Research Output

Now showing 1 - 10 of 35
• Publication
Breadth first search on massive graphs
(American Mathematical Society, 2009)
We consider the problem of Breadth First Search (BFS) traversal on massive sparse undirected graphs. Despite the existence of simple linear time algorithms in the RAM model, it was considered non-viable for massive graphs because of the I/O cost it incurs. Munagala and Ranade [29] and later Mehlhorn and Meyer [27] gave efficient algorithms (refered to as MR BFS and MM BFS, respectively) for computing BFS level decompositions in an external memory model. Ajwani et al. [3] implemented MR BFS and the randomized variant of MM BFS using the external memory library STXXL and gave a comparative study of the two algorithms on various graph classes. In this paper, we review and extend that result demonstrating the effectiveness and viability of the BFS implementations on various other synthetic and real world benchmarks. Furthermore, we present the implementation of the deterministic variant of MM BFS and show that in most cases, it outperforms the randomized variant.
188
• Publication
Automated Knowledge Hierarchy Assessment
(CEUR Workshop Proceedings, 2018-07-12)
Automated construction of knowledge hierarchies is gaining increasing attention to tackle the infeasibility of manually extracting and semantically linking millions of concepts. With the evolution of knowledge hierarchies, there is a need for measures to assess its temporal evolution, quantifying the similarities between different versions and identifying the relative growth of different subgraphs in the knowledge hierarchy. This work proposes a principled and scalable similarity measure, based on Katz similarity between concept nodes, for comparing knowledge hierarchies, modeled as generic Directed Acyclic Graphs (DAGs).
109
• Publication
A Geometric Distance Oracle for Large Real-World Graphs
(2018)
Many graph processing algorithms require determination of shortest-path distances between arbitrary numbers of node pairs. Since computation of exact distances between all node-pairs of a large graph, e.g., 10M nodes and up, is prohibitively expensive both in computational time and storage space, distance approximation is often used in place of exact computation. In this paper, we present a novel and scalable distance oracle that leverages the hyperbolic core of real-world large graphs for fast and scalable distance approximation. We show empirically that the proposed oracle significantly outperforms prior oracles on a random set of test cases drawn from public domain graph libraries. There are two sets of prior work against which we benchmark our approach. The first set, which often outperforms other oracles, employs embedding of the graph into low dimensional Euclidean spaces with carefully constructed hyperbolic distances, but provides no guarantees on the distance estimation error. The second set leverages Gromov-type tree contraction of the graph with the additive error guaranteed not to exceed $2\delta\log{n}$, where $\delta$ is the hyperbolic constant of the graph. We show that our proposed oracle 1) is significantly faster than those oracles that use hyperbolic embedding (first set) with similar approximation error and, perhaps surprisingly, 2) exhibits substantially lower average estimation error compared to Gromov-like tree contractions (second set). We substantiate our claims through numerical computations on a collection of a dozen real world networks and synthetic test cases from multiple domains, ranging in size from 10s of thousand to 10s of millions of nodes.
94
• Publication
Characterizing the Performance of Flash Memory Storage Devices and Its Impact on Algorithm Design
(Springer, 2008-06-25)
Initially used in digital audio players, digital cameras, mobile phones, and USB memory sticks, flash memory may become the dominant form of end-user storage in mobile computing, either completely replacing the magnetic hard disks or being an additional secondary storage. We study the design of algorithms and data structures that can exploit the flash memory devices better. For this, we characterize the performance of NAND flash based storage devices, including many solid state disks. We show that these devices have better random read performance than hard disks, but much worse random write performance. We also analyze the effect of misalignments, aging and past I/O patterns etc. on the performance obtained on these devices. We show that despite the similarities between flash memory and RAM (fast random reads) and between flash disk and hard disk (both are block based devices), the algorithms designed in the RAM model or the external memory model do not realize the full potential of the flash memory devices. We later give some broad guidelines for designing algorithms which can exploit the comparative advantages of both a flash memory device and a hard disk, when used together.
• Publication
Creating and Characterising Electricity Load Profiles of Residential Buildings
(Springer, 2020-09-18)
Intelligent planning, control and forecasting of electricity usage has become a vitally important element of the modern conception of the energy grid. Electricity smart-meters permit the sequential measurement of electricity usage at an aggregate level within a dwelling at regular time intervals. Electricity distributors or suppliers are interested in making general decisions that apply to large groups of customers, making it necessary to determine an appropriate electricity usage behaviour-based clustering of these data to determine appropriate aggregate load profiles. We perform a clustering of time series data associated with 3670 residential smart meters from an Irish customer behaviour trial and attempt to establish the relationship between the characteristics of each cluster based upon responses provided in an accompanying survey. Our analysis provides interesting insights into general electricity usage behaviours of residential consumers and the salient characteristics that affect those behaviours. Our characterisation of the usage profiles at a fine-granularity level and the resultant insights have the potential to improve the decisions made by distribution and supply companies, policy makers and other stakeholders, allowing them, for example, to optimise pricing, electricity usage, network investment strategies and to plan policies to best affect social behavior.
• Publication
Co-optimizing application partitioning and network topology for a reconfigurable interconnect
(Elsevier, 2016-10)
To realize the full potential of a high-performance computing system with a reconfigurable interconnect, there is a need to design algorithms for computing a topology that will allow for a high-throughput load distribution, while simultaneously partitioning the computational task graph of the application for the computed topology. In this paper, we propose a new framework that exploits such reconfigurable interconnects to achieve these interdependent goals, i.e., to iteratively co-optimize the network topology configuration, application partitioning and network flow routing to maximize throughput for a given application. We also present a novel way of computing a high-throughput initial topology based on the structural properties of the application to seed our co-optimizing framework. We show the value of our approach on synthetic graphs that emulate the key characteristics of a class of stream computing applications that require high throughput. Our experiments show that the proposed technique is fast and computes high-quality partitions of such graphs for a broad range of hardware parameters that varies the bottleneck from computation to communication. Finally, we show how using a particular topology as a seed to our framework significantly reduces the time to compute the final topology.
• Publication
Generating synthetic task graphs for simulating stream computing systems
(Elsevier, 2013-10)
Stream-computing is an emerging computational model for performing complex operations on and across multi-source, high-volume data flows. The pool of mature publicly available applications employing this model is fairly small, and therefore the availability of workloads for various types of applications is scarce. Thus, there is a need for synthetic generation of large-scale workloads to drive simulations and estimate the performance of stream-computing applications at scale. We identify the key properties shared by most task graphs of stream-computing applications and use them to extend known random graph generation concepts with stream computing specific features, providing researchers with realistic input stream graphs. Our graph generation techniques serve the purpose of covering a disparity of potential applications and user input. Our first "domain-specific" framework exhibits high user-controlled configurability while the second "application- agnostic" framework focuses solely on emulating the key properties of general stream-computing systems, at the loss of domain-specific fine-tuning. © 2013 Elsevier Inc. All rights reserved.
• Publication
Geometric Algorithms for Private-Cache Chip Multiprocessors
(Springer, 2010-09-08)
We study techniques for obtaining efficient algorithms for geometric problems on private-cache chip multiprocessors.