在非传统计算硬件上进行大规模可持续搜索
Large-scale Sustainable Search on Unconventional Computing Hardware
摘要：
自从互联网出现以来，量化网页的相对重要性是搜索引擎方法的核心。根据一种算法，PageRank，全球网络结构由Google矩阵表示，其主要特征向量分量为网页分配数值以进行排名。在不断增长的网页数量上找到这样一个占主导地位的特征向量成为一个计算密集型的任务，与摩尔定律不相容。我们证明，特殊用途的光学机器，如网络的光学参量振荡器，激光器，增益耗散凝聚，可能有助于加速可靠的重建现实生活中的网络图的主要特征向量。我们讨论了使用这种非传统的硬件在大型Google矩阵上模拟PageRank算法的可行性。我们提供替代排名的基础上最小化的自旋哈密顿。我们的估计表明，特殊用途的光学机器可以提供显着的改进，在经典的计算架构的功耗。
英文摘要：
Since the advent of the Internet, quantifying the relative importance of web pages is at the core of search engine methods. According to one algorithm, PageRank, the worldwide web structure is represented by the Google matrix, whose principal eigenvector components assign a numerical value to web pages for their ranking. Finding such a dominant eigenvector on an ever-growing number of web pages becomes a computationally intensive task incompatible with Moore's Law. We demonstrate that special-purpose optical machines such as networks of optical parametric oscillators, lasers, and gain-dissipative condensates, may aid in accelerating the reliable reconstruction of principal eigenvectors of real-life web graphs. We discuss the feasibility of simulating the PageRank algorithm on large Google matrices using such unconventional hardware. We offer alternative rankings based on the minimisation of spin Hamiltonians. Our estimates show that special-purpose optical machines may provide dramatic improvements in power consumption over classical computing architectures.
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