Investigating applications portability with the Uintah DAG-based runtime system on PetaScale supercomputers

pre-printPresent trends in high performance computing present formidable challenges for applications code using multicore nodes possibly with accelerators and/or co-processors and reduced memory while still attaining scalability. Software frameworks that execute machine-independent applications code using a runtime system that shields users from architectural complexities offer a possible solution. The Uintah framework for example, solves a broad class of large-scale problems on structured adaptive grids using fluid-flow solvers coupled with particle-based solids methods. Uintah executes directed acyclic graphs of computational tasks with a scalable asynchronous and dynamic runtime system for CPU cores and/or accelerators/coprocessors on a node. Uintah's clear separation between application and runtime code has led to scalability increases of 1000x without significant changes to application code. This methodology is tested on three leading Top500 machines; OLCF Titan, TACC Stampede and ALCF Mira using three diverse and challenging applications problems. This investigation of scalability with regard to the different processors and communications performance leads to the overall conclusion that the adaptive DAG-based approach provides a very powerful abstraction for solving challenging multi-scale multi-physics engineering problems on some of the largest and most powerful computers available today

M.: Investigating applications portability with the uintah dag-based runtime system on petascale supercomputers

ABSTRACT Present trends in high performance computing present formidable challenges for applications code using multicore nodes possibly with accelerators and/or co-processors and reduced memory while still attaining scalability. Software frameworks that execute machineindependent applications code using a runtime system that shields users from architectural complexities offer a possible solution. The Uintah framework for example, solves a broad class of large-scale problems on structured adaptive grids using fluid-flow solvers coupled with particle-based solids methods. Uintah executes directed acyclic graphs of computational tasks with a scalable asynchronous and dynamic runtime system for CPU cores and/or accelerators/coprocessors on a node. Uintah's clear separation between application and runtime code has led to scalability increases of 1000x without significant changes to application code. This methodology is tested on three leading Top500 machines; OLCF Titan, TACC Stampede and ALCF Mira using three diverse and challenging applications problems. This investigation of scalability with regard to the different processors and communications performance leads to the overall conclusion that the adaptive DAG-based approach provides a very powerful abstraction for solving challenging multiscale multi-physics engineering problems on some of the largest and most powerful computers available today

M.: Investigating applications portability with the uintah dag-based runtime system on petascale supercomputers

Abstract: Present trends in high performance computing present formidable challenges for applications code using multicore nodes possibly with accelerators and/or co-processors and reduced memory while still attaining scalability. Software frameworks that execute machineindependent applications code using a runtime system that shields users from architectural complexities offer a possible solution. The Uintah framework for example, solves a broad class of large-scale problems on structured adaptive grids using fluid-flow solvers coupled with particle-based solids methods. Uintah executes directed acyclic graphs of computational tasks with a scalable asynchronous and dynamic runtime system for CPU cores and/or accelerators/coprocessors on a node. Uintah's clear separation between application and runtime code has led to scalability increases of 1000x without significant changes to application code. This methodology is tested on three leading Top500 machines; OLCF Titan, TACC Stampede and ALCF Mira using three diverse and challenging applications problems. This investigation of scalability with regard to the different processors and communications performance leads to the overall conclusion that the adaptive DAG-based approach provides a very powerful abstraction for solving challenging multiscale multi-physics engineering problems on some of the largest and most powerful computers available today. Investigating Applications Portability with the ABSTRACT Present trends in high performance computing present formidable challenges for applications code using multicore nodes possibly with accelerators and/or co-processors and reduced memory while still attaining scalability. Software frameworks that execute machineindependent applications code using a runtime system that shields users from architectural complexities offer a possible solution. The Uintah framework for example, solves a broad class of large-scale problems on structured adaptive grids using fluid-flow solvers coupled with particle-based solids methods. Uintah executes directed acyclic graphs of computational tasks with a scalable asynchronous and dynamic runtime system for CPU cores and/or accelerators/coprocessors on a node. Uintah's clear separation between application and runtime code has led to scalability increases of 1000x without significant changes to application code. This methodology is tested on three leading Top500 machines; OLCF Titan, TACC Stampede and ALCF Mira using three diverse and challenging applications problems. This investigation of scalability with regard to the different processors and communications performance leads to the overall conclusion that the adaptive DAG-based approach provides a very powerful abstraction for solving challenging multiscale multi-physics engineering problems on some of the largest and most powerful computers available today

Doctor of Philosophy

dissertationThe detonation of hundreds of explosive devices from either a transportation or storage accident is an extremely dangerous event. Motivation for this work came from a transportation accident where a truck carrying 16,000 kg of seismic boosters overturned, caught fire and detonated. The damage was catastrophic, creating a crater 24 m wide by 10 m deep in the middle of the highway. Our particular interest is understanding the fundamental physical mechanisms by which convective deflagration of cylindrical PBX-9501 devices can transition to a fully-developed detonation in transportation and storage accidents. Predictive computer simulations of large-scale deflagrations and detonations are dependent on the availability of robust reaction models embedded in a computational framework capable of running on massively parallel computer architectures. Our research group has been developing such models in the Uintah Computational Framework, which is capable of scaling up to 512 K cores. The current Deflagration to Detonation Transition (DDT) model merges a combustion model from Ward, Son, and Brewster that captures the effects of pressure and initial temperature on the burn rate, with a criteria model for burning in cracks of damaged explosives from Berghout et al., and a detonation model from Souers describing fully developed detonation. The prior extensive validation against experimental tests was extended to a wide range of temporal and spatial scales. We made changes to the reactant equation of state-enabling predictions of combustions, explosions, and detonations over a range of pressures spanning five orders of magnitude. A resolution dependence was eliminated from the reaction model facilitating large scale simulations to be run at a resolution of 2 mm without loss of fidelity. Adjustments were also made to slow down the flame propagation of conductive and convective deflagration. Large two- and three-dimensional simulations revealed two dominant mechanisms for the initiation of a DDT, inertial confinement and Impact to Detonation Transition. Understanding these mechanisms led to identifying ways to package and store explosive devices that reduced the probability of a detonation. We determined that the arrangement of the explosive cylinders and the number of devices packed in a box greatly affected the propensity to transition to a detonation