5,245 research outputs found
Efficient Large-scale Approximate Nearest Neighbor Search on the GPU
We present a new approach for efficient approximate nearest neighbor (ANN)
search in high dimensional spaces, extending the idea of Product Quantization.
We propose a two-level product and vector quantization tree that reduces the
number of vector comparisons required during tree traversal. Our approach also
includes a novel highly parallelizable re-ranking method for candidate vectors
by efficiently reusing already computed intermediate values. Due to its small
memory footprint during traversal, the method lends itself to an efficient,
parallel GPU implementation. This Product Quantization Tree (PQT) approach
significantly outperforms recent state of the art methods for high dimensional
nearest neighbor queries on standard reference datasets. Ours is the first work
that demonstrates GPU performance superior to CPU performance on high
dimensional, large scale ANN problems in time-critical real-world applications,
like loop-closing in videos
FLASH: Randomized Algorithms Accelerated over CPU-GPU for Ultra-High Dimensional Similarity Search
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 (), will require at least 20 teraflops. We
provide CPU and GPU implementations of FLASH for replicability of our results
Fast -NNG construction with GPU-based quick multi-select
In this paper we describe a new brute force algorithm for building the
-Nearest Neighbor Graph (-NNG). The -NNG algorithm has many
applications in areas such as machine learning, bio-informatics, and clustering
analysis. While there are very efficient algorithms for data of low dimensions,
for high dimensional data the brute force search is the best algorithm. There
are two main parts to the algorithm: the first part is finding the distances
between the input vectors which may be formulated as a matrix multiplication
problem. The second is the selection of the -NNs for each of the query
vectors. For the second part, we describe a novel graphics processing unit
(GPU) -based multi-select algorithm based on quick sort. Our optimization makes
clever use of warp voting functions available on the latest GPUs along with
use-controlled cache. Benchmarks show significant improvement over
state-of-the-art implementations of the -NN search on GPUs
Accelerating Nearest Neighbor Search on Manycore Systems
We develop methods for accelerating metric similarity search that are
effective on modern hardware. Our algorithms factor into easily parallelizable
components, making them simple to deploy and efficient on multicore CPUs and
GPUs. Despite the simple structure of our algorithms, their search performance
is provably sublinear in the size of the database, with a factor dependent only
on its intrinsic dimensionality. We demonstrate that our methods provide
substantial speedups on a range of datasets and hardware platforms. In
particular, we present results on a 48-core server machine, on graphics
hardware, and on a multicore desktop
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