Semi-automatic extraction and exploitation of hierarchical pipeline parallelism using profiling information

Tournavitis, Georgios; Franke, Björn

doi:10.1145/1854273.1854321

Cited by 40 publications

(32 citation statements)

References 26 publications

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“…Tournavitis and Franke [34] propose IR Profiling, a hierarchical whole program representation focused on the extraction of pipeline parallelism. From the original sequential program, an instrumented executable is generated.…”

Section: Related Workmentioning

confidence: 99%

A novel compiler support for automatic parallelization on multicore systems

et al. 2013

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The widespread use of multicore processors is not a consequence of significant advances in parallel programming.In contrast, multicore processors arise due to the complexity of building power-efficient, high-clock-rate, single-core chips. Automatic parallelization of sequential applications is the ideal solution for making parallel programming as easy as writing programs for sequential computers. However, automatic parallelization remains a grand challenge due to its need for complex program analysis and the existence of unknowns during compilation. This paper proposes a new method for converting a sequential application into a parallel counterpart that can be executed on current multicore processors. It hinges on an intermediate representation based on the concept of domain-independent kernel (e.g., assignment, reduction, recurrence). Such kernel-centric view hides the complexity of the implementation details, enabling the construction of the parallel version even when the source code of the sequential application contains different syntactic variations of the computations (e.g., pointers, arrays, complex control flows). Experiments that evaluate the effectiveness and performance of our approach with respect to state-of-the-art compilers are also presented. The benchmark suite consists of synthetic codes that represent common domain-independent kernels, dense/sparse linear algebra and image processing routines, and full-scale applications from SPEC CPU2000.

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Section: Related Workmentioning

confidence: 99%

A novel compiler support for automatic parallelization on multicore systems

et al. 2013

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“…An intelligent analysis engine [7], [8] [58], [82], running in the background does the trick of parallelization. The intelligent engine includes static analysis of code fordata dependency [59] check among different code segments.…”

Section: Automatic Parallelizationmentioning

confidence: 99%

“…Semiautomatic parallelization techniques [7], [8], [44], [62], [73], [74], [75], [77], [78], [82], [ 86], [88], [91] do not provide end-to-end code parallelization option. Such techniques do not believe in using the application asa black box for parallelization.…”

Section: Semiautomatic Parallelizationmentioning

confidence: 99%

“…First stage of the parallelization process is parallelization identification. The code is parsed and analyzed (static or dynamic dependency analysis [7], [8], [58], [82], [99]) to search for the code sections that can be executed concurrently. Apart from dependency checks, profiling of code is also done to identify hot spots.…”

Section: 1classification Based On Parallelization Stagesmentioning

confidence: 99%

“…al. [6], [7], [8], have presented similar but limited review and classification of such tools. However, this review includes most of the relevant tools proposed in recent times as well as significant older tools.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Review of Parallelization Tools and Introduction to Easypar

Sah¹,

Vaidya²

2012

IJCA

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Multicore processors have paved the way to increase the performance of any application by the virtue of benefits of parallelization. However, exploiting parallelism from a program is not easy, as it requires parallel programming expertise. In addition, manual parallelization is a cumbersome, time consuming and inefficient process. A number of tools proposed in the past ease the effort of parallel programming. This paper presents a classification of such parallelization tools. The classification is based on different eras of tool development, role playedby these tools in various parallelization stages, and features provided by parallel program assistance tools. Classification of tools concludes with a discussion on requirements of futuristic parallelization tools. Finally, this paper proposesour on-going work about the development of a parallel program assistance tool called EasyPar, which is a parallel program assistance tool.

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ParTool: A Feedback-Directed Parallelizer

Mishra

Aggarwal

2011

Lecture Notes in Computer Science

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Abstract. We present a tool which gives detailed feedback to application developers on how their programs can be made amenable to parallelization. Also, the tool automatically parallelizes the code for a large number of constructs. Since the tool outputs a parallelized code with OpenMP pragmas, the feedback cycle can be run any number of times till the desired performance is achieved. ParTool also acts as a good learning tool to understand the usage of these parallel constructs and thus enables the developer to write better quality code in the future. Our results show that the use of compiler generated feedback can greatly improve the performance of not only benchmarked code but also real life applications.

show abstract

Semi-automatic extraction and exploitation of hierarchical pipeline parallelism using profiling information

Cited by 40 publications

References 26 publications

A novel compiler support for automatic parallelization on multicore systems

A novel compiler support for automatic parallelization on multicore systems

A Review of Parallelization Tools and Introduction to Easypar

ParTool: A Feedback-Directed Parallelizer

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