Green-Marl

Chafi, Hassan; Sedlar, Edic; Olukotun, Kunle

doi:10.1145/2248487.2151013

Cited by 46 publications

(5 citation statements)

References 22 publications

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“…We also constructed explicit graphs from the selected on-the-fly model checking and synthetic experiments (by storing all edges during an on-the-fly search in CSR format), these graphs are prefixed with "e-" in Table 4. We used the GreenMarl [24] framework to convert the graphs to a binary format suitable for the implementation.…”

Section: Methodsmentioning

confidence: 99%

Multi-core on-the-fly SCC decomposition

2016

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The main advantages of Tarjan's strongly connected component (SCC) algorithm are its linear time complexity and ability to return SCCs on-the-fly, while traversing or even generating the graph. Until now, most parallel SCC algorithms sacrifice both: they run in quadratic worst-case time and/or require the full graph in advance. The current paper presents a novel parallel, on-the-fly SCC algorithm. It preserves the linear-time property by letting workers explore the graph randomly while carefully communicating partially completed SCCs. We prove that this strategy is correct. For efficiently communicating partial SCCs, we develop a concurrent, iterable disjoint set structure (combining the union-find data structure with a cyclic list). We demonstrate scalability on a 64-core machine using 75 real-world graphs (from model checking and explicit data graphs), synthetic graphs (combinations of trees, cycles and linear graphs), and random graphs. Previous work did not show speedups for graphs containing a large SCC. We observe that our parallel algorithm is typically 10-30× faster compared to Tarjan's algorithm for graphs containing a large SCC. Comparable performance (with respect to the current state-of-the-art) is obtained for graphs containing many small SCCs.

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Section: Methodsmentioning

confidence: 99%

Multi-core on-the-fly SCC decomposition

2016

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“…The advantage of graphs over the traditional relational model is that they can inherently model entities and their relationships. While the relational model needs to join tabular data in order to process foreign key relationships, graph-processing engines have built-in ways to efficiently iterate over graphs [29], e.g., over the neighbors of vertices, and they support a plethora of imperative languages for writing graph algorithms (such as Green-Marl [4,30]), as well as declarative languages for pattern-matching queries (such as PGQL [15], SPARQL [31], and Gremlin [32]).…”

Section: Background and Related Workmentioning

confidence: 99%

“…In order for graph-processing engines to provide efficient solutions for large-scale graphs, they rely on efficient data structures, which potentially reside in the main memory [3,6,16,38], to store and process vertices and their relationships. One of the key challenges for in-memory graph-processing engines is to design data structures with reasonable memory footprint [3] that can support fast graph algorithm execution [30,39] and query pattern matching [40] while supporting topological modifications, i.e., additions or removals of vertices and edges, either in batches or in a streaming fashion [19,41]. In the rest of this section, we discuss the most prominent data structures in related work [16] and motivate the necessity for the novel CSR++.…”

Section: Background and Related Workmentioning

confidence: 99%

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A Scalable Data Structure for Efficient Graph Analytics and In-Place Mutations

Firmli,

Chiadmi

2023

Data

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The graph model enables a broad range of analyses; thus, graph processing (GP) is an invaluable tool in data analytics. At the heart of every GP system lies a concurrent graph data structure that stores the graph. Such a data structure needs to be highly efficient for both graph algorithms and queries. Due to the continuous evolution, the sparsity, and the scale-free nature of real-world graphs, GP systems face the challenge of providing an appropriate graph data structure that enables both fast analytical workloads and fast, low-memory graph mutations. Existing graph structures offer a hard tradeoff among read-only performance, update friendliness, and memory consumption upon updates. In this paper, we introduce CSR++, a new graph data structure that removes these tradeoffs and enables both fast read-only analytics, and quick and memory-friendly mutations. CSR++ combines ideas from CSR, the fastest read-only data structure, and adjacency lists (ALs) to achieve the best of both worlds. We compare CSR++ to CSR, ALs from the Boost Graph Library (BGL), and the following state-of-the-art update-friendly graph structures: LLAMA, STINGER, GraphOne, and Teseo. In our evaluation, which is based on popular GP algorithms executed over real-world graphs, we show that CSR++ remains close to CSR in read-only concurrent performance (within 10% on average) while significantly outperforming CSR (by an order of magnitude) and LLAMA (by almost 2×) with frequent updates. We also show that both CSR++’s update throughput and analytics performance exceed those of several state-of-the-art graph structures while maintaining low memory consumption when the workload includes updates.

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“…Beamer et al also measure 3 graph libraries and propose processor architecture change. Green‐Marl is a domain specific language for graph processing. Chen et al proposed compiler optimization methodology for graph and other irregular applications on Intel Xeon Phi coprocessors.…”

Section: Related Workmentioning

confidence: 99%

Efficient and high‐quality sparse graph coloring on GPUs

Chen

Fang

et al. 2016

Concurrency and Computation

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Summary Graph coloring has been broadly used to discover concurrency in parallel computing. To speed up graph coloring for large‐scale datasets, parallel algorithms have been proposed to leverage modern GPUs. Existing GPU implementations either have limited performance or yield unsatisfactory coloring quality (too many colors assigned). We present a work‐efficient parallel graph coloring implementation on GPUs with good coloring quality. Our approach uses the speculative greedy scheme, which inherently yields better quality than the method of finding maximal independent set. To achieve high performance on GPUs, we refine the algorithm to leverage efficient operators and alleviate conflicts. We also incorporate common optimization techniques to further improve performance. Our method is evaluated with both synthetic and real‐world sparse graphs on the NVIDIA GPU. Experimental results show that our proposed implementation achieves averaged 4.1 × (up to 8.9 × ) speedup over the serial implementation. It also outperforms the existing GPU implementation from the NVIDIA CUSPARSE library (2.2 × average speedup), while yielding much better coloring quality than CUSPARSE.

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Green-Marl

Cited by 46 publications

References 22 publications

Multi-core on-the-fly SCC decomposition

Multi-core on-the-fly SCC decomposition

A Scalable Data Structure for Efficient Graph Analytics and In-Place Mutations

Efficient and high‐quality sparse graph coloring on GPUs

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