论文标题
通过GPU上的XOR友好二进制量化搜索快速的TOP-K余弦相似性搜索
Fast top-K Cosine Similarity Search through XOR-Friendly Binary Quantization on GPUs
论文作者
论文摘要
我们探讨了GPU加速大规模最近的邻居搜索的使用，我们提出了一种基于快速的矢量定量详尽的最近邻居搜索算法，该算法可以实现高精度，而无需任何专门为余弦设计设计的索引结构。该算法使用一种新颖的XOR友好型二进制量化方法来编码浮点数，以便可以将高复杂性乘法优化为低复杂性位于位置操作。实验表明，我们的量化方法需要短暂的预处理时间，并有助于使我们的详尽搜索方法的搜索速度比流行的近似近似邻居算法的速度快得多。
We explore the use of GPU for accelerating large scale nearest neighbor search and we propose a fast vector-quantization-based exhaustive nearest neighbor search algorithm that can achieve high accuracy without any indexing construction specifically designed for cosine similarity. This algorithm uses a novel XOR-friendly binary quantization method to encode floating-point numbers such that high-complexity multiplications can be optimized as low-complexity bitwise operations. Experiments show that, our quantization method takes short preprocessing time, and helps make the search speed of our exhaustive search method much more faster than that of popular approximate nearest neighbor algorithms when high accuracy is needed.
