Automatic Source-to-Source Error Compensation of Floating-Point Programs

International audienceNumerical programs with IEEE 754 floating-point computations may suffer from inaccuracies since finite precision arithmetic is an approximation of real arithmetic. Solutions that reduce the loss of accuracy are available as, for instance, compensated algorithms, more precise computation with double-double or similar libraries. Our objective is to automatically improve the numerical quality of a numerical program with the smallest impact on its performances. We define and implement source code transformation to derive automatically compensated programs. We present several experimental results to compare the transformed programs and existing solutions. The transformed programs are as accurate and efficient than the implementations of compensated algorithms when the latter exist

Feature-Based Comparison of Language Transformation Tools

Code transformation is the best option while switching from farmer to next technology. Our paper presents a comparative analysis of code transformation tools based on 18 different factors. These factors are Classes, pointers, Access Specifiers, Functions and Exceptions, etc. For this purpose, we have selected varyCode, Telerik, Multi-online converter, and InstantVB. Source Language considered for this purpose is C sharp (C#) and the target language is Visual Basics (VB). Results show that VaryCode is best among the four tools as its converted programs throw fewer errors and require minor changes while running the program

Automatic source-to-source error compensation of floating-point programs: code synthesis to optimize accuracy and time

International audienceNumerical programs with IEEE 754 floating-point computations may suffer from inaccuracies, since finite precision arithmetic is an approximation of real arithmetic. Solutions that reduce the loss of accuracy are available, such as, compensated algorithms or double-double precision floating-point arithmetic. Our goal is to automatically improve the numerical quality of a numerical program with the smallest impact on its performance. We define and implement source code transformations in order to derive automatically compensated programs. We present several experimental results to compare the transformed programs and existing solutions. The transformed programs are as accurate and efficient as the implementations of compensated algorithms when the latter exist. Furthermore, we propose some transformation strategies allowing us to improve partially the accuracy of programs and to tune the impact on execution time. Trade-offs between accuracy and performance are assured by code synthesis. Experimental results show that user-defined trade-offs are achievable in a reasonable amount of time, with the help of the tools we present in the paper