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Physical Sciences and Mathematics Commons™
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Full-Text Articles in Physical Sciences and Mathematics
Search-Based Model-Driven Loop Optimizations For Tensor Contractions, Ajay Panyala
Search-Based Model-Driven Loop Optimizations For Tensor Contractions, Ajay Panyala
LSU Doctoral Dissertations
Complex tensor contraction expressions arise in accurate electronic structure models in quantum chemistry, such as the coupled cluster method. The Tensor Contraction Engine (TCE) is a high-level program synthesis system that facilitates the generation of high-performance parallel programs from tensor contraction equations. We are developing a new software infrastructure for the TCE that is designed to allow experimentation with optimization algorithms for modern computing platforms, including for heterogeneous architectures employing general-purpose graphics processing units (GPGPUs). In this dissertation, we present improvements and extensions to the loop fusion optimization algorithm, which can be used with cost models, e.g., for minimizing memory …
Extreme Scale Parallel Nbody Algorithm With Event Driven Constraint Based Execution Model, Chirag Dekate
Extreme Scale Parallel Nbody Algorithm With Event Driven Constraint Based Execution Model, Chirag Dekate
LSU Doctoral Dissertations
Traditional scientific applications such as Computational Fluid Dynamics, Partial Differential Equations based numerical methods (like Finite Difference Methods, Finite Element Methods) achieve sufficient efficiency on state of the art high performance computing systems and have been widely studied / implemented using conventional programming models. For emerging application domains such as Graph applications scalability and efficiency is significantly constrained by the conventional systems and their supporting programming models. Furthermore technology trends like multicore, manycore, heterogeneous system architectures are introducing new challenges and possibilities. Emerging technologies are requiring a rethinking of approaches to more effectively expose the underlying parallelism to the applications …
Advanced Semantics For Accelerated Graph Processing, Dylan Thomas Stark
Advanced Semantics For Accelerated Graph Processing, Dylan Thomas Stark
LSU Doctoral Dissertations
Large-scale graph applications are of great national, commercial, and societal importance, with direct use in fields such as counter-intelligence, proteomics, and data mining. Unfortunately, graph-based problems exhibit certain basic characteristics that make them a poor match for conventional computing systems in terms of structure, scale, and semantics. Graph processing kernels emphasize sparse data structures and computations with irregular memory access patterns that destroy the temporal and spatial locality upon which modern processors rely for performance. Furthermore, applications in this area utilize large data sets, and have been shown to be more data intensive than typical floating-point applications, two properties that …