Abstract

In this work we show that given a set S of n points with coordinates on an n × n grid, we can construct data structures for (i) reporting and (ii) counting the maximal points in an axes-parallel query rectangle in sub-logarithmic time. We assume our model of computation to be the word RAM with size of each word being log n bits.

Abstract

We present output sensitive techniques for the generalized reporting versions of the planar range maxima problem and the planar range convex hull problem. Our solutions are in the pointer machine model, for orthogonal range queries on a static point set. We solve the planar range maxima problem for two-sided, three-sided and four-sided queries. We achieve a query time of O(logn + c) using O(n) space for the two-sided case, where n denotes the number of stored points and c the number of colors reported. For the three-sided case, we achieve query time O(log2 n + clogn) using O(n) space while for four-sided queries we answer queries in O(log3 n + clog2 n) using O(nlogn) space. For the planar range convex hull problem, we provide a solution that answers queries in O(log2 n + clogn) time, using O(nlog2 n) space.

Abstract

In this work, we consider the problem of finding the closest pair (in L 1 metric) of points in an orthogonal query rectangle. Given a set of n static points on a U× U grid, we preprocess these points into a data structure of size O (m f (m) log 2 m) that can be queried in time O ((g (m)+ log log m) log 3 m), for m= O (n log U) and (i) f (m)= O (1) and g (m)= O (log ε m);(ii) f (m)= O (log log m) and g (m)= O (log log m);(iii) f (m)= O (log ε m) and g (m)= O (1). Here ε: 0< ε< 1 is a small but arbitrary constant.

Abstract

A t-ruling set of a graph G=(V, E) is a vertex-subset S⊆ V that is independent and satisfies the property that every vertex v∈ V is at a distance of at most t hops from some vertex in S. A maximal independent set (MIS) is a 1-ruling set. Extending results from Kothapalli et al.(FSTTCS 2012) this note presents a randomized algorithm for computing, with high probability, a t-ruling set in O (t⋅ log 1/(t-1) n) rounds for 2< t≤√(log log n) and in (O (√(log log n))) rounds for t>√(log log n).

Abstract

Range searching is a primal problem in computational geometry with applications to database systems, mobile computing, geographical information systems, and the like. Defined simply, the problem is to preprocess a given a set of points in a d-dimensional space so that the points that lie inside an orthogonal query rectangle can be efficiently reported. Many practical applications of range trees require one to process a massive amount of points and a massive number of queries. In this context, we propose an efficient parallel implementation of range trees on manycore architectures such as GPUs. We extend our implementation to query processing. While queries can be batched together to exploit interquery parallelism, we also utilize intra-query parallelism. This inter- and intra-query parallelism greatly reduces the per query latency thereby increasing the throughput. On an input of 1 M points in a 2-dimensional space, our implementation on a single Nvidia GTX 580 GPU for constructing a range tree shows an improvement of 12X over a 12-threaded CPU implementation. We also achieve an average throughput of 10 M queries per second for answering 4 M queries on a range tree containing 1 M points on a Nvidia GTX 580 GPU. We extend our implementation to an application where we seek to report the set of maximal points in a given orthogonal query rectangle.

Abstract

Multiplying a sparse matrix with a vector, denoted spmv, is a fundamental operation in linear algebra with several applications. Hence, efficient and scalable implementation of spmv has been a topic of immense research. Recent efforts are aimed at implementations on GPUs, multicore architectures, and such emerging computational platforms. Owing to the highly irregular nature of spmv, it is observed that GPUs and CPUs can offer comparable performance.

Abstract

GPUs with their massive Single Instruction Multiple Data (SIMD) capability have become attractive for scientific computation. The Kirchhoff-Helmholtz Transform (KHT) is a two dimensional integral commonly used in numerical reconstruction of the object from its experimentally measured diffraction pattern (called hologram). We explore the evaluation of KHT using GPU at various levels of optimisation. These optimisations are of two types:(a) algorithmic: exploiting the symmetries inherent in KHT, and (b) programmatic: optimizations that are specific to the GPU architecture like the lookup tables, scheduling of read/writes. From the numerical experiments, we report the speedup for each level of optimisation.

Abstract

We consider the planar convex hull range query problem. Let P be a set of points in the plane. We preprocess these points into a data structure such that given an orthogonal range query, we can report the convex hull of the points in the range in O (log2 n+ h) time, where h is the size of the output. The data structure uses O (n log n) space. This improves the previous bound of O (log5 n+ h) time and O (n log2 n) space. Given a range query, it also supports extreme points in a given direction, tangent queries through a given point, and line-hull intersection queries on the points in the range in time O (log2 n) for each orthogonal query and O (log n) time for each additional query on that range. These problems have not been studied before.

Abstract

This paper initiates formal analysis of a simple, distributed algorithm for community detection on networks. We analyze an algorithm that we call Max-LPA, both in terms of its convergence time and in terms of the “quality” of the communities detected. Max-LPA is an instance of a class of community detection algorithms called label propagation algorithms. As far as we know, most analysis of label propagation algorithms thus far has been empirical in nature and in this paper we seek a theoretical understanding of label propagation algorithms. In our main result, we define a clustered version of Erdös-Rényi random graphs with clusters V 1, V 2, …, V k where the probability p, of an edge connecting nodes within a cluster V i is higher than p′, the probability of an edge connecting nodes in distinct clusters. We show that even with fairly general restrictions on p and p′ ( p=Ω(1n1/4−ϵ) for any ε > 0, p′ = O(p 2), where n is the number of nodes), Max-LPA detects the clusters V 1, V 2, …, V n in just two rounds. Based on this and on empirical results, we conjecture that Max-LPA can correctly and quickly identify communities on clustered Erdös-Rényi graphs even when the clusters are much sparser, i.e., with p=clognn for some c > 1.

Abstract

Sorting has been a topic of immense research value since the inception of Computer Science. Hybrid computing on multicore architectures involves computing simultaneously on a tightly coupled heterogeneous collection of devices. In this work, we consider a multicore CPU along with a many core GPU as our experimental hybrid platform. In this work, we present a hybrid comparison based sorting algorithm which utilizes a many-core GPU and a multi-core CPU to perform sorting. The algorithm is broadly based on splitting the input list according to a large number of splitters followed by creating independent sub lists. Sorting the independent sub lists results in sorting the entire original list. On a CPU+GPU platform consisting of an Intel i7 980 and an Nvidia GTX 580, our algorithm achieves a 20% gain over the current best known comparison sort result that was published by Davidson et. al. [In Par 2012]. On the above experimental platform, our results are better by 40% on average over a similar GPU-alone algorithm proposed by Leischner et. al. [IPDPS 2010]. Our results also show that our algorithm and its implementation scale with the size of the input. We also show that such performance gains can be obtained on other hybrid CPU+GPU platforms.