Approximate nearest neighbor search for single instruction, multiple thread (SIMT) or single instruction, multiple data (SIMD) type processors

A near-nearest-neighbor, processor-like technology, applied in single-instruction-multiple-data multiprocessor, electrical digital data processing, special data processing applications, etc., can solve problems such as inability to effectively use hardware tools

Active Publication Date: 2021-05-25
BAIDU USA LLC
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

A significant hurdle is the inability to effectively utilize powerful hardware tools such as GPUs

Method used

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  • Approximate nearest neighbor search for single instruction, multiple thread (SIMT) or single instruction, multiple data (SIMD) type processors
  • Approximate nearest neighbor search for single instruction, multiple thread (SIMT) or single instruction, multiple data (SIMD) type processors
  • Approximate nearest neighbor search for single instruction, multiple thread (SIMT) or single instruction, multiple data (SIMD) type processors

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Embodiment Construction

[0071] In the following description, for purposes of explanation, specific details are set forth in order to provide an understanding of the present disclosure. It will be apparent, however, to one skilled in the art that the present disclosure may be practiced without these details. In addition, those skilled in the art will appreciate that the embodiments of the present disclosure described below can be implemented on tangible computer-readable media in various ways, such as a process, apparatus, system, device, or method.

[0072] Components or modules shown in the figures are illustrative of embodiments of the disclosure and are intended to avoid obscuring the disclosure. It should also be understood that throughout this discussion, components may be described as separate functional units (which may include subunits), but those skilled in the art will recognize that various components, or portions thereof, may be divided into separate components, or may be integrated toge...

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Abstract

Approximate nearest neighbor (ANN) searching is a fundamental problem in computer science with numerous applications in area such as machine learning and data mining. For typical graph-based ANN methods, the searching method is executed iteratively, and the execution dependency prohibits graphics processor unit (GPU)/GPU-type processor adaptations. Presented herein are embodiments of a novel framework that decouples the searching on graph methodology into stages, in order to parallel the performance-crucial distance computation. Furthermore, in one or more embodiments, to obtain better parallelism on GPU-type components, also disclosed are novel ANN-specific optimization methods that eliminate dynamic memory allocations and trade computations for less memory consumption. Embodiments were empirically compared against other methods, and the results confirm the effectiveness.

Description

[0001] Cross References to Related Applications [0002] This patent application is related to co-pending and co-owned Serial No. 62 / 940,043, entitled "APPROXIMATE NEARESTNEIGHBOR SEARCH FOR PARALLEL PROCESSING HARDWARE COMPONENTS," filed November 25, 2019 No. 28888-2373P, which lists Weijie Zhao, Shulong Tan, and Ping Li as inventors, and which is hereby incorporated by reference in its entirety and for all purposes. technical field [0003] The present disclosure relates generally to systems and methods for computer learning that provide improved computer performance, features, and use. More specifically, the present disclosure relates to implementations for improved search processing when the processing is intended to be done at a specific time on the processor. Background technique [0004] Since the early days of computer science, nearest neighbor (NN) search has been a fundamental problem with numerous practical applications in many fields such as machine learning, c...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F9/38G06F15/80G06F16/903
CPCG06F9/3887G06F15/8007G06F16/903G06F17/10G06F9/3009G06F9/4881G06F18/2323G06F18/24147
Inventor 赵炜捷谭树龙李平
Owner BAIDU USA LLC
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