

confidence: 99%

Exploiting in-Hub Temporal Locality in SpMV-based Graph Processing

Esfahani

Kilpatrick

Vandierendonck

2021

50th International Conference on Parallel Processing

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The skewed degree distribution of real-world graphs is the main source of poor locality in traversing all edges of the graph, known as Sparse Matrix-Vector (SpMV) Multiplication. Conventional graph traversal methods, such as push and pull, traverse all vertices in the same manner, and we show applying a uniform traversal direction for all edges leads to sub-optimal memory locality, hence poor efficiency. This paper argues that different vertices in power-law graphs have different locality characteristics and the traversal method should be adapted to these characteristics.To solve this problem, we propose to inspect the number of destination and source vertices in selecting a cachecompatible traversal direction for each type of vertex. We introduce in-Hub Temporal Locality (iHTL), a structure-aware SpMV that combines push and pull in one graph traversal, but for different vertex types. iHTL exploits temporal locality by traversing incoming edges to in-hubs in push direction, while processing other edges in pull direction.The evaluation shows iHTL is 1.5× -2.4× faster than pull and 4.8× -9.5× faster than push in state-of-the-art graph processing frameworks such as GraphGrind, GraphIt and Galois. More importantly, iHTL is 1.3× -1.5× faster than pull traversal of state-of-the-art locality optimizing reordering algorithms such as SlashBurn, GOrder, and Rabbit-Order.

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“…These techniques have first been investigated for dense linear algebra [62]. More recently they have been applied to graph processing [63], [64]. They are most easily applied in a coordinate list representation of the graph.…”

Section: A Locality Optimizing Algorithmsmentioning

confidence: 99%

Locality Analysis of Graph Reordering Algorithms

Esfahani

Kilpatrick

Vandierendonck

2021

2021 IEEE International Symposium on Workload Characterization (IISWC)

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A major challenge in processing real-world graphs stems from poor locality of memory accesses and vertex reordering algorithms (RAs) have been proposed to improve locality by changing the order of memory accesses.While state-of-the-art RAs like SlashBurn, GOrder, and Rabbit-Order effectively speed up graph algorithms, their capabilities and disadvantages are not fully understood, mainly, for three reasons: (1) the large size of datasets, (2) the lack of suitable measurement tools, and (3) disparate characteristics of graphs. The paucity of analysis has also inhibited the design of more efficient RAs.This paper unlocks this black box by introducing a number of tools, including: (1) a cache simulation technique for processing large graphs, (2) the Neighbour to Neighbour Average ID Distance (N2N AID) as a spatial locality metric, (3) the degree distributions of simulated cache miss rate and AID to investigate how locality of different vertices is affected by RAs, and (4) "effective cache size" to measure how much of cache capacity is used to support random accesses.We introduce (1) asymmetricity degree distribution, (2) degree range decomposition, and (3) push and pull locality to present a structural analysis of different types of real-world graphs by explaining their contrasting behaviours in confronting RAs.Finally, we propose a number of improvements for RAs using the analysis provided in this paper.

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Accelerating Graph Analytics by Utilising the Memory Locality of Graph Partitioning

Cited by 11 publications

References 31 publications

VEBO: A Vertex- and Edge-Balanced Ordering Heuristic to Load Balance Parallel Graph Processing

VEBO: A Vertex- and Edge-Balanced Ordering Heuristic to Load Balance Parallel Graph Processing

Exploiting in-Hub Temporal Locality in SpMV-based Graph Processing

Locality Analysis of Graph Reordering Algorithms

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