Integrating Task Migration Capability in Software Tool-Chain for Data-Flow Applications Mapped on Multi-tiled Architectures

2015 ◽  
Author(s):  
Ashraf El-Antably ◽  
Nicolas Fournel ◽  
Frederic Rousseau
Keyword(s):  
Data Flow ◽  
Software Tool ◽  
Task Migration ◽  
Tool Chain

ON THE COMPILATION OF A LANGUAGE FOR GENERAL CONCURRENT TARGET ARCHITECTURES

2010 ◽  
Vol 20 (01) ◽  
pp. 51-69 ◽  
Author(s):  
THOMAS A.M. BERNARD ◽  
CLEMENS GRELCK ◽  
CHRIS R. JESSHOPE
Keyword(s):  
General Model ◽  
Software Tool ◽  
System Level ◽  
Tool Chain ◽  
Parallel Language ◽  
Resource Mapping ◽  
Transformation Rules ◽  
Many Core

The challenge of programming many-core architectures efficiently and effectively requires models and methods to co-design chip architectures and their software tool chain, using an approach that is both vertical and general. In this paper, we present compilation schemes for a general model of concurrency captured in a parallel language designed for system-level programming and as a target for higher level compilers. We also expose the challenges of integrating these transformation rules into a sequential-oriented compiler. Moreover, we discuss resource mapping inherent to those challenges. Our aim has been to reuse as much of the existing sequential compiler technology as possible in order to harness decades of prior research in compiling sequential languages.

Generation and Use of an ASIP Software Tool Chain

2009 ◽  
pp. 173-202 ◽  
Author(s):  
Sterling Augustine ◽  
Marc Gauthier ◽  
Steve Leibson ◽  
Peter Macliesh ◽  
Grant Martin ◽  
...  
Keyword(s):  
Software Tool ◽  
Tool Chain

scholarly journals The future of software tool chain safety qualification

2015 ◽  
Vol 74 ◽  
pp. 37-43 ◽  
Author(s):  
Fredrik Asplund
Keyword(s):  
Software Tool ◽  
Tool Chain ◽  
The Future

scholarly journals A tool to increase information-processing capacity for consumer water meter data

2012 ◽  
Vol 14 (1) ◽  
Author(s):  
Heinz E. Jacobs ◽  
Kerry A. Fair
Keyword(s):  
Information Processing ◽  
Data Flow ◽  
Service Providers ◽  
Software Tool ◽  
Detailed Knowledge ◽  
Processing Capacity ◽  
Water Usage ◽  
Information Processing Capacity ◽  
Technological Advances ◽  
Water Meter

Background: Water service providers invoice most South African urban consumers for the water they use every month. A secure treasury system generates water invoices at municipalities’ financial departments. Information about the water usage of customers initially comes from reading the water meters, usually located in gardens near the front boundaries of properties. Until as recently as 1990, the main purpose of the water meter readings was to generate invoices for water usage. There are various treasury systems for this purpose.Objective: The objective of this research article was to describe the development of Swift, a locally developed software tool for analysing water meter data from an information management perspective, which engineers in the water field generally use, and to assess critically the influence of Swift on published research and industry. This article focuses on water usage and the challenge of data interchange and extraction as issues that various industries face.Method: This article presents the first detailed report on Swift. It uses a detailed knowledge review and presents and summarises the findings chronologically.Results: The water meter data flow path used to be quite simple. The risk of breaches in confidentiality was limited. Technological advances over the years have led to additional knowledge coming from the same water meter readings with subsequent research outputs. However, there are also complicated data flow paths and increased risks. Users have used Swift to analyse more than two million consumers’ water meter readings to date. Studies have culminated in 10 peer-reviewed journal articles using the data. Seven of them were in the last five years.Conclusion: Swift-based data was the basis of various research studies in the past decade. Practical guidelines in the civil engineering fraternity for estimating water use in South Africa have incorporated knowledge from these studies. Developments after 1995 have increased the information processing capacity for water meter data.

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