Executing Dynamic Data-Graph Computations Deterministically Using Chromatic Scheduling

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

doi:10.1145/2896850

Cited by 7 publications

(3 citation statements)

References 87 publications

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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%

“…Our parallel thinning algorithm is implemented as a chromatically scheduled dynamic data-graph computation [25,26]. A grid graph represents the labeled 3D volume and a statically-computed distance-2 coloring is used to identify sets of voxels that may be processed in parallel.…”

Section: Skeletonizationmentioning

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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Section: Our Contributionsmentioning

confidence: 99%

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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High-performance and balanced parallel graph coloring on multicore platforms

et al. 2022

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Graph coloring is widely used to parallelize scientific applications by identifying subsets of independent tasks that can be executed simultaneously. Graph coloring assigns colors the vertices of a graph, such that no adjacent vertices have the same color. The number of colors used corresponds to the number of parallel steps in a real-world end-application. Therefore, the total runtime of the graph coloring kernel adds to the overall parallel overhead of the real-world end-application, whereas the number of the vertices of each color class determines the number of the independent concurrent tasks of each parallel step, thus affecting the amount of parallelism and hardware resource utilization in the execution of the real-world end-application. In this work, we propose a high-performance graph coloring algorithm, named ColorTM, that leverages Hardware Transactional Memory (HTM) to detect coloring inconsistencies between adjacent vertices. ColorTM detects and resolves coloring inconsistencies between adjacent vertices with an eager approach to minimize data access costs, and implements a speculative synchronization scheme to minimize synchronization costs and increase parallelism. We extend our proposed algorithmic design to propose a balanced graph coloring algorithm, named BalColorTM, with which all color classes include almost the same number of vertices to achieve high parallelism and resource utilization in the execution of the real-world end-applications. We evaluate ColorTM and BalColorTM using a wide variety of large real-world graphs with diverse characteristics. ColorTM and BalColorTM improve performance by 12.98$$\times$$ × and 1.78$$\times$$ × on average using 56 parallel threads compared to prior state-of-the-art approaches. Moreover, we study the impact of our proposed graph coloring algorithmic designs on a popular end-application, i.e., Community Detection, and demonstrate the ColorTM and BalColorTM can provide high-performance improvements in real-world end-applications across various input data given.

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