Online Adaptive Code Generation and Tuning

Tiwari, Ananta; Hollingsworth, Jeffrey K.

doi:10.1109/ipdps.2011.86

Cited by 61 publications

(56 citation statements)

References 21 publications

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“…The authors have created a framework that statically evaluates different "computation kernels" and compiler optimisation flags to find an optimum in terms of execution time. Their work is based on Active Harmony [6], which is a well known framework for static and dynamic tuning. The application can define a search space and retrieve new configurations to test from a central tuning server.…”

Section: Related Workmentioning

confidence: 99%

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The READEX formalism for automatic tuning for energy efficiency

et al. 2017

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Energy efficiency is an important aspect of future exascale systems, mainly due to rising energy cost. Although High performance computing (HPC) applications are compute centric, they still exhibit varying computational characteristics in different regions of the program, such as compute-, memory-, and I/O-bound code regions. Some of today's clusters already offer mechanisms to adjust the system to the resource requirements of an application, e.g., by controlling the CPU frequency. However, manually tuning for improved energy efficiency is a tedious and painstaking task that is often neglected by application developers. The European Union's Horizon 2020 project READEX (Runtime Exploitation of Application Dynamism for Energyefficient eXascale computing) aims at developing a tools-aided approach for improved energy efficiency of current and future HPC applications. To reach this goal, the READEX project combines technologies from two ends of the compute spectrum, embedded systems and HPC, constituting a split design-time/runtime methodology. dynamic auto-tuning of fine-grained application regions using the systems scenario methodology, which was originally developed for improving the energy efficiency in embedded systems. This paper introduces the concepts of the READEX project, its envisioned implementation, and preliminary results that demonstrate the feasibility of this approach.

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Section: Related Workmentioning

confidence: 99%

“…5 and the proposed implementation of the tool suite is discussed in Sect. 6. The results of first experiments applying dynamic tuning to a target application are presented in Sect.…”

Section: Introductionmentioning

confidence: 99%

The READEX formalism for automatic tuning for energy efficiency

et al. 2017

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“…Greedy Algorithm and Hill Climbing Cooper et al [21] use meta-heuristics to find the optimal compilation parameters while reducing the number of evaluations during search space exploration from 10000 to a single one using profiling data and estimated virtual execution Genetic Algorithm Sandrieser et al [88] obtain speedup of 23% for hyper-block formation Parallel Rank Ordering Tiwari and Hollingsworth [93] and Tiwari et al [92] use PRO for automatic tuning of compilation process and report 46% performance improvement compared to the original code [57,58,99] Determining the best partitioning strategy Assumes that for any two functions with similar features, the same partitioning strategy can be used [95] Determining loops that benefit from parallelization and their best scheduling policy Targets OpenMP loop constructs only. Uses profiling to detect loop candidates, which may significantly increase the compilation time [1,21] Adaptive tuning of the compilation process Profiling data needs to be collected to perform the virtual executions.…”

Section: Meta-heuristicsmentioning

confidence: 99%

Using meta-heuristics and machine learning for software optimization of parallel computing systems: a systematic literature review

et al. 2018

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While modern parallel computing systems offer high performance, utilizing these powerful computing resources to the highest possible extent demands advanced knowledge of various hardware architectures and parallel programming models. Furthermore, optimized software execution on parallel computing systems demands consideration of many parameters at compile-time and run-time. Determining the optimal set of parameters in a given execution context is a complex task, and therefore to address this issue researchers have proposed different approaches that use heuristic search or machine learning. In this paper, we undertake a systematic literature review to aggregate, analyze and classify the existing software optimization methods for parallel computing systems. We review approaches that use machine learning or meta-heuristics for software optimization at compile-time and run-time. we discuss challenges and future research directions. The results of this study may help to better understand the state-of-the-art techniques that use machine learning and meta-heuristics to deal with the complexity of software optimization for parallel computing systems. Furthermore, it may aid in understanding the limitations of existing approaches and identification of areas for improvement.

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“…The PTF offers a set of standard search algorithms, including exhaustive search, probabilistic random search, individual search, multi-objective genetic search, and Active Harmony's [33] Nelder-Mead Simplex algorithm. The search algorithms are dynamically loaded on request of tuning plugins.…”

Section: B Tuning Pluginsmentioning

confidence: 99%

Tuning OpenCL Applications with the Periscope Tuning Framework

Bajrovic

Mijakovic

Dokulil

et al. 2016

2016 49th Hawaii International Conference on System Sciences (HICSS)

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Due to the complexity and diversity of new parallel architectures automatic tuning of parallel applications has become increasingly important for achieving acceptable performance levels as well as performance portability. The European AutoTune project developed a tuning framework which closely integrates and automates performance analysis and performance tuning. The Periscope Tuning Framework relies on a flexible plugin mechanism providing tuning plugins for different tuning aspects. This paper presents plugins for tuning the execution time of OpenCL kernels on three different architectures, namely standard multicore CPUs, Xeon Phi coprocessors, and GPUs. We present OpenCL tuning via the flags used during offline kernel compilation as well as through the selection of the most appropriate NDRange configuration, which defines the organization of parallel threads used for kernel execution. Both tuning plugins show significant performance impact and a clear dependence on the target architecture and thus improve performance portability via automatic tuning.

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Online Adaptive Code Generation and Tuning

Cited by 61 publications

References 21 publications

The READEX formalism for automatic tuning for energy efficiency

The READEX formalism for automatic tuning for energy efficiency

Using meta-heuristics and machine learning for software optimization of parallel computing systems: a systematic literature review

Tuning OpenCL Applications with the Periscope Tuning Framework

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