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CLC: A cross-level program characterization method

PERFORMANCE EVALUATION 2023年第1期161卷

作者： Tang, Li Pakin, Scott Los Alamos Natl Lab Appl Comp Sci CCS-7PO Box 1663 Los Alamos NM 87545 USA

characterization of program execution plays a key role in performance improvement. There are numerous transformations applied to each step that a program takes on its lowering from source code to a compiler intermediate representation to machine language to microarchitecture-specific execution. The unpredictable benefit of each transformation step could lead a notionally superior algorithm to exhibit inferior perfor-mance once actually run, and it can be hard to discern which step in the transformation path contradicted the code developer's assumptions. Conventional approaches to program-execution characterization consider the behav-ior after only a single one of those steps, which limits the information that can be provided to the user. To help address the issue of myopic views of program execution, this paper presents a novel cross-level characterization approach for understanding the behavior of program execution at different levels in the process of writing, compiling, and running a program. We show that this approach provides a richer view of the sources of performance gains and losses and helps identify program execution in a more accurate manner. & COPY;2023 Published by Elsevier B.V.

关键词： Performance analysis Benchmarking program characterization Binary instrumentation

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program State Element characterization 2023

Program State Element Characterization

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21st ACM/IEEE International Symposium on Code Generation and Optimization (CGO)

作者： Deiana, Enrico Armenio Suchy, Brian Wilkins, Michael Homerding, Brian McMichen, Tommy Dunajewski, Katarzyna Dinda, Peter Hardavellas, Nikos Campanoni, Simone Northwestern Univ Evanston IL 60208 USA

ISBN: (纸本)9798400701016

Modern programming languages offer abstractions that simplify software development and allow hardware to reach its full potential. These abstractions range from the well-established OpenMP language extensions to newer C++ features like smart pointers. To properly use these abstractions in an existing codebase, programmers must determine how a given source code region interacts with program State Elements (PSEs) (i.e., the program's variables and memory locations). We call this process program State Element characterization (PSEC). Without tool support for PSEC, a programmer's only option is to manually study the entire codebase. We propose a profile-based approach that automates PSEC and provides abstraction recommendations to programmers. Because a profile-based approach incurs an impractical overhead, we introduce the Compiler and Runtime Memory Observation Tool (CARMOT), a PSEC-specific compiler co-designed with a parallel runtime. CARMOT reduces the overhead of PSEC by two orders of magnitude, making PSEC practical. We show that CARMOT's recommendations achieve the same speedup as hand-tuned OpenMP directives and avoid memory leaks with C++ smart pointers. From this, we argue that PSEC tools, such as CARMOT, can provide support for the rich ecosystem of modern programming language abstractions.

关键词： code optimization dynamic analysis program characterization

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SupermarQ: A Scalable Quantum Benchmark Suite 28

SupermarQ: A Scalable Quantum Benchmark Suite

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28th Annual IEEE International Symposium on High-Performance Computer Architecture (HPCA)

作者： Tomesh, Teague Gokhale, Pranav Omole, Victory Ravi, Gokul Subramanian Smith, Kaitlin N. Viszlai, Joshua Wu, Xin-Chuan Hardavellas, Nikos Martonosi, Margaret R. Chong, Frederic T. Princeton Univ Dept Comp Sci Princeton NJ 08544 USA Supertech Chicago IL 60615 USA Univ Chicago Dept Comp Sci Chicago IL 60637 USA Northwestern Univ Dept Comp Sci Evanston IL 60208 USA

ISBN: (纸本)9781665420273

The emergence of quantum computers as a new computational paradigm has been accompanied by speculation concerning the scope and timeline of their anticipated revolutionary changes. While quantum computing is still in its infancy, the variety of different architectures used to implement quantum computations make it difficult to reliably measure and compare performance. This problem motivates our introduction of SupermarQ, a scalable, hardware-agnostic quantum benchmark suite which uses application-level metrics to measure performance. SupermarQ is the first attempt to systematically apply techniques from classical benchmarking methodology to the quantum domain. We define a set of feature vectors to quantify coverage, select applications from a variety of domains to ensure the suite is representative of real workloads, and collect benchmark results from the IBM, IonQ, and AQT@LBNL platforms. Looking forward, we envision that quantum benchmarking will encompass a large cross-community effort built on open source, constantly evolving benchmark suites. We introduce SupermarQ as an important step in this direction.

