Profiling Data-Dependence to Assist Parallelization: Framework, Scope, and Optimization

Ketterlin, Alain; Clauss, Philippe

doi:10.1109/micro.2012.47

Cited by 47 publications

(19 citation statements)

References 20 publications

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“…The partitioning algorithm only uses the tileto-tile dependences, so it can reduce many runtime work. Ketterlin et al [67] introduced a tool named Parwiz, which synthetically use static and dynamic techniques for multiple types of parallelism. The main functions include using one or more trail executions to detect parallelism, performing loop transformation, and obtaining the runtime behavior of the loop.…”

Section: The Dynamic Schedulingmentioning

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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Section: The Dynamic Schedulingmentioning

confidence: 99%

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

Ye¹,

Deng²,

Zou³

2016

IJGDC

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“…Profiling has been used in program analysis for various purposes, examples of which include identifying hot regions for mapping to accelerators [41], disambiguating dependences for speculative optimizations [12], and identifying parallel loops [29,32]. In this work, we use profiling for a completely different purpose.…”

Section: Profile-guided Memory Partitioningmentioning

confidence: 99%

Memory Partitioning in the Limit

Kültürsay

Ebcioğlu

Küçük

et al. 2015

Int J Parallel Prog

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The key difficulties in designing memory hierarchies for future computing systems with extreme scale parallelism include (1) overcoming the design complexity of system-wide memory coherence, (2) achieving low power, and (3) achieving fast access times within such a memory hierarchy. Towards addressing these difficulties, in this paper we propose an automatic memory partitioning method to generate a customized, application-specific, energy-efficient, low latency memory hierarchy, tailored to particular application programs. Given a software program to accelerate, our method automatically partitions the memory of the original program, creates a new customized application-specific multi-level memory hierarchy for the program, and modifies the original program to use the new memory hierarchy. This new memory hierarchy and modified program are then used as the basis to create a customized, application-specific, highly parallel hardware accelerator, which is functionally equivalent to the original, unmodified program. Using dependence analysis and fine grain valid/dirty bits, the memories in the generated hierarchy can operate in parallel with-B Emre Kültürsay 123 338 Int J Parallel Prog (2016) 44:337-380 out the need for maintaining coherence and can be independently initialized/flushed from/to their parent memories in the hierarchy, enabling a scalable memory design. The generated memories are fully compatible with the memory addressing in the original software program; this compatibility feature enables the translation of general software applications to application-specific accelerators. We also provide a compiler analysis method to perform accurate dependence analysis for memory partitioning based on symbolic execution, and a profiler-based futuristic limit study to identify the maximum gains that can be achieved by memory partitioning.

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“…Tools for discovering parallelism [10,57,13,62,15,63] analyzes data dependences to identify the most promising parallelization opportunities. Runtime scheduling frameworks [64,65,66,67] analyzes data dependences to add more parallelism to programs by dispatching code sections in a more effective way.…”

Section: Data-dependence Analysismentioning

confidence: 99%

“…Dynamic dependence profiling captures only those dependences that actually occur at runtime. Although dependence profiling is inherently input sensitive, the results are still useful in many situations, which is why such profiling forms the basis of many program analysis tools [10,62,15]. Besides, input sensitivity can be addressed to some degree by running the target program with changing inputs and computing the union of all collected dependences.…”

Section: Dynamic Approachesmentioning

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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Profiling Data-Dependence to Assist Parallelization: Framework, Scope, and Optimization

Cited by 47 publications

References 20 publications

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

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

Memory Partitioning in the Limit

Discovery of Potential Parallelism in Sequential Programs

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