Data Dependence Profiling for Speculative Optimizations

Chen, Tong; Lin, Jin; Dai, Xiaoru; Hsu, Wei-Chung; Yew, Pen-Chung

doi:10.1007/978-3-540-24723-4_5

Cited by 41 publications

(38 citation statements)

References 25 publications

(21 reference statements)

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“…In some of the recent work [6], [8], context sensitive profiling [2] is used to collect dependence information for parallelization. However, context sensitivity is not sufficient in general.…”

Section: Inadequacy Of Context Sensitivitymentioning

confidence: 99%

“…In all the four cases, the calling context is the same. In case four, even using a loop iteration vector [6] does not help.…”

Section: Inadequacy Of Context Sensitivitymentioning

confidence: 99%

“…It would be interesting to see if indexing can be integrated with ParaMeter to present a hierarchical view of traces. Recently, context sensitive dependence profiling is used for speculative optimization and parallelization in [6], [8]. However context sensitivity is not adequate to model loop carry dependences.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Alchemist: A Transparent Dependence Distance Profiling Infrastructure

Zhang

Navabi

Jagannathan

2009

2009 International Symposium on Code Generation and Optimization

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Abstract-Effectively migrating sequential applications to take advantage of parallelism available on multicore platforms is a well-recognized challenge. This paper addresses important aspects of this issue by proposing a novel profiling technique to automatically detect available concurrency in C programs. The profiler, called Alchemist, operates completely transparently to applications, and identifies constructs at various levels of granularity (e.g., loops, procedures, and conditional statements) as candidates for asynchronous execution. Various dependences including read-after-write (RAW), write-after-read (WAR), and write-after-write (WAW), are detected between a construct and its continuation, the execution following the completion of the construct. The time-ordered distance between program points forming a dependence gives a measure of the effectiveness of parallelizing that construct, as well as identifying the transformations necessary to facilitate such parallelization. Using the notion of post-dominance, our profiling algorithm builds an execution index tree at run-time. This tree is used to differentiate among multiple instances of the same static construct, and leads to improved accuracy in the computed profile, useful to better identify constructs that are amenable to parallelization. Performance results indicate that the profiles generated by Alchemist pinpoint strong candidates for parallelization, and can help significantly ease the burden of application migration to multicore environments.

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Section: Inadequacy Of Context Sensitivitymentioning

confidence: 99%

“…In all the four cases, the calling context is the same. In case four, even using a loop iteration vector [6] does not help.…”

Section: Inadequacy Of Context Sensitivitymentioning

confidence: 99%

See 1 more Smart Citation

Alchemist: A Transparent Dependence Distance Profiling Infrastructure

Zhang

Navabi

Jagannathan

2009

2009 International Symposium on Code Generation and Optimization

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“…As it turns out, the probability distribution of most data dependences are very bimodal, i.e. it is either very likely, or not likely at all [20]. Using this approach, more optimization opportunities can be exposed for possible speculation.…”

Section: Speculative Data Dependence Analysismentioning

confidence: 99%

“…Otherwise, they are considered independent. Data dependence profile [20] could also be used to provide such information.…”

Section: Speculative Data Dependence Analysismentioning

confidence: 99%

A General Compiler Framework for Speculative Optimizations Using Data Speculative Code Motion

Dai

Zhai

Hsu

et al.

International Symposium on Code Generation and Optimization

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Data speculative optimization refers to code transformations that allow load and store instructions to be moved across potentially dependent memory operations. Existing research work on data speculative optimizations has mainly focused on individual code transformation. The required speculative analysis that identifies data speculative optimization opportunities and the required recovery code generation that guarantees the correctness of their execution are handled separately for each optimization. This paper proposes a new compiler framework to facilitate the design and implementation of general data speculative optimizations such as dead store elimination, redundancy elimination, copy propagation, and code scheduling. This framework allows different data speculative optimizations to share the followings: (i) a speculative analysis mechanism to identify data speculative optimization opportunities by ignoring low probability data dependences from optimizations, and (ii) a recovery code generation mechanism to guarantee the correctness of the data speculative optimizations. The proposed recovery code generation is based on Data Speculative Code Motion (DSCM) that uses code motion to facilitate a desired transformation. Based on the position of the moved instruction, recovery code can be generated accordingly. The proposed framework greatly simplifies the task of incorporating data speculation into non-speculative optimizations by sharing the recovery code generation and the speculative analysis. We have implemented the proposed framework in the ORC 2.1 compiler and demonstrated its effectiveness on SPEC2000 benchmark programs.

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Optimal and Perfectly Parallel Algorithms for On-demand Data-Flow Analysis

Chatterjee

Goharshady

Ibsen-Jensen

et al. 2020

Lecture Notes in Computer Science

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Interprocedural data-flow analyses form an expressive and useful paradigm of numerous static analysis applications, such as live variables analysis, alias analysis and null pointers analysis. The most widely-used framework for interprocedural data-flow analysis is IFDS, which encompasses distributive data-flow functions over a finite domain. On-demand data-flow analyses restrict the focus of the analysis on specific program locations and data facts. This setting provides a natural split between (i) an offline (or preprocessing) phase, where the program is partially analyzed and analysis summaries are created, and (ii) an online (or query) phase, where analysis queries arrive on demand and the summaries are used to speed up answering queries. In this work, we consider on-demand IFDS analyses where the queries concern program locations of the same procedure (aka same-context queries). We exploit the fact that flow graphs of programs have low treewidth to develop faster algorithms that are space and time optimal for many common data-flow analyses, in both the preprocessing and the query phase. We also use treewidth to develop query solutions that are embarrassingly parallelizable, i.e. the total work for answering each query is split to a number of threads such that each thread performs only a constant amount of work. Finally, we implement a static analyzer based on our algorithms, and perform a series of on-demand analysis experiments on standard benchmarks. Our experimental results show a drastic speed-up of the queries after only a lightweight preprocessing phase, which significantly outperforms existing techniques.

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Data Dependence Profiling for Speculative Optimizations

Cited by 41 publications

References 25 publications

Alchemist: A Transparent Dependence Distance Profiling Infrastructure

Alchemist: A Transparent Dependence Distance Profiling Infrastructure

A General Compiler Framework for Speculative Optimizations Using Data Speculative Code Motion

Optimal and Perfectly Parallel Algorithms for On-demand Data-Flow Analysis

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