| NSF Org: |
CCF Division of Computing and Communication Foundations |
| Recipient: |
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| Initial Amendment Date: | June 28, 2013 |
| Latest Amendment Date: | June 28, 2013 |
| Award Number: | 1320444 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Sol Greenspan
sgreensp@nsf.gov (703)292-7841 CCF Division of Computing and Communication Foundations CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | July 1, 2013 |
| End Date: | June 30, 2018 (Estimated) |
| Total Intended Award Amount: | $400,000.00 |
| Total Awarded Amount to Date: | $400,000.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
2550 NORTHWESTERN AVE # 1100 WEST LAFAYETTE IN US 47906-1332 (765)494-1055 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
IN US 47907-2107 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | SOFTWARE ENG & FORMAL METHODS |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
Computational Science involves computer modeling and simulation of natural phenomena, and the validity of scientific inquiry depends on the way computers are used to do numerical computation. Numeric errors pose a serious threat to output validity for modern scientific data processing. Raw inputs are acquired by physical instruments that have limited precision, leading to input errors. Parameters used in data processing may be provided by human scientists based on their experience, leading to uncertainty. Data may not be represented exactly due to the limited precision of the machine used. Once these errors creep into a computation, they may get propagated and magnified by the sequence of operations conducted, producing unreliable output. Such instability problems may ultimately have substantial impact on scientific research and even the economy.
This project aims to develop dynamic program analysis tools to address instability problems caused by errors. These tools will automatically analyze the data processing programs provided by the users and transform them to allow online representation of and reasoning about errors. The user runs the transformed programs on the original input data as usual, with the option of providing additional input/coefficient error ranges. The execution will produce regular output as before, together with an indication of whether the output is stable in the presence of errors, including input errors, uncertain coefficients, and internal representation errors. If the execution is determined to be unstable, the technique will automatically report the possible consequences induced by the errors. Another option is to automatically switch to executing a high-precision version of the program, which is also generated by the project's tool set.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
Errors pose a serious threat to output validity for modern scientific
data processing, which is often performed by computer programs.
Raw inputs are acquired by physical instruments that have precision
limitations, leading to input errors. Parameters used in data processing
may be provided by human scientists based on their experience, leading
to uncertainty. Data may not be precisely represented due to the limited
precision of the machine used, leading to representation errors.
Once these errors get into computation, they may get propagated and magnified
by the operations conducted, producing unreliable output.
In this project, the PI developed techniques to address the possible problems caused by floating point errors during execution. These tools are based on compilers. They automatically analyze data processing programs and transform them to allow representing and reasoning about errors at runtime.
The user runs the transformed programs on the original input data as usual,
with the option of providing the additional input/coefficient error bounds
explicitly. The execution will produce regular output as before, together
with indication if the output is stable in the presence different kinds of
errors, including input errors, uncertain coefficients, and internal
representation errors. If the execution is determined as unstable,
The techniques automatically report the possible different consequences
induced by the errors, to facilitate better human decisions.
Through this project, the PI graduated graduated two PhD students. One joined industry and the other joined academia as an assistant professor. Research outcomes were published on top venues such as OOPSLA, ICSE, FSE and TSE.
Last Modified: 10/14/2018
Modified by: Xiangyu Zhang
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