FLASH Randomized Algorithms Accelerated Over CPU-GPU For Ultra-high Dimensional Similarity Search

Wang Yiqiu, Shrivastava Anshumali, Wang Jonathan, Ryu Junghee. Arxiv 2017

[Paper]    
ARXIV Graph Independent LSH

We present FLASH (\textbf{F}ast \textbf{L}SH \textbf{A}lgorithm for \textbf{S}imilarity search accelerated with \textbf{H}PC), a similarity search system for ultra-high dimensional datasets on a single machine, that does not require similarity computations and is tailored for high-performance computing platforms. By leveraging a LSH style randomized indexing procedure and combining it with several principled techniques, such as reservoir sampling, recent advances in one-pass minwise hashing, and count based estimations, we reduce the computational and parallelization costs of similarity search, while retaining sound theoretical guarantees. We evaluate FLASH on several real, high-dimensional datasets from different domains, including text, malicious URL, click-through prediction, social networks, etc. Our experiments shed new light on the difficulties associated with datasets having several million dimensions. Current state-of-the-art implementations either fail on the presented scale or are orders of magnitude slower than FLASH. FLASH is capable of computing an approximate k-NN graph, from scratch, over the full webspam dataset (1.3 billion nonzeros) in less than 10 seconds. Computing a full k-NN graph in less than 10 seconds on the webspam dataset, using brute-force ($n^{2}D$), will require at least 20 teraflops. We provide CPU and GPU implementations of FLASH for replicability of our results.

Similar Work

• Learning-based Efficient Graph Similarity Computation Via Multi-scale Convolutional Set Matching
• Convolutional Set Matching For Graph Similarity
• Scalable Locality-sensitive Hashing For Similarity Search In High-dimensional Large-scale Multimedia Datasets
• Accelerated Distance Computation With Encoding Tree For High Dimensional Data