GraphBLAS and GraphChallenge Advance Network Frontiers
The challenges associated with graph algorithm scaling led multiple scientists to identify the need for an abstraction layer that would allow algorithm specialists to write high-performance, matrix-based graph algorithms that hardware specialists could then design to without having to manage the com...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | en_US |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://hdl.handle.net/1721.1/146227 |
| Summary: | The challenges associated with graph algorithm scaling led multiple scientists to identify the need for an abstraction layer that would allow algorithm specialists to write high-performance, matrix-based graph algorithms that hardware specialists could then design to without having to manage the complexities of every type of graph algorithm. With this philosophy in mind, a number of researchers (including two Turing Award winners) came together and proposed the idea that “the state of the art in constructing a large collection of graph algorithms in terms of linear algebraic operations is mature enough to support the emergence of a standard set of primitive building blocks” |
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