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| Content Provider | ACM Digital Library |
|---|---|
| Author | Chandra, Satish Rountev, Atanas |
| Abstract | Most compiler optimizations and software productivity tools rely oninformation about the effects of pointer dereferences in a program.The purpose of points-to analysis is to compute this informationsafely, and as accurately as is practical. Unfortunately, accuratepoints-to information is difficult to obtain for large programs,because the time and space requirements of the analysis becomeprohibitive.We consider the problem of scaling flow- and context-insensitivepoints-to analysis to large programs, perhaps containing hundreds ofthousands of lines of code. Our approach is based on a variable substitution transformation, which is performed off-line, i.e.,before a standard points-to analysis is performed. The general idea ofvariable substitution is that a set of variables in a program can be replaced by a single representative variable, thereby reducing the input size of the problem. Our main contribution is a linear-time algorithm which finds a particular variable substitution that maintains the precision of the standard analysis, and is also very effective in reducing the size of the problem.We report our experience in performing points-to analysis on largeC programs, including some industrial-sized ones. Experiments show thatour algorithm can reduce the cost of Andersen's points-to analysis substantially: on average, it reduced the running time by 53% and the memory cost by 59%, relative to an efficient baseline implementation of the analysis. |
| Starting Page | 47 |
| Ending Page | 56 |
| Page Count | 10 |
| File Format | |
| ISBN | 1581131992 |
| DOI | 10.1145/349299.349310 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2000-08-01 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Content Type | Text |
| Resource Type | Article |
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