Runtime, Speculative On-Stack Parallelization of For-Loops in Binary Programs

Yusuf, Marwa; El-Mahdy, Ahmed; Rohou, Erven

doi:10.1109/locs.2018.2872454

Cited by 2 publications

(2 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We propose to apply the SP technique of Yusuf et. al [32]. This SP technique exploits the on-stack replacement which deals with the dependence according to our approach.…”

Section: E Dependence Flags Map and Recommendations (7)mentioning

confidence: 99%

See 1 more Smart Citation

IR-Level Dynamic Data Dependence Using Abstract Interpretation Towards Speculative Parallelization

2020

Self Cite

View full text Add to dashboard Cite

Recently, with the wide usage of multicore architectures, automatic parallelization has become a pressing issue. Speculative parallelization, one of the most popular automatic parallelization techniques, depends on estimating probably-parallelized code parts. This in turn motivates the employment of data dependence detection techniques for these code parts to report whether they contain dependence or not in order to be parallelized. In this paper, we propose a runtime data-dependence detection technique that is based on abstract interpretation at the intermediate representation (IR) level. We apply our proposed approach on the most frequently visited blocks of the code, hot loops. Unlike most existing approaches in which data analysis occurs at compile time, our proposed method conducts the analysis immediately while interpreting the code, which in turn saves the analysis time for potentially parallelized loops. Specifically, the proposed technique depends on the concept of abstract interpretation to analyze the hot loops at runtime. This process is done by firstly computing the abstract domain for each hot loop program points. Each abstract domain is incrementally computed, till a fixpoint is achieved for all program points, and correspondingly the analysis terminates in order to consecutively detect the existence of data dependence. Once the analysis result reports a parallelization possibility for the finished hot loop, the interpreter invokes the compiler to resume the execution in a parallel fashion as recommended by our proposed approach. The proposed technique is implemented on LLVM compiler, then used to test the dependence detection for a set of kernels on the Polybench framework, and the data dependence analysis required for each kernel is studied in terms of the computation overhead.

show abstract

“…We propose to apply the SP technique of Yusuf et. al [32]. This SP technique exploits the on-stack replacement which deals with the dependence according to our approach.…”

Section: E Dependence Flags Map and Recommendations (7)mentioning

confidence: 99%

“…The SP technique of Yusuf et. al [32] would be suggested to be applied at the highest level of optimization. This optimization jump would be useful for executing the program in parallel whenever our method generate its recommendations for current HL.…”

Section: E Dependence Flags Map and Recommendations (7)mentioning

confidence: 99%

IR-Level Dynamic Data Dependence Using Abstract Interpretation Towards Speculative Parallelization

2020

Self Cite

View full text Add to dashboard Cite

show abstract

Binary-level data dependence analysis of hot execution regions using abstract interpretation at runtime

et al. 2020

Self Cite

View full text Add to dashboard Cite

With the widespread of multicore systems, automatic parallelization becomes more pronounced, particularly for legacy programs, where the source code is not generally available. An essential operation in any parallelization system is detecting data dependence among parallelization candidate instructions. Conducting dependence analysis at the binary-level is more challenging than that at the source-level due to the much lower semantics of the binary code. In this paper, we consider using the elaborate 'static' analysis of abstract interpretation, for the first time, at runtime for data dependence detection. Specifically, our system interprets instructions at a hot region, while at the same time, collect programs semantics for seen program points, thereby conducting abstract interpretation analysis dynamically. The analysis is guaranteed to be correct as long as execution does not exit the region prematurely. Moreover, successive hot region re-entries will resume previous analysis, albeit much faster in case no major change in the program semantics. Such approach provides for more rigorous analysis than other simple dynamic analysis which would typically miss parallelization opportunities. The proposed approach also does not require any hardware support, availability of the source code, as well as any code re-compilation. To study the performance and accuracy of our approach, we have extended the Padrone dynamic code modification framework, and conduct an initial study on a set of PolyBench kernels and selected programs from SPEC CPU. Experimental results show accurate dependence detection with low overhead.

show abstract

Runtime, Speculative On-Stack Parallelization of For-Loops in Binary Programs

Cited by 2 publications

References 7 publications

IR-Level Dynamic Data Dependence Using Abstract Interpretation Towards Speculative Parallelization

IR-Level Dynamic Data Dependence Using Abstract Interpretation Towards Speculative Parallelization

Binary-level data dependence analysis of hot execution regions using abstract interpretation at runtime

Contact Info

Product

Resources

About