Improvements to the Psi-SSA representation

Ferrière, François de

doi:10.1145/1269843.1269859

Cited by 4 publications

(3 citation statements)

References 16 publications

(24 reference statements)

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“…Nevertheless, there exists several extensions to deal with code with predication (e.g. ψ-SSA form [14]) and aliasing (e.g. Hashed SSA [10] or Array SSA [16]).…”

Section: Resultsmentioning

confidence: 99%

Parameterized Construction of Program Representations for Sparse Dataflow Analyses

Tavares¹,

Boissinot²,

Pereira³

et al. 2014

Lecture Notes in Computer Science

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Abstract. Data-flow analyses usually associate information with control flow regions. Informally, if these regions are too small, like a point between two consecutive statements, we call the analysis dense. On the other hand, if these regions include many such points, then we call it sparse. This paper presents a systematic method to build program representations that support sparse analyses. To pave the way to this framework we clarify the bibliography about well-known intermediate program representations. We show that our approach, up to parameter choice, subsumes many of these representations, such as the SSA, SSI and e-SSA forms. In particular, our algorithms are faster, simpler and more frugal than the previous techniques used to construct SSI -Static Single Information -form programs. We produce intermediate representations isomorphic to Choi et al.'s Sparse Evaluation Graphs (SEG) for the family of data-flow problems that can be partitioned per variables. However, contrary to SEGs, we can handle -sparsely -problems that are not in this family. We have tested our ideas in the LLVM compiler, comparing different program representations in terms of size and construction time.

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“…Nevertheless, there exists several extensions to deal with code with predication (e.g. ψ-SSA form [14]) and aliasing (e.g. Hashed SSA [10] or Array SSA [16]).…”

Section: Resultsmentioning

confidence: 99%

Parameterized Construction of Program Representations for Sparse Dataflow Analyses

Tavares¹,

Boissinot²,

Pereira³

et al. 2014

Lecture Notes in Computer Science

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“…Ferrière [25] extends the ψ-SSA form algorithms of [49] to architectures with partial predicated execution support, by formulating simple correctness conditions for the predicate promotion of operations that do not have side-effects. This work also details how to transform the ψ-SSA form to conventional ψ-SSA form by generating CMOV operations.…”

Section: Stoutchinin and Gaomentioning

confidence: 99%

“…The largest impact of handling ψ-function is apparent in the ψ-SSA form destruction [25], whose original description [49] was incomplete.…”

Section: Stoutchinin and Gaomentioning

confidence: 99%

Using the SSA-Form in a Code Generator

Dinechin

2014

Lecture Notes in Computer Science

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Abstract. In high-end compilers such as Open64, GCC or LLVM, the Static Single Assignment (SSA) form is a structural part of the targetindependent program representation that supports most of the code optimizations. However, aggressive compilation also requires that optimizations that are more effective with the SSA form be applied to the target-specific program representations operated by the code generator, that is, the set of compiler phases after and including instruction selection.While using the SSA form in the code generator has definite advantages, the SSA form does not apply to all the code generator program representations, and is not suited for all optimizations. We discuss some of the issues of inserting the SSA form in a code generator, specifically: what are the challenges of maintaining the SSA form on a program representation based on machine instructions; how the SSA form may be used in the if-conversion optimizations; why the SSA form does not seem to benefit instruction scheduling; and what is the state-of-the-art in SSA form destruction on machine code.

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