Modeling synthetic aperture radar computation with Aspen

Spafford, Kyle; Vetter, Jeffrey S.; Benson, Thomas; Parker, M. N.

doi:10.1177/1094342013488262

Cited by 2 publications

(2 citation statements)

References 16 publications

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“…Second, we are extending our Aspen/FLAME domain-specific language to capture hierarchical details of system design. To address many of these concerns, ORNL created the domain-specific languages called Aspen (Spafford and Vetter 2012, 2015; Spafford et al, 2013; Peng and Vetter 2018; Lee et al, 2015) and FLAME (Belviranli and Vetter 2019) to model applications and architectures. Aspen allows users to create formal written descriptions of the applications and the architectures.…”

Section: Codesignmentioning

confidence: 99%

Abisko: Deep codesign of an architecture for spiking neural networks using novel neuromorphic materials

Vetter

Date

Fahim

et al. 2023

The International Journal of High Performance Computing Applica

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The Abisko project aims to develop an energy-efficient spiking neural network (SNN) computing architecture and software system capable of autonomous learning and operation. The SNN architecture explores novel neuromorphic devices that are based on resistive-switching materials, such as memristors and electrochemical RAM. Equally important, Abisko uses a deep codesign approach to pursue this goal by engaging experts from across the entire range of disciplines: materials, devices and circuits, architectures and integration, software, and algorithms. The key objectives of our Abisko project are threefold. First, we are designing an energy-optimized high-performance neuromorphic accelerator based on SNNs. This architecture is being designed as a chiplet that can be deployed in contemporary computer architectures and we are investigating novel neuromorphic materials to improve its design. Second, we are concurrently developing a productive software stack for the neuromorphic accelerator that will also be portable to other architectures, such as field-programmable gate arrays and GPUs. Third, we are creating a new deep codesign methodology and framework for developing clear interfaces, requirements, and metrics between each level of abstraction to enable the system design to be explored and implemented interchangeably with execution, measurement, a model, or simulation. As a motivating application for this codesign effort, we target the use of SNNs for an analog event detector for a high-energy physics sensor.

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Section: Codesignmentioning

confidence: 99%

Abisko: Deep codesign of an architecture for spiking neural networks using novel neuromorphic materials

Vetter

Date

Fahim

et al. 2023

The International Journal of High Performance Computing Applica

Self Cite

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“…Aspen extends these analytical modeling concepts to a formal DSL and adds extensibility and flexibility to analytical techniques; see Spafford and Vetter (2012) for a treatment of FFT on HPC architectures. Aspen is under active development and investigation for a variety of performance modeling contexts; see Spafford et al (2013) for a more recent example of the use of Aspen in the HPC space.…”

Section: Related Workmentioning

confidence: 99%

PANORAMA: An approach to performance modeling and diagnosis of extreme-scale workflows

Deelman

Carothers

Mandal

et al. 2016

The International Journal of High Performance Computing Applica

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Computational science is well established as the third pillar of scientific discovery and is on par with experimentation and theory. However, as we move closer toward the ability to execute exascale calculations and process the ensuing extreme-scale amounts of data produced by both experiments and computations alike, the complexity of managing the compute and data analysis tasks has grown beyond the capabilities of domain scientists. Thus, workflow management systems are absolutely necessary to ensure current and future scientific discoveries. A key research question for these workflow management systems concerns the performance optimization of complex calculation and data analysis tasks. The central contribution of this article is a description of the PANORAMA approach for modeling and diagnosing the run-time performance of complex scientific workflows. This approach integrates extreme-scale systems testbed experimentation, structured analytical modeling, and parallel systems simulation into a comprehensive workflow framework called Pegasus for understanding and improving the overall performance of complex scientific workflows.

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Modeling synthetic aperture radar computation with Aspen

Cited by 2 publications

References 16 publications

Abisko: Deep codesign of an architecture for spiking neural networks using novel neuromorphic materials

Abisko: Deep codesign of an architecture for spiking neural networks using novel neuromorphic materials

PANORAMA: An approach to performance modeling and diagnosis of extreme-scale workflows

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