Congra: Towards Efficient Processing of Concurrent Graph Queries on Shared-Memory Machines

Pan, Peitian; Li, Chao

doi:10.1109/iccd.2017.40

Cited by 25 publications

(8 citation statements)

References 21 publications

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“…They therefore process the graph chunk wise and schedule the work of the different queries so that they work on the same chunk that has already been loaded to the LLC. Pan and Li [14] try to increase the execution efficiency by scheduling optimization. Via up-front profiling runs of each graph and algorithm pair they estimate the bandwidth requirement and which of two thread counts offers the highest performance.…”

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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“…The implementations on GPUs [29] adapt the existing parallel algorithms to the enhanced available parallelism therein, whereas, on the streaming frameworks the same algorithms are applied on static snapshots [14,31]. A couple of exceptions though: Stinger [19] and Congra [40] support concurrent updates, however, they steer away from discussing the main challenges accompanying concurrency -progress guarantee and correctness. To our knowledge, the present work is the first in this direction.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Graph Operations: A Consistent Non-blocking Approach

Chatterjee,

Peri,

2020

Preprint

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Graph algorithms enormously contribute to the domains such as blockchains, social networks, biological networks, telecommunication networks, and several others. The ever-increasing demand of data-volume as well as speed of such applications have essentially transported these applications from their comfort zone: static setting, to a challenging territory of dynamic updates. At the same time, mainstreaming of multi-core processors have entailed that the dynamic applications should be able to exploit concurrency as soon as parallelization gets inhibited. Thus, the design and implementation of efficient concurrent dynamic graph algorithms have become significant. This paper reports a novel library of concurrent shared-memory algorithms for breadth-first search (BFS), single-source shortest-path (SSSP), and betweenness centrality (BC) in a dynamic graph. The presented algorithms are provably non-blocking and linearizable. We extensively evaluate C++ implementations of the algorithms through several micro-benchmarks. The experimental results demonstrate the scalability with the number of threads. Our experiments also highlight the limitations of static graph analytics methods in dynamic setting.

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“…However, these storage systems still suffer from high redundant data access cost for concurrent iterative graph jobs without the consideration of the data access similarities. Note that some graph storage and querying systems [10,17,30,31,34,42] are recently devised for concurrent graph queries. However, they are dedicated to graph queries which usually only access different small subsets of a graph for exactly once, and can not efficiently support iterative graph processing which needs to frequently traverse the whole graph.…”

Section: Related Workmentioning

confidence: 99%