Portable section-level tuning of compiler parallelized applications

Mustafa, Dheya; Eigenmann, Rudolf

doi:10.1109/sc.2012.97

Cited by 7 publications

(10 citation statements)

References 31 publications

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“…Profile-based optimization lies between program-level and section-level tuning [3]. It is similar to section-level tuning, with a p-window size of 1(discussed in section B.2), and a constant parallelization threshold for all loops in the program.…”

Section: ) Profile-based Optimizationmentioning

confidence: 99%

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A Survey of Performance Tuning Techniques and Tools for Parallel Applications

Mustafa

2022

IEEE Access

Self Cite

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Automatic parallelization of sequential programs combined with autotuning is an alternative to manual parallelization. With wider research directions and the increased number of performance tuning tools that have been developed, it becomes harder to choose a particular tuning tool to use. This paper reviews the fundamentals of different performance optimization and tuning techniques. It also surveys many tuning frameworks and classifies them into different groups based on their criteria. Developing benchmarks for HPC and validating their accuracy have been demanding tasks for computer architects, researchers, and application developers. In addition to providing a survey of performance tuning tools, we also performed a detailed review of current benchmarks and discussed the requirements for future benchmarks. We performed a detailed comparison of these tuning tools based on other features such as speedup and infrastructure details. We believe that this paper will be a very useful resource for the parallel computing community, especially for early-stage parallel computing and performance researchers to gain exposure to the existing performance optimization options.

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Section: ) Profile-based Optimizationmentioning

confidence: 99%

“…An automatic tuning system is needed to tune program performance quickly in an exponential search space, and to achieve high program performance. In previous work [3], we introduced a novel, fast tuning algorithm that is able to optimize individual program sections.…”

Section: Introductionmentioning

confidence: 99%

A Survey of Performance Tuning Techniques and Tools for Parallel Applications

Mustafa

2022

IEEE Access

Self Cite

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“…Concerning autotuning, there are several offline approaches focusing the tuning of OpenMP parameters [30,31,32], which may also be knobs on the ANTAREX approach. There are also frameworks focused on more general, application-level autotuning [33,34,35].…”

Section: Related Workmentioning

confidence: 99%

The ANTAREX tool flow for monitoring and autotuning energy efficient HPC systems

Silvano

Agosta

Barbosa

et al. 2017

2017 International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation (SAMOS)

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Designing and optimizing HPC applications are difficult and complex tasks, which require mastering specialized languages and tools for performance tuning. As this is incompatible with the current trend to open HPC infrastructures to a wider range of users, the availability of more sophisticated programming languages and tools to assist and automate the design stages is crucial to provide smoothly migration paths towards novel heterogeneous HPC platforms. The ANTAREX project intends to address these issues by providing a tool flow, a Domain Specific Launguage and APIs to provide application's adaptivity and to runtime manage and autotune applications for heterogeneous HPC systems. Our DSL provides a separation of concerns, where analysis, runtime adaptivity, performance tuning and energy strategies are specified separately from the application functionalities with the goal to increase productivity, significantly reduce time to solution, while making possible the deployment of substantially improved implementations. This paper presents the ANTAREX tool flow and shows the impact of optimization strategies in the context of one of the ANTAREX use cases related to personalized drug design. We show how simple strategies, not devised by typical compilers, can substantially speedup the execution and reduce energy consumption.

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“…For a shared memory system, code with OpenMP directives is to be generated. The C file generated by the CI can be further optimized using existing autotuners [5,27,28]. …”

Section: Code Instrumentormentioning

confidence: 99%

Bridging the Performance Gap between Manual and Automatic Compilers with Intent-based Compilation

Ahmed¹

2014

IJCA

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In spite of years of research in automatic parallelization, progress has been slow in terms of tools that can consistently generate scalable, portable and efficient code for multiple architectures. Moreover, a substantial difference in efficiency exists between code generated automatically and code generated by an expert programmer. Although the fact that the best sequential algorithm for a problem can be very different from the best parallel algorithm is well known, the feature of algorithm substitution is absent from most tools available today. However, automatically identifying an algorithm used in code is not trivial considering the nuances in programming style, algorithmic representations and expressions. This paper presents a novel Intent Based Compilation approach that uses a rule-based Expert System Engine to identify the intent of the algorithm used in the code based on fine-and coarse-grained features extracted from code. Using this information, the most optimized algorithm for the target architecture is then substituted. Results obtained by using Amoeba, a framework that incorporates this methodology, on codes obtained from the public domain are presented.

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Portable section-level tuning of compiler parallelized applications

Cited by 7 publications

References 31 publications

A Survey of Performance Tuning Techniques and Tools for Parallel Applications

A Survey of Performance Tuning Techniques and Tools for Parallel Applications

The ANTAREX tool flow for monitoring and autotuning energy efficient HPC systems

Bridging the Performance Gap between Manual and Automatic Compilers with Intent-based Compilation

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