FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2898231337> ?p ?o ?g. }

• W2898231337 endingPage "30" @default.
• W2898231337 startingPage "1" @default.
• W2898231337 abstract "The performance bottlenecks of graph applications depend not only on the algorithm and the underlying hardware, but also on the size and structure of the input graph. As a result, programmers must try different combinations of a large set of techniques, which make tradeoffs among locality, work-efficiency, and parallelism, to develop the best implementation for a specific algorithm and type of graph. Existing graph frameworks and domain specific languages (DSLs) lack flexibility, supporting only a limited set of optimizations. This paper introduces GraphIt, a new DSL for graph computations that generates fast implementations for algorithms with different performance characteristics running on graphs with different sizes and structures. GraphIt separates what is computed (algorithm) from how it is computed (schedule). Programmers specify the algorithm using an algorithm language, and performance optimizations are specified using a separate scheduling language. The algorithm language simplifies expressing the algorithms, while exposing opportunities for optimizations. We formulate graph optimizations, including edge traversal direction, data layout, parallelization, cache, NUMA, and kernel fusion optimizations, as tradeoffs among locality, parallelism, and work-efficiency. The scheduling language enables programmers to easily search through this complicated tradeoff space by composing together a large set of edge traversal, vertex data layout, and program structure optimizations. The separation of algorithm and schedule also enables us to build an autotuner on top of GraphIt to automatically find high-performance schedules. The compiler uses a new scheduling representation, the graph iteration space, to model, compose, and ensure the validity of the large number of optimizations. We evaluate GraphIt’s performance with seven algorithms on graphs with different structures and sizes. GraphIt outperforms the next fastest of six state-of-the-art shared-memory frameworks (Ligra, Green-Marl, GraphMat, Galois, Gemini, and Grazelle) on 24 out of 32 experiments by up to 4.8×, and is never more than 43% slower than the fastest framework on the other experiments. GraphIt also reduces the lines of code by up to an order of magnitude compared to the next fastest framework." @default.
• W2898231337 created "2018-11-02" @default.
• W2898231337 creator A5013514007 @default.
• W2898231337 creator A5039453861 @default.
• W2898231337 creator A5044704994 @default.
• W2898231337 creator A5046791216 @default.
• W2898231337 creator A5051583907 @default.
• W2898231337 creator A5085157310 @default.
• W2898231337 date "2018-10-24" @default.
• W2898231337 modified "2023-10-14" @default.
• W2898231337 title "GraphIt: a high-performance graph DSL" @default.
• W2898231337 cites W1489951562 @default.
• W2898231337 cites W1783256592 @default.
• W2898231337 cites W1955594754 @default.
• W2898231337 cites W1986603225 @default.
• W2898231337 cites W1988144253 @default.
• W2898231337 cites W2000041758 @default.
• W2898231337 cites W2033700683 @default.
• W2898231337 cites W2034102265 @default.
• W2898231337 cites W2035080386 @default.
• W2898231337 cites W2053076698 @default.
• W2898231337 cites W2056116767 @default.
• W2898231337 cites W2062140606 @default.
• W2898231337 cites W2072901340 @default.
• W2898231337 cites W2080098453 @default.
• W2898231337 cites W2095223744 @default.
• W2898231337 cites W2098688018 @default.
• W2898231337 cites W2100218206 @default.
• W2898231337 cites W2101196063 @default.
• W2898231337 cites W2119407198 @default.
• W2898231337 cites W2144085134 @default.
• W2898231337 cites W2149941839 @default.
• W2898231337 cites W2152506070 @default.
• W2898231337 cites W2154968583 @default.
• W2898231337 cites W2162630236 @default.
• W2898231337 cites W2165461546 @default.
• W2898231337 cites W2232813226 @default.
• W2898231337 cites W2399032974 @default.
• W2898231337 cites W2511364592 @default.
• W2898231337 cites W2546973305 @default.
• W2898231337 cites W2574229471 @default.
• W2898231337 cites W2604520577 @default.
• W2898231337 cites W2606413522 @default.
• W2898231337 cites W2619040869 @default.
• W2898231337 cites W2718955078 @default.
• W2898231337 cites W2730999914 @default.
• W2898231337 cites W2780077279 @default.
• W2898231337 cites W2782087530 @default.
• W2898231337 cites W2788981395 @default.
• W2898231337 cites W2798525482 @default.
• W2898231337 cites W2884496840 @default.
• W2898231337 cites W2962865652 @default.
• W2898231337 cites W2963921057 @default.
• W2898231337 cites W4234350265 @default.
• W2898231337 cites W4240241667 @default.
• W2898231337 cites W4249456448 @default.
• W2898231337 cites W4249583447 @default.
• W2898231337 doi "https://doi.org/10.1145/3276491" @default.
• W2898231337 hasPublicationYear "2018" @default.
• W2898231337 type Work @default.
• W2898231337 sameAs 2898231337 @default.
• W2898231337 citedByCount "111" @default.
• W2898231337 countsByYear W28982313372018 @default.
• W2898231337 countsByYear W28982313372019 @default.
• W2898231337 countsByYear W28982313372020 @default.
• W2898231337 countsByYear W28982313372021 @default.
• W2898231337 countsByYear W28982313372022 @default.
• W2898231337 countsByYear W28982313372023 @default.
• W2898231337 crossrefType "journal-article" @default.
• W2898231337 hasAuthorship W2898231337A5013514007 @default.
• W2898231337 hasAuthorship W2898231337A5039453861 @default.
• W2898231337 hasAuthorship W2898231337A5044704994 @default.
• W2898231337 hasAuthorship W2898231337A5046791216 @default.
• W2898231337 hasAuthorship W2898231337A5051583907 @default.
• W2898231337 hasAuthorship W2898231337A5085157310 @default.
• W2898231337 hasBestOaLocation W28982313371 @default.
• W2898231337 hasConcept C11413529 @default.
• W2898231337 hasConcept C115537543 @default.
• W2898231337 hasConcept C138885662 @default.
• W2898231337 hasConcept C140745168 @default.
• W2898231337 hasConcept C173608175 @default.
• W2898231337 hasConcept C27602214 @default.
• W2898231337 hasConcept C2779808786 @default.
• W2898231337 hasConcept C41008148 @default.
• W2898231337 hasConcept C41895202 @default.
• W2898231337 hasConcept C80444323 @default.
• W2898231337 hasConcept C96333769 @default.
• W2898231337 hasConceptScore W2898231337C11413529 @default.
• W2898231337 hasConceptScore W2898231337C115537543 @default.
• W2898231337 hasConceptScore W2898231337C138885662 @default.
• W2898231337 hasConceptScore W2898231337C140745168 @default.
• W2898231337 hasConceptScore W2898231337C173608175 @default.
• W2898231337 hasConceptScore W2898231337C27602214 @default.
• W2898231337 hasConceptScore W2898231337C2779808786 @default.
• W2898231337 hasConceptScore W2898231337C41008148 @default.
• W2898231337 hasConceptScore W2898231337C41895202 @default.
• W2898231337 hasConceptScore W2898231337C80444323 @default.
• W2898231337 hasConceptScore W2898231337C96333769 @default.

• next