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Graph Algorithms in the Language of Linear Algebra: How Did We Get Here, and Where Do We Go Next?

机译:线性代数语言中的图算法:我们如何到达这里,下一步要去哪里?

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Numerical computational science dominated the first half century of high- performance computing; graph theory served numerical linear algebra by enabling efficient sparse matrix methods. Turnabout is fair play: Nowadays more and more computational problems concern graphs in their own right, and sparse matrix methods are often a good way to look at algorithms on graphs. This has led via a long path to the Graph BLAS and its reference implementations, which are a significant milestone. But there's a lot left to do. What happens now?
机译:数值计算科学主导了高性能计算的前半个世纪。图论通过启用有效的稀疏矩阵方法为数值线性代数服务。转折是公平的:如今,越来越多的计算问题本身就涉及图,而稀疏矩阵方法通常是查看图算法的好方法。这通向Graph BLAS及其参考实现很长的路要走,这是一个重要的里程碑。但是还有很多事情要做。现在会发生什么?

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