关键词： Quantum Computing Benchmarking program characterization

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Automatic Selection of Tuning Plugins in PTF Using Machine Learning 34

Automatic Selection of Tuning Plugins in PTF Using Machine L...

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34th IEEE International Parallel and Distributed Processing Symposium (IPDPS)

作者： Mijakovic, Robert Gerndt, Michael Bavarian Acad Sci & Humanities Leibniz Supercomp Ctr LRZ Garching Germany Tech Univ Munich Inst Informat Garching Germany

ISBN: (纸本)9781728174457

Performance tuning of scientific codes often requires tuning many different aspects like vectorization, OpenMP synchronization, MPI communication, and load balancing. The Periscope Tuning Framework (PTF), an online automatic tuning framework, relies on a flexible plugin mechanism providing tuning plugins for different tuning aspects. Individual plugins can be combined for convenience into meta-plugins. Since each plugin can take considerable execution time for testing various combination of the tuning parameters, it is desirable to automatically predict the tuning potential of plugins for programs before their application. We developed a generic automatic prediction mechanism based on machine learning techniques for this purpose. This paper demonstrates this technique in the context of the Compiler Flags Selection plugin, that tunes the parameters of a user specified compiler for a given application.

关键词： Parallel computing performance tuning auto-tuning PTF program characterization machine learning

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Heavy Tails in program Structure

IEEE COMPUTER ARCHITECTURE LETTERS

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IEEE COMPUTER ARCHITECTURE LETTERS 2017年第1期16卷 34-37页

作者： Sasaki, Hiroshi Su, Fang-Hsiang Tanimoto, Teruo Sethumadhavan, Simha Columbia Univ Dept Comp Sci New York NY 10027 USA Kyushu Univ Grad Sch Informat Sci & Elect Engn Fukuoka 8190395 Japan

Designing and optimizing computer systems require deep understanding of the underlying system behavior. Historically many important observations that led to the development of essential hardware and software optimizations were driven by empirical observations about program behavior. In this paper, we report an interesting property of program structures by viewing dynamic program execution as a changing network. By analyzing the communication network created as a result of dynamic program execution, we find that communication patterns follow heavy-tailed distributions. In other words, a few instructions have consumers that are orders of magnitude larger than most instructions in a program. Surprisingly, these heavy-tailed distributions follow the iconic power law previously seen in man-made and natural networks. We provide empirical measurements based on the SPEC CPU2006 benchmarks to validate our findings as well as perform semantic analysis of the source code to reveal the causes of such behavior.

关键词： program characterization statistical distribution empirical studies

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FEAST: An Automated Feature Selection Framework for Compilation Tasks 31

FEAST: An Automated Feature Selection Framework for Compilat...

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31st IEEE International Conference on Advanced Information Networking and Applications (IEEE AINA)

作者： Ting, Pai-Shun Tu, Chun-Chen Chen, Pin-Yu Lo, Ya-Yun Cheng, Shin-Ming Univ Michigan Dept Elect Engn & Comp Sci Ann Arbor MI 48109 USA Univ Michigan Dept Stat Ann Arbor MI 48109 USA IBM Corp TJ Watson Res Ctr Yorktown Hts NY 10598 USA Adobe Syst San Francisco CA 94103 USA Natl Taiwan Univ Sci & Technol Dept Comp Sci & Informat Engn Taipei 106 Taiwan

