Andrew Danner

Cache-oblivious data structures for orthogonal range searching

We develop cache-oblivious data structures for orthogonal range searching, the problem of finding all <i>T</i> points in a set of <i>N</i> points in <i>IR^d</i> lying in a query hyper-rectangle. Cache-oblivious data structures are designed to be efficient in arbitrary memory hierarchies.We describe a dynamic linear-size data structure that answers <i>d</i>-dimensional queries in <i>O((N/B)^1-1/d+T/B)</i> memory transfers, where <i>B</i> is the block size of any two levels of a multilevel memory hierarchy. A point can be inserted into or deleted from this data structure in <i>O(log²_B N)</i> memory transfers. We also develop a static structure for the two-dimensional case that answers queries in <i>O(log_B N+T/B)</i> memory transfers using <i>O(N log₂² N)</i> space. The analysis of the latter structure requires that <i>B=2^{2^c}</i> for some non-negative integer constant <i>c</i>.

From Point Cloud to Grid DEM: A Scalable Approach

Given a set S of points in ℝ3 sampled from an elevation function H : ℝ2 → ℝ, we present a scalable algorithm for constructing a grid digital elevation model (DEM). Our algorithm consists of three stages: First, we construct a quad tree on S to partition the point set into a set of non-overlapping segments. Next, for each segment q, we compute the set of points in q and all segments neighboring q. Finally, we interpolate each segment independently using points within the segment and its neighboring segments.

I/O-efficient point location using persistent B-trees

We present an external planar point location data structure that is I/O-efficient both in theory and practice.The developed structure uses linear space and answers a query in optimal O(log BN) I/Os, where B is the disk block size. It is based on a persistent B-tree, and all previously developed such structures assume a total order on the elements in the structure. As a theoretical result of independent interest, we show how to remove this assumption.Most previous theoretical I/O-efficient planar point location structures are relatively complicated and have not been implemented. Based on a bucket approach, Vahrenhold and Hinrichs therefore developed a simple and practical, but theoretically non-optimal, heuristic structure. We present an extensive experimental evaluation that shows that, on a range of real-world Geographic Information Systems (GIS) data, our structure uses a similar number of I/Os as the structure of Vahrenhold and Hinrichs to answer a query. On a synthetically generated worst-case dataset our structure uses significantly fewer I/Os.

A support program for introductory CS courses that improves student performance and retains students from underrepresented groups

In line with institutions across the United States, the Computer Science Department at Swarthmore College has faced the challenge of maintaining a demographic composition of students that matches the student body as a whole. To combat this trend, our department has made a concerted effort to revamp our introductory course sequence to both attract and retain more women and minority students. The focus of this paper is the changes instituted in our Introduction to Computer Science course (i.e., CS1) intended for both majors and non-majors. In addition to changing the content of the course, we introduced a new student mentoring program that is managed by a full-time coordinator and consists of undergraduate students who have recently completed the course. This paper describes these efforts in detail, including the extension of these changes to our CS2 course and the associated costs required to maintain these efforts. We measure the impact of these changes by tracking student enrollment and performance over 13 academic years. We show that, unlike national trends, enrollment from underrepresented groups has increased dramatically over this time period. Additionally, we show that the student mentoring program has increased both performance and retention of students, particularly from underrepresented groups, at statistically significant levels.

I/O-Efficient Hierarchical Watershed Decomposition of Grid Terrain Models

Recent progress in remote sensing has made massive amounts of high resolution terrain data readily available. Often the data is distributed as regular grid terrain models where each grid cell is associated with a height. When terrain analysis applications process such massive terrain models, data movement between main memory and slow disk (I/O), rather than CPU time, often becomes the performance bottleneck. Thus it is important to consider I/O-efficient algorithms for fundamental terrain problems. One such problem is the hierarchical decomposition of a grid terrain model into watersheds—regions where all water flows towards a single common outlet. Several different hierarchical watershed decompositions schemes have been described in the hydrology literature. One important such scheme is the Pfafstetter label method where each watershed is assigned a unique label and each grid cell is assigned a sequence of labels corresponding to the (nested) watersheds to which it belongs.

Hybrid MPI/GPU interpolation for grid DEM construction

The proliferation of lidar technology in remote sensing has resulted in extremely large, high resolution point clouds covering a wide variety of terrain. Constructing a grid digital elevation model (DEM) from these large data sets requires extensive computational resources and ample disk space. We propose a framework for leveraging modern computing resources including multi-core distributed systems and general purpose GPU computing to reduce computational bottlenecks and accelerate DEM construction. We employ an I/O-efficient strategy using quad trees to automatically partition the lidar point clouds into a set of independent work bundles. We then distribute these work bundles to multiple GPU-equipped hosts which independently interpolate a portion of the DEM and return partial results. Finally, we gather the partial results and assemble the final DEM I/O-efficiently. Our approach balances I/O, computation, and network communication to reduce bottlenecks. Experimental results show that our approach scales linearly with the number of compute hosts, and achieves speed-ups of 25 × or greater using GPU computing. These results make it practical to use more complex interpolation methods such as regularized splines with tension, which provide geomorphological advantages over simpler interpolation methods such as linear interpolation, nearest neighbor interpolation, or natural neighbor interpolation.

Integrating Parallel and Distributed Computing Topics into an Undergraduate CS Curriculum

We present changes to our undergraduate computer science curriculum for a small liberal arts college. The changes are designed to incorporate parallel and distributed computing topics into all levels of our curriculum, with the goal of ensuring that all graduating CS majors have exposure to, and experience with, parallel and distributed computing. Our effort is motivated by the ACM/IEEE Ironman Curriculum, which includes a increased focus on these important topics. In addition, we use the NSF/IEEE-TCPP model curriculum as a guide in our effort. Because of the small size of our department, and the breadth constraints of a liberal arts college, we face some unique challenges. Our multi-year effort involves at least six courses in our curriculum. Of these courses, one is a new introductory-level course, while the others are existing courses whose content has been modified to include more focus on these important topics. We present our curricular changes and we discuss an initial evaluation of the first implementation of these changes.

Pervasive parallel and distributed computing in a liberal arts college curriculum

Abstract We present a model for incorporating parallel and distributed computing (PDC) throughout an undergraduate CS curriculum. Our curriculum is designed to introduce students early to parallel and distributed computing topics and to expose students to these topics repeatedly in the context of a wide variety of CS courses. The key to our approach is the development of a required intermediate-level course that serves as an introduction to computer systems and parallel computing. It serves as a requirement for every CS major and minor and is a prerequisite to upper-level courses that expand on parallel and distributed computing topics in different contexts. With the addition of this new course, we are able to easily make room in upper-level courses to add and expand parallel and distributed computing topics. The goal of our curricular design is to ensure that every graduating CS major has exposure to parallel and distributed computing, with both a breadth and depth of coverage. Our curriculum is particularly designed for the constraints of a small liberal arts college, however, much of its ideas and its design are applicable to any undergraduate CS curriculum.