Advanced Symbolic Analysis for Compilers: New Techniques and by Thomas Fahringer

By Thomas Fahringer

The target of application research is to immediately verify the p- perties of a application. instruments of software program improvement, corresponding to compilers, p- formance estimators, debuggers, reverse-engineering instruments, application veri?- tion/testing/proving platforms, application comprehension platforms, and application specializationtoolsarelargelydependentonprogramanalysis. Advancedp- gram research can: support to ?nd software blunders; become aware of and music performan- severe code areas; make certain assumed constraints on facts should not violated; tailor a regular application to fit a speci?c software; reverse-engineer so- ware modules, and so on. A well known application research method is symbolic a- lysis, which has attracted gigantic consciousness for a few years because it isn't depending on executing a software to ascertain the semantics of a software, and it might probably yield very based formulations of many analyses. in addition, the complexity of symbolic research will be principally self sustaining of the enter information measurement of a application and of the scale of the computing device on which this system is being carried out. during this publication we current novel symbolic keep an eye on and knowledge ?ow repres- tation options in addition to symbolic ideas and algorithms to investigate and optimize courses. application contexts which de?ne a brand new symbolic - scription of software semantics for regulate and information ?ow research are on the middle of our process. we've got solved a couple of difficulties encountered in application research through the use of application contexts. Our answer equipment are e?cient, flexible, uni?ed, and extra common (they deal with general and abnormal codes) than such a lot present methods.

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Extra resources for Advanced Symbolic Analysis for Compilers: New Techniques and Algorithms for Symbolic Program Analysis and Optimization

Example text

To collect the loop information as part of the program context we introduce function µ(v, s, c) which comprises a symbolic recurrence system. The recurrence relations of the recurrence variables are given in program context c as part of the state and state condition. The recurrence condition is represented by the path condition of c. The boundary condition of the reccurrence system is determined by state s of function µ, and variable v determines which recurrence variable is selected for the result of the µ function.

2. Exit and entry contexts for a generic node n with k (1 ≤ i ≤ k) successor nodes. 3 Algorithm The algorithm computes program contexts for all nodes in the CFG. Every node n ∈ N has a unique entry context cb (n) = [sb , tb , pb ] that describes the variable bindings and state and path conditions immediately before node n. Note that every node n has only a single entry context although node n may have multiple predecessors1 . 2. Furthermore, every CFG node n has a unique exit context ca (n, n′ ) for each of its successor nodes n′ ∈ succs(n).

Counting the number of loop iterations has been shown [44, 40, 67, 100] to be crucial for many performance analyses such as modeling work distribution [43], data locality [44] and communication overhead [42]. All of these problems can be formulated as queries based on a set of linear and non-linear constraints I. The constraints of I are defined over loop variables and parameters (loop invariants) which are commonly derived from loop bounds, conditional statements, data declarations, data and work distribution specifications [74, 11], etc.

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