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Exciting research challenges often present themselves as complex problems-they are inherently difficult to understand and require multiple domains of expertise to solve. As a result, teamwork is becoming the everyday norm in most professional settings and is often required when studying topical problems (e.g., autonomous vehicles, cyberphysical, renewable energy, among others). For students, learning to operate within, and eventually lead, a team is a necessary and valuable skill that will benefit them throughout their professional lives.

Quantum key distribution (QKD) is an innovative technology that exploits the laws of quantum mechanics to generate and distribute a shared cryptographic key for secure communications. The unique nature of QKD ensures that eavesdropping on quantum communications necessarily introduces detectable errors which is desirable for high-security environments. QKD systems have been demonstrated in both freespace and optical fiber configurations, gaining global interest from national laboratories, commercial entities, and the U.S. Department of Defense. However, QKD is a nascent technology where realized systems are constructed from non-ideal components, which can significantly impact system performance and security. In this article, we describe QKD technology as part of a secure communications solution and identify vulnerabilities associated with practical network architectures. In particular, we examine the performance of decoy state enabled QKD systems against a modeled photon number splitting attack and suggest an improvement to the decoy state protocol security condition that does not assume a priori knowledge of the QKD channel efficiency.

Quantum key distribution (QKD) exploits the laws of quantum physics to generate shared secret cryptographic keys and can detect eavesdroppers during the key generation process. However, previous QKD research has focused more on theory than practice.

Empirical optimizers like ATLAS have been very effective in optimizing computational kernels in libraries. The best choice of parameters such as tile size and degree of loop unrolling is determined in ATLAS by executing different versions of the computation. In contrast, optimizing compilers use a model-driven approach to program transformation. While the model-driven approach of optimizing compilers is generally orders of magnitude faster than ATLAS-like library generators, its effectiveness can be limited by the accuracy of the performance models used. In this paper, we describe an approach where a class of computations is modeled in terms of constituent operations that are empirically measured, thereby allowing modeling of the overall execution time. The performance model with empirically determined cost components is used to select library calls and choose data layout transformations in the context of the Tensor Contraction Engine, a compiler for a high-level domain-specific language for expressing computational models in quantum chemistry. The effectiveness of the approach is demonstrated through experimental measurements on representative computations from quantum chemistry.

The need to evaluate expression trees involving large objects arises in scientific computing applications such as electronic structure calculations. Often, the tree node objects are so large that only a subset of them can fit into memory at a time. This paper addresses the problem of finding an evaluation order of the nodes in a given expression tree that uses the least amount of memory. We present an algorithm that finds an optimal evaluation order in @Q(nlog^2n) time for an n-node expression tree and prove its correctness. We demonstrate the utility of our algorithm using representative equations from quantum chemistry.

Compile-time optimizations involve a number of transformations such as loop permutation, fusion, tiling, array contraction etc. The selection of the appropriate transformation to minimize the execution time is a challenging task. We address this problem in the context of tensor contraction expressions involving arrays too large to fit in main memory. Domain-specific features of the computation are exploited to develop an integrated framework that facilitates the exploration of the entire search space of optimizations. In this paper, we discuss the exploration of the space of loop fusion and tiling transformations in order to minimize the disk I/O cost. These two transformations are integrated and pruning strategies are presented that significantly reduce the number of loop structures to be evaluated for subsequent transformations. The evaluation of the framework using representative contraction expressions from quantum chemistry shows a dramatic reduction in the size of the search space using the strategies presented. Copyright © 2007 John Wiley & Sons, Ltd.

The Global Arrays (GA) toolkit provides a shared-memory programming model in which data locality is explicitly managed by the programmer. It inter-operates with MPI and supports a variety of language bindings. The Disk Resident Arrays (DRA) model extends the GA programming model to secondary storage. GA and DRA together provide a convenient programming model that encourages locality-aware programming by the user, while presenting a high-level abstraction. High performance depends on the appropriate distribution of the data in the disk-resident arrays. In this paper, we discuss the addition of layout transformation support to DRA. The implementation of an efficient parallel layout transformation algorithm is done on top of existing GA/DRA functions; thus GA/DRA is itself used in implementing the enhanced DRA functionality. Experimental performance data is provided that demonstrates the effectiveness of the new layout transformation functionality.

The Organic Grid is a biologically inspired and fully decentralized approach to the organization of computation that is based on the autonomous scheduling of strongly mobile agents on a peer-to-peer network. Through the careful design of agent behavior, the emerging organization of the computation can be customized for different classes of applications. In this paper, we report on our experience in adapting the general framework to run two representative applications on our Organic Grid prototype: the National Center for Biotechnology Information (NCBI) basic local alignment search tool (BLAST) code for sequence alignment, and the Cannon's algorithm for matrix multiplication. The first is an example of independent task application, a type of application commonly used for grid scheduling research because of its easily decomposable nature and absence of intra-node communication. The second is a popular block algorithm for parallel matrix multiplication, and represents a challenging application for grid platforms because of its highly structured and synchronous communication pattern. Agent behavior completely determines the way computation is organized on the Organic Grid. We intentionally chose two applications at opposite ends of the distributed computing spectrum having very different requirements in terms of communication topology, resource use, and response to faults. We detail the design of the agent behavior and show how the different requirements can be satisfied. By encapsulating application code and scheduling functionality into mobile agents, we decouple both computation and scheduling from the underlying grid infrastructure. In the resulting system, every node can inject a computation onto the grid; the computation naturally organizes itself around available resources.

As both electronic structure methods and the computers on which they are run become increasingly complex, the task of producing robust, reliable, high-performance implementations of methods at a rapid pace becomes increasingly daunting. In this paper we present an overview of the Tensor Contraction Engine (TCE), a unique effort to address issues of both productivity and performance through automatic code generation. The TCE is designed to take equations for many-body methods in a convenient high-level form and acts like an optimizing compiler, producing an implementation tuned to the target computer system and even to the specific chemical problem of interest. We provide examples to illustrate the TCE approach, including the ability to target different parallel programming models, and the effects of particular optimizations.

This paper provides an overview of a program synthesis system for a class of quantum chemistry computations. These computations are expressible as a set of tensor contractions and arise in electronic structure modeling. The input to the system is a a high-level specification of the computation, from which the system can synthesize high-performance parallel code tailored to the characteristics of the target architecture. Several components of the synthesis system are described, focusing on performance optimization issues that they address.

The accurate modeling of the electronic structure of atoms and molecules is very computationally intensive. Many models of electronic structure, such as the Coupled Cluster approach, involve collections of tensor contractions. There are usually a large number of alternative ways of implementing the tensor contractions, representing different trade-offs between the space required for temporary intermediates and the total number of arithmetic operations. In this paper, we present an algorithm that starts with an operation-minimal form of the computation and systematically explores the possible space-time trade-offs to identify the form with lowest cost that fits within a specified memory limit. Its utility is demonstrated by applying it to a computation representative of a component in the CCSD(T) formulation in the NWChem quantum chemistry suite from Pacific Northwest National Laboratory.

This paper describes the basic concept of an integrated virtual learning system as an instructional tool for the development of DSP-based control schemes for motor drive applications. The system is comprised of a graphical user interface (GUI) front-end and a hardware-inthe-loop custom digital signal processor (DSP) for rapid prototyping and efficient testing of digital control algorithms. The circuit design and control algorithm development of a pulse width modulation (PWM) voltage source inverter (VSI) for three-phase brushless dc (BLDC) motor control applications is used to describe the functions of the system. It is shown that the virtual learning system provides a cost-effective learning tool for students or engineers in training and can serve as a supplement to a conventional laboratory based system.