Clustering is the task of assigning a set of objects into groups (clusters) so that objects in the same group are more similar to each other than to those in other groups. In particular, K-means is a clustering algorithm that calculates the cluster with the nearest mean for each object. To achieve this, it uses a function like Euclidean or Manhattan distance. Our objective is to exploit our heterogeneous computing environment, that integrates an Intel Core i7-6700K chip, 2x NVIDIA TITAN X and an Intel Altera Terasic Stratix V DE5-NET FPGA, to run K-means as fast as possible.
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机译：群集是将一组对象分配到组（群集）中的任务，以便同一组中的对象彼此之间的相似性高于其他组中的对象。特别是，K-means是一种聚类算法，它为每个对象计算均值最近的聚类。为此，它使用了诸如欧几里得距离或曼哈顿距离之类的函数。我们的目标是利用我们的异构计算环境，该环境集成了Intel Core i7-6700K芯片，2个NVIDIA TITAN X和Intel Altera Terasic Stratix V DE5-NET FPGA，以尽可能快地运行K-means。
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