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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:图形算法中的线性代数语言:我们是如何到达这里的,我们下次去哪里?

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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;图表理论通过启用有效的稀疏矩阵方法提供数值线性代数。 Turnabout是公平的游戏:现在越来越多的计算问题涉及自己的权利,稀疏矩阵方法通常是看图形上的算法的好方法。这通过了GRAPL BLA的长路径和其参考实现,这是一个重要的里程碑。但是还有很多去做。现在发生了什么?

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