Executing dynamic data-graph computations deterministically using chromatic scheduling

Kaler, Tim; Hasenplaugh, William; Schardl, Tao B.; Leiserson, Charles E.

doi:10.1145/2612669.2612673

Cited by 39 publications

(29 citation statements)

References 73 publications

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“…We were motivated to work on graph coloring in the context of "chromatic scheduling" [1,7,37] of parallel "data-graph computations." A data graph is a graph with data associated with its vertices and edges.…”

Section: Introductionmentioning

confidence: 99%

Ordering heuristics for parallel graph coloring

Hasenplaugh¹,

Kaler²,

Schardl³

et al. 2014

Proceedings of the 26th ACM Symposium on Parallelism in Algorithms and Architectures

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This paper introduces the largest-log-degree-first (LLF) and smallest-log-degree-last (SLL) ordering heuristics for parallel greedy graph-coloring algorithms, which are inspired by the largest-degree-first (LF) and smallest-degree-last (SL) serial heuristics, respectively. We show that although LF and SL, in practice, generate colorings with relatively small numbers of colors, they are vulnerable to adversarial inputs for which any parallelization yields a poor parallel speedup. In contrast, LLF and SLL allow for provably good speedups on arbitrary inputs while, in practice, producing colorings of competitive quality to their serial analogs.We applied LLF and SLL to the parallel greedy coloring algorithm introduced by Jones and Plassmann, referred to here as JP. Jones and Plassman analyze the variant of JP that processes the vertices of a graph in a random order, and show that on an O(1)-degree graph G = (V, E), this JP-R variant has an expected parallel running time of O(lgV / lg lgV ) in a PRAM model. We improve this bound to show, using work-span analysis, that JP-R, augmented to handle arbitrary-degree graphs, colors a graph G = (V, E) with degree ∆ using Θ(V + E) work and O(lgV + lg ∆ · min{ √ E, ∆ + lg ∆ lgV / lg lgV }) expected span. We prove that JP-LLF and JP-SLL-JP using the LLF and SLL heuristics, respectivelyexecute with the same asymptotic work as JP-R and only logarithmically more span while producing higher-quality colorings than JP-R in practice.We engineered an efficient implementation of JP for modern shared-memory multicore computers and evaluated its performance on a machine with 12 Intel Core-i7 (Nehalem) processor cores. Our implementation of JP-LLF achieves a geometric-mean speedup of 7.83 on eight real-world graphs and a geometric-mean speedup of 8.08 on ten synthetic graphs, while our implementation using SLL achieves a geometric-mean speedup of 5.36 on these real-world graphs and a geometric-mean speedup of 7.02 on these synthetic graphs. Furthermore, on one processor, JP-LLF is slightly faster than a well-engineered serial greedy algorithm using LF, and likewise, JP-SLL is slightly faster than the greedy algorithm using SL.

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Section: Introductionmentioning

confidence: 99%

Ordering heuristics for parallel graph coloring

Hasenplaugh¹,

Kaler²,

Schardl³

et al. 2014

Proceedings of the 26th ACM Symposium on Parallelism in Algorithms and Architectures

Self Cite

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“…• Two new algorithms for the connectomics domain: (1) a new inter-block merging algorithm that, unlike prior approaches, applies parallelized NeuroProof to optimize object-pair merges, and (2) a parallel skeletonization algorithm that uses novel techniques and GCC-Cilk based chromatic scheduling [25,26] to execute efficiently on multicores. We describe the algorithmic side in more detail in [46].…”

Section: Our Contributionsmentioning

confidence: 99%

“…Thinning starts with points on the object boundary and repeatedly removes ones that do not affect overall topological connectivity. We devised a simple and efficient parallel algorithm for extracting volume skeletons using chromatic scheduling [25,26] to efficiently schedule the parallel order of which points are considered for deletion doing the thinning process. The details of the skeletonization algorithm are discussed further in Section 7.…”

Section: Pipeline Structurementioning

confidence: 99%

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A Multicore Path to Connectomics-on-Demand

Shavit

2016

Proceedings of the 28th ACM Symposium on Parallelism in Algorithms and Architectures

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The current design trend in large scale machine learning is to use distributed clusters of CPUs and GPUs with MapReduce-style programming. Some have been led to believe that this type of horizontal scaling can reduce or even eliminate the need for traditional algorithm development, careful parallelization, and performance engineering. This paper is a case study showing the contrary: that the benefits of algorithms, parallelization, and performance engineering, can sometimes be so vast that it is possible to solve "clusterscale" problems on a single commodity multicore machine.Connectomics is an emerging area of neurobiology that uses cutting edge machine learning and image processing to extract brain connectivity graphs from electron microscopy images. It has long been assumed that the processing of connectomics data will require mass storage, farms of CPU/GPUs, and will take months (if not years) of processing time. We present a high-throughput connectomics-ondemand system that runs on a multicore machine with less than 100 cores and extracts connectomes at the terabyte per hour pace of modern electron microscopes.

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“…This work was further extended in [7] using colouring techniques to acquire independent sets while also supporting dynamic data-graph computations. However, the recent PowerGraph [5] enhanced system relies instead on a dynamic locking mechanism to ensure that conflicting assesses to shared data are resolved.…”

Section: Related Workmentioning

confidence: 99%

Analysis of the semi-synchronous approach to large-scale parallel community finding

Duriakova

Hurley

Ajwani³

et al. 2014

Proceedings of the Second ACM Conference on Online Social Networks

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Community-finding in graphs is the process of identifying highly cohesive vertex subsets. Recently the vertex-centric approach has been found effective for scalable graph processing and is implemented in systems such as GraphLab and Pregel. In the vertex-centric approach, the analysis is decomposed into a set of local computations at each vertex of the graph, with results propagated to neighbours along the vertex's edges. Many community finding algorithms are amenable to this approach as they are based on the optimisation of an objective through a process of iterative local update (ILU), in which vertices are successively moved to the community of one of their neighbours in order to achieve the highest local gain in the quality of the objective. The sequential processing of such iterative algorithms generally benefits from an asynchronous approach, where a vertex update uses the most recent state as generated by the previous update of vertices in its neighbourhood. When vertices are distributed over a parallel machine, the asynchronous approach can encounter race conditions that impact on its performance and destroy the consistency of the results. Alternatively, a semi-synchronous approach ensures that only non-conflicting vertices are updated simultaneously. In this paper we study the semi-synchronous approach to ILU algorithms for community finding on social networks. Because of the heavy-tailed vertex distribution, the order in which vertex updates are applied in asynchronous ILU can greatly impact both convergence time and quality of the found communities. We study the impact of ordering on the distributed label propagation and modularity maximisation algorithms implemented on a shared-memory multicore architecture. We demonstrate that the semi-synchronous ILU approach is competitive in time and quality with the asynchronous approach, while allowing the analyst to maintain consistent control over update ordering. Thus, our implementation results in a more robust and predictable performance and provides control over the order in which the node Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. labels are updated, which is crucial to obtaining the correct trade-off between running time and quality of communities on many graph classes.

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Executing dynamic data-graph computations deterministically using chromatic scheduling

Cited by 39 publications

References 73 publications

Ordering heuristics for parallel graph coloring

Ordering heuristics for parallel graph coloring

A Multicore Path to Connectomics-on-Demand

Analysis of the semi-synchronous approach to large-scale parallel community finding

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