A compiler framework for speculative analysis and optimizations

Lin, Jin; Chen, Tong; Hsu, Wei-Chung; Yew, Pen-Chung; Ju, Roy; Ngai, Tin-Fook; Chan, Sin-Lam

doi:10.1145/781131.781164

Cited by 48 publications

(8 citation statements)

References 25 publications

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“…Speculative tuning uses control [58], [59], and data speculation to help program analysis and compiler optimizations such as instruction scheduling, partial redundancy elimination (PRE), register promotion, strength reduction, and alias analysis. [60]- [65].…”

Section: ) Speculative Tuningmentioning

confidence: 99%

A Survey of Performance Tuning Techniques and Tools for Parallel Applications

Mustafa

2022

IEEE Access

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Automatic parallelization of sequential programs combined with autotuning is an alternative to manual parallelization. With wider research directions and the increased number of performance tuning tools that have been developed, it becomes harder to choose a particular tuning tool to use. This paper reviews the fundamentals of different performance optimization and tuning techniques. It also surveys many tuning frameworks and classifies them into different groups based on their criteria. Developing benchmarks for HPC and validating their accuracy have been demanding tasks for computer architects, researchers, and application developers. In addition to providing a survey of performance tuning tools, we also performed a detailed review of current benchmarks and discussed the requirements for future benchmarks. We performed a detailed comparison of these tuning tools based on other features such as speedup and infrastructure details. We believe that this paper will be a very useful resource for the parallel computing community, especially for early-stage parallel computing and performance researchers to gain exposure to the existing performance optimization options.

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Section: ) Speculative Tuningmentioning

confidence: 99%

A Survey of Performance Tuning Techniques and Tools for Parallel Applications

Mustafa

2022

IEEE Access

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“…The framework presented here aims in providing a formal basis for speculative optimisation. Speculative optimisation [15,2] has been an element of hardware design for some time, in particular to branch prediction [16] or for cache optimisation [17]. More recently, related ideas have also been discussed in the context of speculative multi-threading [4] or probabilistic pointer analysis [9,13].…”

Section: Conclusion and Related Workmentioning

confidence: 99%

Probabilistic data flow analysis: a linear equational approach

Pierro

Wiklicky

2013

Electron. Proc. Theor. Comput. Sci.

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“…If the compiler cannot prove either case to be true (which is often the result in practice because of the difficulties associated with pointer analysis) then it must be conservative and refrain from optimization. In this particular example, neither optimization is possible; however, it is possible to perform either optimization speculatively so long as the optimized code is guarded with a check and recovery mechanism [22]. To decide whether a speculative optimization is desirable for the code involving the points-to relations at S7 and S8, we first require the corresponding probabilities.…”

Section: Example Programmentioning

confidence: 99%

“…For example, EPIC instruction sets (eg., Intel's IA64) provide hardware support that allows the compiler to schedule a load ahead of a potentially-dependent store, and to specify recovery code that is executed in the event that the execution is unsafe [11,18]. Proposed speculative optimizations that allow the compiler to exploit this new hardware support include speculative dead store elimination, speculative redundancy elimination, speculative copy propagation, and speculative code scheduling [7,21,22].…”

Section: Introductionmentioning

confidence: 99%

A probabilistic pointer analysis for speculative optimizations

Silva

Steffan

2006

Proceedings of the 12th International Conference on Architectural Support for Programming Languages and Operating Systems

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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.

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A compiler framework for speculative analysis and optimizations

Cited by 48 publications

References 25 publications

A Survey of Performance Tuning Techniques and Tools for Parallel Applications

A Survey of Performance Tuning Techniques and Tools for Parallel Applications

Probabilistic data flow analysis: a linear equational approach

A probabilistic pointer analysis for speculative optimizations

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