ISBN: (纸本)9781509060290

Modern machine-learning techniques greatly reduce the efforts required to conduct high-quality program compilation, which, without the aid of machine learning, would otherwise heavily rely on human manipulation as well as expert intervention. The success of the application of machine-learning techniques to compilation tasks can be largely attributed to the recent development and advancement of program characterization, a process that numerically or structurally quantifies a target program. While great achievements have been made in identifying key features to characterize programs, choosing a correct set of features for a specific compiler task remains an ad hoc procedure. In order to guarantee a comprehensive coverage of features, compiler engineers usually need to select excessive number of features. This, unfortunately, would potentially lead to a selection of multiple similar features, which in turn could create a new problem of bias that emphasizes certain aspects of a program's characteristics, hence reducing the accuracy and performance of the target compiler task. In this paper, we propose FEAture Selection for compilation Tasks (FEAST), an efficient and automated framework for determining the most relevant and representative features from a feature pool. Specifically, FEAST utilizes widely used statistics and machine-learning tools, including LASSO, sequential forward and backward selection, for automatic feature selection, and can in general be applied to any numerical feature set. This paper further proposes an automated approach to compiler parameter assignment for assessing the performance of FEAST. Intensive experimental results demonstrate that, under the compiler parameter assignment task, FEAST can achieve comparable results with about 18% of features that are automatically selected from the entire feature pool. We also inspect these selected features and discuss their roles in program execution.

关键词： Compiler optimization feature selection LASSO machine learning program characterization

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Maximizing Hardware Prefetch Effectiveness with Machine Learning 17

Maximizing Hardware Prefetch Effectiveness with Machine Lear...

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2015 IEEE 17th International Conference on High Performance Computing and Communications (HPCC)

作者： Rahman, Saami Burtscher, Martin Zong, Ziliang Qasem, Apan Texas State Univ Dept Comp Sci San Marcos TX 78666 USA

ISBN: (纸本)9781479989379

Modern processors are equipped with multiple hardware prefetchers, each of which targets a distinct level in the memory hierarchy and employs a separate prefetching algorithm. However, different programs require different subsets of these prefetchers to maximize their performance. Turning on all available prefetchers rarely yields the best performance and, in some cases, prefetching even hurts performance. This paper studies the effect of hardware prefetching on multithreaded code and presents a machine-learning technique to predict the optimal combination of prefetchers for a given application. This technique is based on program characterization and utilizes hardware performance events in conjunction with a pruning algorithm to obtain a concise and expressive feature set. The resulting feature set is used in three different learning models. All necessary steps are implemented in a framework that reaches, on average, 96% of the best possible prefetcher speedup. The framework is built from open-source tools, making it easy to extend and port to other architectures.

关键词： learning (artificial intelligence) multi-threading performance evaluation public domain software storage management feature set hardware performance events hardware prefetch effectiveness maximization learning models machine learning memory hierarchy multiple hardware prefetchers multithreaded code open-source tools optimal prefetcher prediction performance maximization prefetcher speedup program characterization pruning algorithm Algorithms Hardware Optimization Prefetching Testing Training prefetching memory hierarchy Computer hardware Multithreading open source software Store management Machine Learning Performance Evaluation Artificial Intelligence Functional training

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***: novel extensible methodology, framework and public repository to collaboratively address Exascale challenges

***: novel extensible methodology, framework and public repo...

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25th ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis (SC)

作者： Fursin, Grigori INRIA Saclay France

ISBN: (纸本)9780769549569;9781467362184

Designing and optimizing novel computing systems became intolerably complex, ad-hoc, costly and error prone due to an unprecedented number of available tuning choices, and complex interactions between all software and hardware components. I present a novel holistic methodology, extensible infrastructure and public repository (*** and Collective Mind) to overcome the rising complexity of computer systems by distributing their characterization and optimization among multiple users. This technology effectively combines online auto-tuning, run-time adaptation, data mining and predictive modeling to collaboratively analyze thousands of codelets and datasets, explore large optimization spaces and detect abnormal behavior. It then extrapolates collected knowledge to suggest program optimizations, run-time adaptation scenarios or architecture designs to balance performance, power consumption and other characteristics. This technology has been recently successfully validated and extended in several academic and industrial projects with NCAR, Intel Exascale Lab, IBM and CAPS Entreprise, and we believe that it will be vital for developing future Exascale systems. © 2012 IEEE.

