Stephen Fitzpatrick

Distributed Coordination through Anarchic Optimization

In this chapter, a peer-to-peer algorithm is described for approximately solving distributed, real-time, constraint optimization problems. The ANTS challenge problem is formulated as a distributed constraint optimization problem; an approximation version of the classical problem of graph k-coloring is formulated as a distributed constraint optimization problem to enable simple experimental assessment of the algorithm’s performance.

An Experimental Assessment of a Stochastic, Anytime, Decentralized, Soft Colourer for Sparse Graphs

This paper reports on a simple, decentralized, anytime, stochastic, soft graph-colouring algorithm. The algorithm is designed to quickly reduce the number of colour conflicts in large, sparse graphs in a scalable, robust, low-cost manner. The algorithm is experimentally evaluated in a framework motivated by its application to resource coordination in large, distributed networks.

The automated transformation of abstract specifications of numerical algorithms into efficient array processor implementations

Abstract We present a set of program transformations which are applied automatically to convert abstract functional specifications of numerical algorithms into efficient implementations tailored to the AMT DAP array processor. The transformations are based upon a formal algebra of a functional array form, which provides a functional model of the array operations supported by the DAP programming language. The transformations are shown to be complete. We present specifications and derivations of two example algorithms: an algorithm for computing eigensystems and an algorithm for solving systems of linear equations. For the former, we compare the execution performance of the implementation derived by transformation with the performance of an independent, manually constructed implementation; the efficiency of the derived implementation matches that of the manually constructed implementation.

The Construction of Numerical Mathematical Software for the AMT DAP by Program Transformation

We have shown that it is possible mechanically to produce a highly efficient implementation tailored for execution on the AMT DAP 510 of a high-level functional specification. The functional specification is not biased in ways that would permit its efficient execution on a particular machine architecture, but is expressed in a way that gives a clear statement of the algorithm. Indeed, the functional specification may be used as the starting point for producing implementations tailored for execution on other machines (and will be used in this way in future investigations).

A Family of Data-Parallel Derivations

A good programmer attempts to minimize architecture-specific detail when writing a sequential implementation of an algorithm. Such good programming practice makes it possible to transport the implementation to other hardware architectures and thus minimize programmer effort. Indeed, high-level programming languages attempt to hide the detail required by particular machine architectures and thus make it easier to construct programs and transport them. When using traditional methods to implement an algorithm for an advanced parallel architecture, the programmer faces the dilemma of

CSC: Criticality-Sensitive Coordination

Our Criticality-Sensitive Coordination (CSC) agents are designed to enhance the performance of a human-team working together in uncertain and dynamic settings by monitoring and adapting their plans as dictated by the evolution of the environment. Such situations model military scenarios such as a coordinated joint operations or enterprise settings such as multiple-project management. Among the many challenges in these situations are the large space of possible states due to uncertainty, the distributed / partial knowledge of current state and plan among the agents and the need to react in a timely manner to events that may not be in the original model. In fact, reaction alone is often insufficient as in environments where success depends on completing sequences of coupled actions, one needs to anticipate future difficulties and enable contingencies to alleviate potential hazards.

Deriving Efficient Parallel Implementations of Algorithms Operating on General Sparse Matrices Using Automatic Program Transformation

We show how efficient implementations can be derived from high-level functional specifications of numerical algorithms using automatic program transformation. We emphasize the automatic tailoring of implementations for manipulation of sparse data sets. Execution times are reported for a conjugate gradient algorithm.

The Tailoring of Abstract Functional Specifications of Numerical Algorithms for Sparse Data Structures through Automated Program Derivation and Transformation

The automated application of program transformations is used to derive, from abstract functional specifications of numerical mathematical algorithms, highly efficient imperative implementations tailored for execution on sequential, vector and array processors. Emphasis is placed on transformations which tailor implementations to use special programming techniques optimized for sparse matrices. We demonstrate that derived implementations attain superior execution performance than manual implementations for two significant algorithms.