Mix-and-Match: A Model-Driven Runtime Optimisation Strategy for BFS on GPUs

Verstraaten, Merijn; Vărbănescu, Ana Lucia; Laat, Cees de

doi:10.1109/ia3.2018.00014

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…Verstraaten, Varbanescu and de Laat [20] analyze BFS variants on different types of graphs in the context of GPU processing. They observe that depending on the graphs different algorithmic variants have an advantage.…”

Section: Related Workmentioning

confidence: 99%

Scheduling of Graph Queries: Controlling Intra- and Inter-query Parallelism for a High System Throughput

Hauck¹,

Oukid²,

Fröning³

2021

Preprint

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The vast amounts of data used in social, business or traffic networks, biology and other natural sciences are often managed in graph-based data sets, consisting of a few thousand up to billions and trillions of vertices and edges, respectively. Typical applications utilizing such data either execute one or a few complex queries or many small queries at the same time interactively or as batch jobs. Furthermore, graph processing is inherently complex, as data sets can substantially differ (scale free vs. constant degree), and algorithms exhibit diverse behavior (computational intensity, local or global, push-or pull-based).This work is concerned with multi-query execution by automatically controlling the degree of parallelization, with overall objectives including high system utilization, low synchronization cost, and highly efficient concurrent execution. The underlying concept is three-fold: (1) sampling is used to determine graph statistics, (2) parallelization constraints are derived from algorithm and system properties, and (3) suitable work packages are generated based on the previous two aspects. We evaluate the proposed concept using different algorithms on synthetic and real world data sets, with up to 16 concurrent sessions (queries). The results demonstrate a robust performance in spite of these various configurations, and in particular that the performance is always close to or even slightly ahead of the performance of manually optimized implementations. Furthermore, the similar performance to manually optimized implementations under extreme configurations, which require either a full parallelization (few large queries) or complete sequential execution (many small queries), shows that the proposed concept exhibits a particularly low overhead.

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Section: Related Workmentioning

confidence: 99%

Scheduling of Graph Queries: Controlling Intra- and Inter-query Parallelism for a High System Throughput

Hauck¹,

Oukid²,

Fröning³

2021

Preprint

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show abstract

“…In [13], it has provided a large-scale graph that resolved the limitation of the GPU memory capacity in this scale. Its processing capability includes billions of edges.…”

Section: Introductionmentioning

confidence: 99%

Graph Traversal (GT) on Linear and Quadratic Graphic Processing Units with CPU

Eftekhari¹

2023

EJECE

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Nowadays, Graph Traversal (GT) is a fundamental procedure for developing a measure in computing systems and algorithms. Graph traversal algorithms usually iterate input datasets of a graph to provide a logical convergence in every iteration. Graphic Processing Units (GPUs) have been utilized broadly as accelerators of GT due to their massive parallelism and accessibility of high-transfer speed memory. Furthermore, graph-based computation is pervasive and challenging for memory systems implementation in a specific time period. This paper provides quadratic and single GPUs, allowing developers to extend graph-based GPU computation effortlessly. The paper concentrates on the creditability and validation of sparse graphs in GPUs. Since the benchmark characteristic of Breadth-First Search (BFS) plays a critical role in graphs, this paper considers a primary characteristic benchmark for BFS computation usage. This paper explores the effectiveness and applicability of BFS for analyzing graph systems by utilizing the mentioned characteristics in BFS computation. This paper also utilizes the penalization of GPU BFS during the analysis and simulation results. In addition, using a comparative simulation result, this paper concludes that improved traversal rates speed up the computation and reduce memory usage for the single and quadratic GPU structures.

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Efficient trimming for strongly connected components calculation

Niewenhuis

Varbanescu

2022

Proceedings of the 19th ACM International Conference on Computing Frontiers

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Mix-and-Match: A Model-Driven Runtime Optimisation Strategy for BFS on GPUs

Cited by 3 publications

References 23 publications

Scheduling of Graph Queries: Controlling Intra- and Inter-query Parallelism for a High System Throughput

Scheduling of Graph Queries: Controlling Intra- and Inter-query Parallelism for a High System Throughput

Graph Traversal (GT) on Linear and Quadratic Graphic Processing Units with CPU

Efficient trimming for strongly connected components calculation

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