SHF: Small: Reliable Data Processing by Dynamic Program Analysis

SHF：小型：通过动态程序分析进行可靠的数据处理

• 批准号: 1320444
• 负责人: Xiangyu Zhang
• 金额: $ 40万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-07-01 至 2018-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1320444&HistoricalAwards=false
• 关键词: SHF; Small; Reliable; Data; Processing

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.

计算科学涉及自然现象的计算机建模和模拟，科学探究的有效性取决于计算机用于数值计算的方式。在现代科学数据处理中，数值误差严重威胁到输出的有效性。原始输入是由精度有限的物理仪器获取的，这会导致输入错误。数据处理中使用的参数可能由人类科学家根据他们的经验提供，导致不确定性。由于所用机器的精度有限，数据可能无法准确表示。一旦这些错误蔓延到计算中，它们可能会被执行的操作序列传播和放大，从而产生不可靠的输出。这种不稳定问题最终可能对科学研究甚至经济产生重大影响。该项目旨在开发动态程序分析工具，以解决由错误引起的不稳定问题。这些工具将自动分析用户提供的数据处理程序，并将其转换为允许对错误进行在线表示和推理。用户像往常一样在原始输入数据上运行转换后的程序，并可选择提供额外的输入/系数误差范围。执行将像以前一样产生常规输出，并显示在存在错误时输出是否稳定，包括输入错误、不确定系数和内部表示错误。如果确定执行是不稳定的，该技术将自动报告由错误引起的可能后果。另一种选择是自动切换到执行程序的高精度版本，这也是由项目的工具集生成的。