Randomized selection on the GPU

Monroe, Laura; Michalak, Sarah; Wendelberger, Joanne

doi:10.2172/1090658

Cited by 2 publications

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Sec. 2.1, random sampling similar to bootstrapping and the technique used in randomizedSelect [Monroe et al 2011] permits a rapid definition of buckets which will each contain a roughly uniform number of values from the full data set. The fast linear projection into buckets is borrowed from bucketSelect [Alabi et al 2012].…”

Section: An Algorithm For Selecting Multiple Order Statistics: Bucketmentioning

confidence: 99%

“…In 2011, several GPU selection algorithms were announced including an optimization based algorithm cuttingPlane [Beliakov 2011], a randomized but deterministic selection randomizedSelect [Monroe et al 2011], a radix selection radixSelect [Alabi et al 2012], and an algorithm based on distributive partitioning bucketSelect [Alabi et al 2012]. The performance of these four algorithms was extensively compared in [Alabi et al 2012] and all four algorithms are implemented in the software GGKS: Grinnell GPU k-selection [Alabi et al 2011].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Selecting Multiple Order Statistics with a Graphics Processing Unit

Blanchard

Opavsky

Uysaler

2016

ACM Trans. Parallel Comput.

View full text Add to dashboard Cite

Extracting a set of multiple order statistics from a huge data set provides important information about the distribution of the values in the full set of data. This article introduces an algorithm, bucketMultiSelect, for simultaneously selecting multiple order statistics with a graphics processing unit (GPU). Typically, when a large set of order statistics is desired the vector is sorted. When the sorted version of the vector is not needed, bucketMultiSelect significantly reduces computation time by eliminating a large portion of the unnecessary operations involved in sorting. For large vectors, bucketMultiSelect returns thousands of order statistics in less time than sorting the vector while typically using less memory. For vectors containing 2 28 values of type double, bucketMultiSelect selects the 101 percentile order statistics in less than 200ms and is more than 10× faster than sorting the vector with a GPU optimized radix sort.

show abstract

Section: An Algorithm For Selecting Multiple Order Statistics: Bucketmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Selecting Multiple Order Statistics with a Graphics Processing Unit

Blanchard

Opavsky

Uysaler

2016

ACM Trans. Parallel Comput.

View full text Add to dashboard Cite

show abstract

“…The k-selection is a fundamental problem in computer science, which has been widely studied. The state-of-the art parallel algorithm is due to Monroe et al [2011]. Their algorithm proceeds Algorithm 1: Parallel Chen-Han Algorithm Input: A triangle mesh M = (V, E, F ), the set of source points S = {si|si ∈ V, 1 ≤ i ≤ m}, the selection parameter k, and the number of GPU threads organize the newly-generated windows and update events 7: parallel process the update events 8: Until all windows are processed via an iterative probabilistic guess-and-check process on pivots for a three-way partition.…”

Section: Pch Algorithmmentioning

confidence: 99%

“…Selection and sorting can be fully implemented in parallel. We adopted the probabilistic parallel selection [Monroe et al 2011] and CUDA's thrust sorting [Bell and Hoberock 2011] in our implementation, but, surprisingly, observed poor performance.…”

Section: Implementation Detailsmentioning

confidence: 99%

Parallel chen-han (PCH) algorithm for discrete geodesics

Xiang

Xin

2014

ACM Trans. Graph.

View full text Add to dashboard Cite

In many graphics applications, the computation of exact geodesic distance is very important. However, the high computational cost of the existing geodesic algorithms means that they are not practical for large-scale models or time-critical applications. To tackle this challenge, we propose the parallel Chen-Han (or PCH) algorithm, which extends the classic Chen-Han (CH) discrete geodesic algorithm to the parallel setting. The original CH algorithm and its variant both lack a parallel solution because the windows (a key data structure that carries the shortest distance in the wavefront propagation) are maintained in a strict order or a tightly coupled manner, which means that only one window is processed at a time. We propose dividing the CH's sequential algorithm into four phases, window selection, window propagation, data organization, and events processing so that there is no data dependence or conflicts in each phase and the operations within each phase can be carried out in parallel. The proposed PCH algorithm is able to propagate a large number of windows simultaneously and independently. We also adopt a simple yet effective strategy to control the total number of windows. We implement the PCH algorithm on modern GPUs (such as Nvidia GTX 580) and analyze the performance in detail. The performance improvement (compared to the sequential algorithms) is highly consistent with GPU doubleprecision performance (GFLOPS). Extensive experiments on realworld models demonstrate an order of magnitude improvement in execution time compared to the state-of-the-art.

show abstract

Randomized selection on the GPU

Cited by 2 publications

References 0 publications

Selecting Multiple Order Statistics with a Graphics Processing Unit

Selecting Multiple Order Statistics with a Graphics Processing Unit

Parallel chen-han (PCH) algorithm for discrete geodesics

Contact Info

Product

Resources

About