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Auto-tuning Similarity Search Algorithms on Multi-core Architectures

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Abstract

In recent times, large high-dimensional datasets have become ubiquitous. Video and image repositories, financial, and sensor data are just a few examples of such datasets in practice. Many applications that use such datasets require the retrieval of data items similar to a given query item, or the nearest neighbors (NN or \(k\)-NN) of a given item. Another common query is the retrieval of multiple sets of nearest neighbors, i.e., multi \(k\)-NN, for different query items on the same data. With commodity multi-core CPUs becoming more and more widespread at lower costs, developing parallel algorithms for these search problems has become increasingly important. While the core nearest neighbor search problem is relatively easy to parallelize, it is challenging to tune it for optimality. This is due to the fact that the various performance-specific algorithmic parameters, or “tuning knobs”, are inter-related and also depend on the data and query workloads. In this paper, we present (1) a detailed study of the various tuning knobs and their contributions on increasing the query throughput for parallelized versions of the two most common classes of high-dimensional multi-NN search algorithms: linear scan and tree traversal, and (2) an offline auto-tuner for setting these knobs by iteratively measuring actual query execution times for a given workload and dataset. We show experimentally that our auto-tuner reaches near-optimal performance and significantly outperforms un-tuned versions of parallel multi-NN algorithms for real video repository data on a variety of multi-core platforms.

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Notes

  1. This is just an analogy, not to be confused with the SIMD instructions, like SSE, which we discuss later.

  2. Horizontal addition of a vector of \(4\) floats, which is an operation needed for distance computation via SIMD instructions, requires \(2\) SIMD instructions with SSE3, which is only \(2\) (\(<4\)) times better than the scalar version of the same computation.

  3. If the blocks are sufficiently small, this may still be advantageous if the index trees fit inside L2 or L3 cache.

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  1. Department of Computer Engineering, Bilkent University, Ankara, Turkey

    Buğra Gedik

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Gedik, B. Auto-tuning Similarity Search Algorithms on Multi-core Architectures. Int J Parallel Prog 41, 595–620 (2013). https://doi.org/10.1007/s10766-013-0239-8

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