2,503 research outputs found
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
Adaptive GPU-accelerated force calculation for interactive rigid molecular docking using haptics
Molecular docking systems model and simulate in silico the interactions of intermolecular binding. Haptics-assisted docking enables the user to interact with the simulation via their sense of touch but a stringent time constraint on the computation of forces is imposed due to the sensitivity of the human haptic system. To simulate high fidelity smooth and stable feedback the haptic feedback loop should run at rates of 500 Hz to 1 kHz. We present an adaptive force calculation approach that can be executed in parallel on a wide range of Graphics Processing Units (GPUs) for interactive haptics-assisted docking with wider applicability to molecular simulations. Prior to the interactive session either a regular grid or an octree is selected according to the available GPU memory to determine the set of interatomic interactions within a cutoff distance. The total force is then calculated from this set. The approach can achieve force updates in less than 2 ms for molecular structures comprising hundreds of thousands of atoms each, with performance improvements of up to 90 times the speed of current CPU-based force calculation approaches used in interactive docking. Furthermore, it overcomes several computational limitations of previous approaches such as pre-computed force grids, and could potentially be used to model receptor flexibility at haptic refresh rates
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
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