Publications
Uniform Random Sampling in Polyhedra
We propose a method for generating uniform samples among a domain of integer points defined by a polyhedron in a multidimensional space. The method extends to domains defined by parametric polyhedra, in which a subset of the variables are symbolic. We motivate this work by a list of applications for
Systems and methods for energy proportional scheduling
A compilation system using an energy model based on a set of generic and practical hardware and software parameters is presented. The model can represent the major trends in energy consumption spanning potential hardware configurations using only parameters available at compilation time. Experimental verification indicates that the model is nimble
Systems and Methods for Minimizing Communications
A system for allocation of one or more data structures used in a program across a number of processing units takes into account a memory access pattern of the data structure, and the amount of total memory available for duplication across the several processing units. Using these parameters duplication factors
G2: A Network Optimization Framework for High-Precision Analysis of Bottleneck and Flow Performance
Congestion control algorithms for data networks have been the subject of intense research for the last three decades. While most of the work has focused around the characterization of a flow’s bottleneck link, understanding the interactions amongst links and the ripple effects that perturbations in a link can cause on
Systems and Methods for Efficient Targeting
A system for determining the physical path of an object can map several candidate paths to a suitable path space that can be explored using a convex optimization technique. The optimization technique may take advantage of the typical sparsity of the path space and can identify a likely physical path
Passive Tracking of Objects Using Bistatic Dual-Polarization Receivers
A system for detecting and/or tracking a moving object uses signals received from the object at two or more receivers, each of which is dual polarized. At each receiver, the component of the received signal associated with the polarization thereof is separated from the non-polarization-based component, and the modulation of
Combining Tensor Decompositions and Graph Analytics to Provide Cyber Situational Awareness at HPC Scale
This paper describes MADHAT (Multidimensional Anomaly Detection fusing HPC, Analytics, and Tensors), an integrated workflow that demonstrates the applicability of HPC resources to the problem of maintaining cyber situational awareness. MADHAT combines two high-performance packages: ENSIGN for large-scale sparse tensor decompositions and HAGGLE for graph analytics. Tensor decompositions isolate coherent
Automatic Parallelization to Asynchronous Task- Based Runtimes Through a Generic Runtime Layer
With the end of Moore’s law, asynchronous taskbased parallelism has seen growing support as a parallel programming paradigm, with the runtime system offering such advantages as dynamic load balancing, locality, and scalability. However, there has been a proliferation of such programming systems in recent years, each of which presents different
Fast and Scalable Distributed Tensor Decompositions
Tensor decomposition is a prominent technique for analyzing multi-attribute data and is being increasingly used for data analysis in different application areas. Tensor decomposition methods are computationally intense and often involve irregular memory accesses over large-scale sparse data. Hence it becomes critical to optimize the execution of such data intensive