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Title: Making Technological Timelines: Anticipatory Repair and Testing in High Performance Scientific Computing

Abstract

Think of some examples of repair in everyday life. Maybe you had a car accident and took your car to the body shop. Maybe the head came off your child’s doll and you had to glue it back on. Maybe the handle of your shovel cracked and you wrapped the cracked area with duct tape to hold it together. These are examples of what could be called reactive repair, where an unexpected accident initiates a sequence of action and decision-making that ends in repair. In these cases, most of the thinking and planning surrounding repair takes place after a breakdown has been identified. This type of repair is often taken to be distinct from deliberate design, as it occurs within the context of technology that is already in operation, often has an improvisational character, and may be performed by end users or technicians rather than credentialed experts. But does repair always have to be reactive? And if not, what does this tell us about the distinction between design and repair, and their respective roles in shaping technological change? The short answer is that repair, like design, can play a dynamic and forward-looking role in shaping technological trajectories – not onlymore » stabilizing existing systems, but anticipating change and generating new technological futures.« less

Authors:
ORCiD logo [1]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1360696
Report Number(s):
LA-UR-16-21095
Journal ID: ISSN 2159-9920
Grant/Contract Number:
AC52-06NA25396
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
continent.
Additional Journal Information:
Journal Volume: 6; Journal Issue: 1; Journal ID: ISSN 2159-9920
Publisher:
Paul Boshears
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING

Citation Formats

Sims, Benjamin. Making Technological Timelines: Anticipatory Repair and Testing in High Performance Scientific Computing. United States: N. p., 2017. Web.
Sims, Benjamin. Making Technological Timelines: Anticipatory Repair and Testing in High Performance Scientific Computing. United States.
Sims, Benjamin. Tue . "Making Technological Timelines: Anticipatory Repair and Testing in High Performance Scientific Computing". United States. doi:. https://www.osti.gov/servlets/purl/1360696.
@article{osti_1360696,
title = {Making Technological Timelines: Anticipatory Repair and Testing in High Performance Scientific Computing},
author = {Sims, Benjamin},
abstractNote = {Think of some examples of repair in everyday life. Maybe you had a car accident and took your car to the body shop. Maybe the head came off your child’s doll and you had to glue it back on. Maybe the handle of your shovel cracked and you wrapped the cracked area with duct tape to hold it together. These are examples of what could be called reactive repair, where an unexpected accident initiates a sequence of action and decision-making that ends in repair. In these cases, most of the thinking and planning surrounding repair takes place after a breakdown has been identified. This type of repair is often taken to be distinct from deliberate design, as it occurs within the context of technology that is already in operation, often has an improvisational character, and may be performed by end users or technicians rather than credentialed experts. But does repair always have to be reactive? And if not, what does this tell us about the distinction between design and repair, and their respective roles in shaping technological change? The short answer is that repair, like design, can play a dynamic and forward-looking role in shaping technological trajectories – not only stabilizing existing systems, but anticipating change and generating new technological futures.},
doi = {},
journal = {continent.},
number = 1,
volume = 6,
place = {United States},
year = {Tue May 23 00:00:00 EDT 2017},
month = {Tue May 23 00:00:00 EDT 2017}
}

Journal Article:
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  • Recent advances in both computational hardware and multidisciplinary science have given rise to an unprecedented level of complexity in scientific simulation software. This paper describes an ongoing grass roots effort aimed at addressing complexity in high-performance computing through the use of Component-Based Software Engineering (CBSE). Highlights of the benefits and accomplishments of the Common Component Architecture (CCA) Forum and SciDAC ISIC are given, followed by an illustrative example of how the CCA has been applied to drive scientific discovery in quantum chemistry. Thrusts for future research are also described briefly.
  • The Common Component Architecture (CCA) provides a means for software developers to manage the complexity of large-scale scientific simulations and to move toward a plug-and-play environment for high-performance computing. In the scientific computing context, component models also promote collaboration using independently developed software, thereby allowing particular individuals or groups to focus on the aspects of greatest interest to them. The CCA supports parallel and distributed computing as well as local high-performance connections between components in a language-independent manner. The design places minimal requirements on components and thus facilitates the integration of existing code into the CCA environment. The CCA modelmore » imposes minimal overhead to minimize the impact on application performance. The focus on high performance distinguishes the CCA from most other component models. The CCA is being applied within an increasing range of disciplines, including combustion research, global climate simulation, and computational chemistry.« less
  • The Common Component Architecture (CCA) provides a means for software developers to manage the complexity of large-scale scientific simulations and to move toward a plug-and-play environment for high-performance computing. In the scientific computing context, component models also promote collaboration using independently developed software, thereby allowing particular individuals or groups to focus on the aspects of greatest interest to them. The CCA supports parallel and distributed computing as well as local high-performance connections between components in a language-independent manner. The design places minimal requirements on components and thus facilitates the integration of existing code into the CCA environment. The CCA modelmore » imposes minimal overhead to minimize the impact on application performance. The focus on high performance distinguishes the CCA from most other component models. The CCA is being applied within an increasing range of disciplines, including combustion research, global climate simulation, and computational chemistry.« less
  • Until recently, performance gains in processors were achieved largely by improvements in clock speeds and instruction level parallelism. Thus, applications could obtain performance increases with relatively minor changes by upgrading to the latest generation of computing hardware. Currently, however, processor performance improvements are realized by using multicore technology and hardware support for multiple threads within each core, and taking full advantage of this technology to improve the performance of applications requires exposure of extreme levels of software parallelism. We will here discuss the architecture of parallel computers constructed from many multicore chips as well as techniques for managing the complexitymore » of programming such computers, including the hybrid message-passing/multi-threading programming model. We will illustrate these ideas with a hybrid distributed memory matrix multiply and a quantum chemistry algorithm for energy computation using Møller–Plesset perturbation theory.« less