Using Template Matching to Infer Parallel Design Patterns

Huda, Zia Ul; Jannesari, Ali; Wolf, Felix

doi:10.1145/2688905

Cited by 18 publications

(11 citation statements)

References 30 publications

(30 reference statements)

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“…Also, in [1], the authors use parallel design patterns to develop efficient, maintainable, and portable parallel applications. Designers can develop parallel applications using refactoring approaches based on well-understood high-level parallel design patterns as in the ParaPhrase project [30] and [32]. In the DiscoPoP project [18], different parallel patterns such as pipeline are detected to parallelize sequential programs.…”

Section: Motivation For Classifying Parallel Patternsmentioning

confidence: 99%

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Using Machine Learning Techniques to Detect Parallel Patterns of Multi-threaded Applications

Deniz

Şen

2015

Int J Parallel Prog

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Multicore hardware and software are becoming increasingly more complex. The programmability problem of multicore software has led to the use of parallel patterns. Parallel patterns reduce the effort and time required to develop multicore software by effectively capturing its thread communication and data sharing characteristics. Hence, detecting the parallel pattern used in a multi-threaded application is crucial for performance improvements and enables many architectural optimizations; however, this topic has not been widely studied. We apply machine learning techniques in a novel approach to automatically detect parallel patterns and compare these techniques in terms of accuracy and speed. We experimentally validate the detection ability of our techniques on benchmarks including PARSEC and Rodinia. Our experiments show that the k-nearest neighbor, decision trees, and naive Bayes classifier are the most accurate techniques. Overall, decision trees are the fastest technique with the lowest characterization overhead producing the best combination of detection results. We also show the usefulness of the proposed techniques on synthetic benchmark generation.

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Section: Motivation For Classifying Parallel Patternsmentioning

confidence: 99%

“…In [32], the authors detect potential pipeline and do-all parallel patterns from sequential applications by using template matching technique. However, we detect six parallel patterns including task parallel and geometric decomposition in parallel multi-threaded application.…”

Section: Parallel Pattern Detectionmentioning

confidence: 99%

Using Machine Learning Techniques to Detect Parallel Patterns of Multi-threaded Applications

Deniz

Şen

2015

Int J Parallel Prog

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“…To support parallel pattern detection [82,83], DiscoPoP profiler also produces the Program Execution Tree (PET). We construct a PET using the following information obtained from both static analyses and profiling data:…”

Section: Control Structure Informationmentioning

confidence: 99%

“…Such features allow the straightforward application of pattern matching technique to detect parallel patterns. Details about parallel pattern detection is introduced in related work [82,83].…”

Section: Control Structure Informationmentioning

confidence: 99%

Discovery of Potential Parallelism in Sequential Programs

Jannesari

Wolf

2013

2013 42nd International Conference on Parallel Processing

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In the era of multicore processors, the responsibility for performance gains has been shifted onto software developers. Once improvements of the sequential algorithm have been exhausted, software-managed parallelism is the only option left. However, writing parallel code is still difficult, especially when parallelizing sequential code written by someone else. A key task in this process is the identification of suitable parallelization targets in the source code. Parallelism discovery tools help developers to find such targets automatically. Unfortunately, tools that identify parallelism during compilation are usually conservative due to the lack of runtime information, and tools relying on runtime information primarily suffer from high overhead in terms of both time and memory. This dissertation presents a generic framework for parallelism discovery based on dynamic program analysis, supporting various types of parallelism while incurring practically affordable overhead. The framework contains two main components: an efficient data-dependence profiler and a set of parallelism discovery algorithms based on a language-independent concept called Computational Unit.The data-dependence profiler serves as the foundation of the parallelism discovery framework. Traditional dependence profiling approaches introduce a tremendous amount of time and memory overhead. To lower the overhead, current methods limit their scope to the subset of the dependence information needed for the analysis they have been created for, sacrificing generality and discouraging reuse. In contrast, the profiler shown in this thesis addresses the problem via signature-based memory management and a lock-free parallel design. It produces detailed dependences not only for sequential but also for multi-threaded code without causing prohibitive overhead, allowing it to serve as a generic base for various program analysis techniques.Computational Units (CUs) provide a language-independent foundation for parallelism discovery. CUs are computations that follow the read-compute-write pattern. Unlike other concepts, they are not restricted to predefined language constructs. A program is represented as a CU graph, in which vertexes are CUs and edges are data dependences. This allows parallelism to be detected that spreads across multiple language constructs, taking code refactoring into consideration. The parallelism discovery algorithms cover both loop and task parallelism.Results of our experiments show that 1) the efficient data-dependence profiler has a very competitive average slowdown of around 80× with accuracy higher than 99.6%; 2) the frame- I would also like to thank my master students and student assistants for their coding support.Wolfram Gottschlich implemented the original version of the signature described in this thesis.Tuan Dung Nguyen implemented the lock-free parallel version of the DiscoPoP profiler. MichaelBeaumont tested the memory skipping technique. Daniel Fried implemented the method of characterizing DOALL loops using machin...

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“…In our previous work, an overview of our toolset DiscoPoP is provided, which includes the evaluation of the dynamic profiler with respect to its performance and memory consumption. In addition, our previous work also discusses the use of CUs to identify parallel patterns in existing sequential code that is discussed in greater detail in our other works . In our previous works, we focus on task parallelism within larger computations and functions, and identification of CUs using a different method with Use‐Definition Chain (UD Chain).…”

Section: Introductionmentioning

confidence: 99%

Dissecting sequential programs for parallelization—An approach based on computational units

Atre

Ul‐Huda

Wolf

et al. 2018

Concurrency and Computation

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Summary When trying to parallelize a sequential program, programmers routinely struggle during the first step: finding out which code sections can be made to run in parallel. While identifying such code sections, most of the current parallelism discovery techniques focus on specific language constructs. In contrast, we propose to concentrate on the computations performed by a program. In our approach, a program is treated as a collection of computations communicating with one another using a number of variables. Each computation is represented as a computational unit (CU). A CU contains the inputs and outputs of a computation, and the three phases of a computation are read, compute, and write. Based on the notion of CU, which ensures that the read phase executes before the write phase, we present a unified framework to identify both loop parallelism and task parallelism in sequential programs. We conducted a range of experiments on 23 applications from four different benchmark suites. Our approach accurately identified the parallelization opportunities in benchmark applications based on comparison with their parallel versions. We have also parallelized the opportunities identified by our approach that were not implemented in the parallel versions of the benchmarks and reported the speedup.

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Using Template Matching to Infer Parallel Design Patterns

Cited by 18 publications

References 30 publications

Using Machine Learning Techniques to Detect Parallel Patterns of Multi-threaded Applications

Using Machine Learning Techniques to Detect Parallel Patterns of Multi-threaded Applications

Discovery of Potential Parallelism in Sequential Programs

Dissecting sequential programs for parallelization—An approach based on computational units

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