关键词： collaborative program optimization crowdsourcing machine learning online auto-tuning program characterization run-time adaptation self-tuning compiler

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Milepost GCC: Machine Learning Enabled Self-tuning Compiler

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INTERNATIONAL JOURNAL OF PARALLEL programMING 2011年第3期39卷 296-327页

作者： Fursin, Grigori Kashnikov, Yuriy Memon, Abdul Wahid Chamski, Zbigniew Temam, Olivier Namolaru, Mircea Yom-Tov, Elad Mendelson, Bilha Zaks, Ayal Courtois, Eric Bodin, Francois Barnard, Phil Ashton, Elton Bonilla, Edwin Thomson, John Williams, Christopher K. I. O'Boyle, Michael INRIA Saclay F-91893 Orsay France Univ Versailles F-78000 Versailles France IBM Res Lab IL-31905 Haifa Israel CAPS Entreprise F-35000 Rennes France ARC Int St Albans AL1 5HE England Univ Edinburgh Edinburgh EH8 9AB Midlothian Scotland

Tuning compiler optimizations for rapidly evolving hardware makes porting and extending an optimizing compiler for each new platform extremely challenging. Iterative optimization is a popular approach to adapting programs to a new architecture automatically using feedback-directed compilation. However, the large number of evaluations required for each program has prevented iterative compilation from widespread take-up in production compilers. Machine learning has been proposed to tune optimizations across programs systematically but is currently limited to a few transformations, long training phases and critically lacks publicly released, stable tools. Our approach is to develop a modular, extensible, self-tuning optimization infrastructure to automatically learn the best optimizations across multiple programs and architectures based on the correlation between program features, run-time behavior and optimizations. In this paper we describe Milepost GCC, the first publicly-available open-source machine learning-based compiler. It consists of an Interactive Compilation Interface (ICI) and plugins to extract program features and exchange optimization data with the *** open public repository. It automatically adapts the internal optimization heuristic at function-level granularity to improve execution time, code size and compilation time of a new program on a given architecture. Part of the MILEPOST technology together with low-level ICI-inspired plugin framework is now included in the mainline GCC. We developed machine learning plugins based on probabilistic and transductive approaches to predict good combinations of optimizations. Our preliminary experimental results show that it is possible to automatically reduce the execution time of individual MiBench programs, some by more than a factor of 2, while also improving compilation time and code size. On average we are able to reduce the execution time of the MiBench benchmark suite by 11% for the ARC reconfigur

关键词： Machine learning compiler Self-tuning compiler Adaptive compiler Automatic performance tuning Machine learning program characterization program features Collective optimization Continuous optimization Multi-objective optimization Empirical performance tuning Optimization repository Iterative compilation Feedback-directed compilation Adaptive compilation Optimization prediction Portable optimization

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Collective Optimization: A Practical Collaborative Approach

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ACM TRANSACTIONS ON ARCHITECTURE AND CODE OPTIMIZATION 2010年第4期7卷 20-20页

作者： Fursin, Grigori Temam, Olivier INRIA Saclay F-91893 Orsay France

Iterative optimization is a popular and efficient research approach to optimize programs using feedback-directed compilation. However, one of the key limitations that prevented widespread use in production compilers and day-to-day practice is the necessity to perform a large number of program runs with the same dataset and environment (architecture, OS, compiler) to test many different combinations of optimizations. In this article, we propose to overcome such a practical obstacle using collective optimization, where the task of optimizing a program or tuning default compiler optimization heuristic leverages the experience of many other users continuously, rather than being performed in isolation, and often redundantly, by each user. During this unobtrusive approach, performance information is sent to a central database after each run and statistically combined with the data from all users to suggest most profitable optimizations for a given program and an architecture, or to gradually improve default optimization level of a compiler for a given architecture. In this article, we address two key challenges of collective optimization. We show that it is possible to simultaneously learn and improve performance while avoiding long training phases. We also demonstrate how to use our approach with static compilers to learn optimizations across multiple datasets and architectures without even a reference run normally needed to compute speedups over the baseline optimization by using static function cloning and dynamic adaptation. We present a novel probabilistic approach based on competition among pairs of optimizations (program reaction to optimizations) to enable optimization knowledge reuse and achieve nearly the best possible iterative optimization performance. We implemented our technique in GCC (widespread production open-source compiler that supports multiple architectures) and connected it to a public collective optimization database at *** to gather profil

关键词： Design Languages Measurement Experimentation Performance Collective optimization iterative compilation continuous optimization statistical optimization adaptive compiler self-tuning computing systems collective optimization database multiple datasets program characterization program reaction to optimization function cloning runtime adaptation

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