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Adaptive heterogeneous parallelism for semi-empirical lattice dynamics in computational materials science.

Garba, Michael

Authors

Michael Garba



Contributors

Horacio Gonzalez-Velez
Supervisor

Daniel Roach
Supervisor

Abstract

With the variability in performance of the multitude of parallel environments available today, the conceptual overhead created by the need to anticipate runtime information to make design-time decisions has become overwhelming. Performance-critical applications and libraries carry implicit assumptions based on incidental metrics that are not portable to emerging computational platforms or even alternative contemporary architectures. Furthermore, the significance of runtime concerns such as makespan, energy efficiency and fault tolerance depends on the situational context. This thesis presents a case study in the application of both Mattsons prescriptive pattern-oriented approach and the more principled structured parallelism formalism to the computational simulation of inelastic neutron scattering spectra on hybrid CPU/GPU platforms. The original ad hoc implementation as well as new patternbased and structured implementations are evaluated for relative performance and scalability. Two new structural abstractions are introduced to facilitate adaptation by lazy optimisation and runtime feedback. A deferred-choice abstraction represents a unified space of alternative structural program variants, allowing static adaptation through model-specific exhaustive calibration with regards to the extrafunctional concerns of runtime, average instantaneous power and total energy usage. Instrumented queues serve as mechanism for structural composition and provide a representation of extrafunctional state that allows realisation of a market-based decentralised coordination heuristic for competitive resource allocation and the Lyapunov drift algorithm for cooperative scheduling.

Citation

GARBA, M. 2015. Adaptive heterogeneous parallelism for semi-empirical lattice dynamics in computational materials science. Robert Gordon University, PhD thesis.

Thesis Type Thesis
Deposit Date Jul 14, 2015
Publicly Available Date Jul 14, 2015
Public URL http://hdl.handle.net/10059/1237
Award Date Apr 30, 2015

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