Online Dynamic Dependence Analysis for Speculative Polyhedral Parallelization

Jimborean, Alexandra; Clauss, Philippe; Martinez, Juan Manuel; Sukumaran-Rajam, Aravind

doi:10.1007/978-3-642-40047-6_21

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…In [25] another data dependence profiling technique is developed, which reduces slowdowns to around 150×, but does not overlay dependence to a dynamic call graph, but only merges dependences in a static call graph. An online dynamic dependence analysis is shown in [23].…”

Section: Related Workmentioning

confidence: 99%

Generalized profile-guided iterator recognition

Manilov

Vasiladiotis

Franke

2018

Proceedings of the 27th International Conference on Compiler Construction

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Iterators prescribe the traversal of data structures and determine loop termination, and many loop analyses and transformations require their exact identification. While recognition of iterators is a straight-forward task for affine loops, the situation is different for loops iterating over dynamic data structures or involving control flow dependent computations to determine the next data element. In this paper we propose a compiler analysis for recognizing loop iterators code for a wide class of loops. We initially develop a static analysis, which is then enhanced with profiling information to support speculative code optimizations. We have prototyped our analysis in the LLVM framework and demonstrate its capabilities using the SPEC CPU2006 benchmarks. Our approach is applicable to all loops and we show that we can recognize iterators in, on average, 88.1% of over 75,000 loops using static analysis alone, and up to 94.9% using additional profiling information. Existing techniques perform substantially worse, especially for C and C++ applications, and cover only 35-44% of the loops. Our analysis enables advanced loop optimizations such as decoupled software pipelining, commutativity analysis and source code rejuvenation for real-world applications, which escape analysis and transformation if loop iterators are not recognized accurately.

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Section: Related Workmentioning

confidence: 99%

Generalized profile-guided iterator recognition

Manilov

Vasiladiotis

Franke

2018

Proceedings of the 27th International Conference on Compiler Construction

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“…Holewinski et al [10] use dynamic data dependence graphs derived from sequential execution traces to identify vectorization opportunities. Jimborean et al [13] propose a dynamic mechanism for detecting data dependences using interpolated linear functions to approximate observed memory accesses to guide speculative parallelization. Mendis et al [20] employ the binary and data collected from several executions of an application to rewrite computational kernels into a DSL by building a forest of expressions and translating it into an optimized version.…”

Section: Related Work and Applicationsmentioning

confidence: 99%

Trace-based affine reconstruction of codes

Rodriguez

Andión

Kandemir

et al. 2016

Proceedings of the 2016 International Symposium on Code Generation and Optimization

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Complete comprehension of loop codes is desirable for a variety of program optimizations. Compilers perform static code analyses and transformations, such as loop tiling or memory partitioning, by constructing and manipulating formal representations of the source code. Runtime systems observe and characterize application behavior to drive resource management and allocation, including dependence detection and parallelization, or scheduling. However, the source codes of target applications are not always available to the compiler or runtime system in an analyzable form. It becomes necessary to find alternate ways to model application behavior. This paper presents a novel mathematical framework to rebuild loops from their memory access traces. An exploration engine traverses a tree-like solution space, driven by the access strides in the trace. It is guaranteed that the engine will find the minimal affine nest capable of reproducing the observed sequence of accesses by exploring this space in a brute force fashion, but most real traces will not be tractable in this way. Methods for an efficient solution space traversal based on mathematical properties of the equation systems which model the solution space are proposed. The experimental evaluation shows that these strategies achieve efficient loop reconstruction, processing hundreds of gigabytes of trace data in minutes. The proposed approach is capable of correctly and minimally reconstructing 100% of the static control parts in PolyBench/C applications. As a side effect, the trace reconstruction process can be used to efficiently compress trace files. The proposed tool can also be used for dynamic access characterization, predicting over 99% of future memory accesses.

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“…Jimborean et al [72] presented an analyzer, which can dynamic test dependences by trial executing samples. The obtained information is used to determine the speculative optimization and parallelization.…”

confidence: 99%

A Survey of Loop Parallelization: Models, Approaches, and Recent Developments

Ye¹,

Deng²,

Zou³

2016

IJGDC

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In cloud computing era, automatic parallelization is still significant for virtualization platform. However, after several decades of development, the overall effect is still to be improved. Summary of the mainstream technology developments will be beneficial to reveal the future direction and trend. This paper reviews the technology of loop parallelization, which is the key issue in automatic parallelization. After introducing the basic models and approaches, we focus on the recent developments, on which we obtain the trend of this field and the conclusions about future.

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Online Dynamic Dependence Analysis for Speculative Polyhedral Parallelization

Cited by 5 publications

References 15 publications

Generalized profile-guided iterator recognition

Generalized profile-guided iterator recognition

Trace-based affine reconstruction of codes

A Survey of Loop Parallelization: Models, Approaches, and Recent Developments

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