Speculative alias analysis for executable code

Fernandez, M.; Espasa, Roger

doi:10.1109/pact.2002.1106020

Cited by 21 publications

(19 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Speculative optimizations [33][34][35][36] are considered by many program analyses. A probabilistic technique for memory disambiguation was proposed 34 .…”

Section: Probabilistic Pointer Analysis and Speculative Optimizationsmentioning

confidence: 99%

See 1 more Smart Citation

Untitled

El-Zawawy¹

2011

ScienceAsia

View full text Add to dashboard Cite

The use of pointers and data-structures based on pointers results in circular memory references that are interpreted by a vital compiler analysis, namely pointer analysis. For a pair of memory references at a program point, a typical pointer analysis specifies if the points-to relation between them may exist, definitely does not exist, or definitely exists. The "may be" case, which describes the points-to relation for most of the pairs, cannot be dealt with by most compiler optimizations. This is so to guarantee the soundness of these optimizations. However, the "may be" case can be capitalized by the modern class of speculative optimizations if the probability that two memory references alias can be measured. Focusing on multithreading, a prevailing technique of programming, this paper presents a new flow-sensitive technique for probabilistic pointer analysis of multithreaded programs. The proposed technique has the form of a type system and calculates the probability of every points-to relation at each program point. The key to our approach is to calculate the points-to information via a post-type derivation. The use of type systems has the advantage of associating each analysis results with a justification (proof) for the correctness of the results. This justification has the form of a type derivation and is very much required in applications like certified code.

show abstract

“…Speculative optimizations [33][34][35][36] are considered by many program analyses. A probabilistic technique for memory disambiguation was proposed 34 .…”

Section: Probabilistic Pointer Analysis and Speculative Optimizationsmentioning

confidence: 99%

“…Nevertheless this approach is not convenient to pointers. By lessening the safety of analysis, a pointer analysis that considers speculation was introduced 35 . Another unsafe analysis, which achieves scalability using transfer functions, was proposed 36 .…”

Section: Probabilistic Pointer Analysis and Speculative Optimizationsmentioning

confidence: 99%

Untitled

El-Zawawy¹

2011

ScienceAsia

View full text Add to dashboard Cite

show abstract

“…Furthermore, their interprocedural approach is based on Emami's algorithm [12] and is therefore not expected to scale to large programs. Fernandez and Espasa [13] proposed a pointer analysis algorithm that targets speculation by relaxing analysis safety. The key insight is that such unsafe analysis results are acceptable because the speculative optimization framework can tolerate them, converting a safety concern into a performance concern.…”

Section: Related Workmentioning

confidence: 99%

A probabilistic pointer analysis for speculative optimizations

Silva

Steffan

2006

SIGARCH Comput. Archit. News

View full text Add to dashboard Cite

Pointer analysis is a critical compiler analysis used to disambiguate the indirect memory references that result from the use of pointers and pointer-based data structures. A conventional pointer analysis deduces for every pair of pointers, at any program point, whether a points-to relation between them (i) definitely exists, (ii) definitely does not exist, or (iii) maybe exists. Many compiler optimizations rely on accurate pointer analysis, and to ensure correctness cannot optimize in the maybe case. In contrast, recently-proposed speculative optimizations can aggressively exploit the maybe case, especially if the likelihood that two pointers alias can be quantified. This paper proposes a Probabilistic Pointer Analysis (PPA) algorithm that statically predicts the probability of each points-to relation at every program point. Building on simple control-flow edge profiling, our analysis is both one-level context and flow sensitive-yet can still scale to large programs including the SPEC 2000 integer benchmark suite. The key to our approach is to compute points-to probabilities through the use of linear transfer functions that are efficiently encoded as sparse matrices. We demonstrate that our analysis can provide accurate probabilities, even without edge-profile information. We also find that-even without considering probability information-our analysis provides an accurate approach to performing pointer analysis.

show abstract

“…Fernandez [12] described some approaches that use speculative may alias information to optimize code. They gave some experimental data on the precision and the mis-speculation rates in their speculative analysis results.…”

Section: Related Workmentioning

confidence: 99%

A compiler framework for speculative analysis and optimizations

Lin¹,

Chen²,

Hsu³

et al. 2003

Proceedings of the ACM SIGPLAN 2003 Conference on Programming Language Design and Implementation - PLDI '03

View full text Add to dashboard Cite

Speculative execution, such as control speculation and data speculation, is an effective way to improve program performance. Using edge/path profile information or simple heuristic rules, existing compiler frameworks can adequately incorporate and exploit control speculation. However, very little has been done so far to allow existing compiler frameworks to incorporate and exploit data speculation effectively in various program transformations beyond instruction scheduling. This paper proposes a speculative SSA form to incorporate information from alias profiling and/or heuristic rules for data speculation, thus allowing existing program analysis frameworks to be easily extended to support both control and data speculation. Such a general framework is very useful for EPIC architectures that provide checking (such as advanced load address table (ALAT) [10]) on data speculation to guarantee the correctness of program execution. We use SSAPRE [21] as one example to illustrate how to incorporate data speculation in those important compiler optimizations such as partial redundancy elimination (PRE), register promotion, strength reduction and linear function test replacement. Our extended framework allows both control and data speculation to be performed on top of SSAPRE and, thus, enables more aggressive speculative optimizations. The proposed framework has been implemented on Intel's Open Research Compiler (ORC). We present experimental data on some SPEC2000 benchmark programs to demonstrate the usefulness of this framework and how data speculation benefits partial redundancy elimination.

show abstract

Speculative alias analysis for executable code

Cited by 21 publications

References 20 publications

Untitled

Untitled

A probabilistic pointer analysis for speculative optimizations

A compiler framework for speculative analysis and optimizations

Contact Info

Product

Resources

About