A parallel algorithm of Euclidean distance matrix computation for the Intel Xeon Phi Knights Landing many-core processor

Computation of a Euclidean distance matrix (EDM) is a typical task in a wide spectrum of problems connected with data mining. Currently, many parallel algorithms for this task have been developed for graphical processors. These developments, however, cannot be directly applied to the Intel Many Integrated Core systems. In this paper, we suggest a parallel algorithm for EDM computation on Intel Xeon Phi Knights Landing processor in the case when the input data fit into the main memory. The algorithm exploits block-oriented scheme of computations that allows for the efficient utilization of Intel Xeon Phi vectorization abilities. In the algorithm, we also apply apply a sophisticated data layout to store data points in main memory so as to reduce the number of processor cache misses during EDM computations. Experimental evaluation of the algorithm on real-world and synthetic datasets shows that it is highly scalable and outruns analogues in the case of rectangular matrices with low-dimensional data points.