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14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid)

作者： Armstrong, Timothy G. Wozniak, Justin M. Wilde, Michael Foster, Ian T. Univ Chicago Dept Comp Sci Chicago IL 60637 USA Argonne Natl Lab Div Math & Comp Sci Argonne IL 60439 USA Univ Chicago Computat Inst Chicago IL 60637 USA Argonne Natl Lab Chicago IL USA

ISBN: (纸本)9781479927845

The data-driven task parallelism execution model can support parallel programming models that are well suited for large-scale distributed-memory parallel computing, for example, simulations and analysis pipelines running on clusters and clouds. We describe a novel compiler intermediate representation and optimizations for this execution model, including adaptions of standard techniques alongside novel techniques. These techniques are applied to Swift/T, a high-level scripting language for flexible data flow composition of functions, which may be serial or use lower-level parallel programming models such as MPI and OpenMP. This paper presents preliminary results, indicating that our compiler optimizations reduce communication overhead by 70% to 93% on distributed-memory systems.

关键词： Parallel programming compiler optimization data flow parallelism garbage collection reference counting

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compiler optimization for Superscalar and Pipelined Processors

Compiler Optimization for Superscalar and Pipelined Processo...

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IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)

作者： Bharadwaj, Vishnu P. Rao, Mahesh Maharaja Inst Technol Dept ECE Mysuru India

ISBN: (纸本)9781509016235

The exploitation of parallelism at both the multiprocessor or multicore level and at the instruction level is the means to achieve high-performance. The compiler for VLIW and superscalar processors must expose sufficient parallelism to effectively utilize the parallel hardware. The amount of instruction level parallelism available to VLIW processors or superscalar processors can be limited. This will limit the performance of these processors to a certain extent. However, with compiler optimization techniques, its performance can be increased to greater extent. This evaluation shows that utilizing the existing resources of the processor with certain programmer constraints and an efficient scheduling of independent and dependent blocks of instructions, we can increase the performance of the processors. As compiler optimization interact with the micro-architecture in complex ways, certain programmer constraints can be added to reduce the complexity and help the compiler to structure the Assembly code in a manner which can be used for out-of-order execution of the code. This paper provides new methods and improvements for the structure of the Assembly code for execution on superscalar processors.

关键词： Blocks Code scheduling compiler optimization Instruction level parallelism Micro-architecture Speculative execution Superscalar processors VLIW processors

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AN ATTRIBUTE GRAMMAR APPROACH TO compiler optimization OF INTRAMODULE INTERPROCESS COMMUNICATION

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INTERNATIONAL JOURNAL OF PARALLEL PROGRAMMING 1991年第3期20卷 181-202页

作者： MCNAMEE, CM OLSSON, RA CALIF STATE UNIV SACRAMENTO DEPT COMP SCISACRAMENTOCA 95819 UNIV CALIF DAVIS DEPT COMP SCIDAVISCA 95616

Earlier work has shown the effectiveness of hand-applied program transformations optimizing high-level interprocess communication mechanisms. This paper describes the static analysis techniques necessary to ensure correct compiler application of the optimizing transformations. These techniques include both dataflow analysis and interprocess analysis. This paper focuses on the analysis of communication mechanisms within program modules;however, the analysis techniques can be generalized to handle inter-module optimization analysis as well. The major contributions of this paper include the application of dataflow analysis and the extension of interprocedural analysis-interprocess analysis-to real concurrent programming languages and, more specifically, to the optimization of interprocess communication and synchronization mechanisms that use both static and dynamic channels. In addition, the use of attribute grammars to perform interprocess analysis is significant. This paper also describes an implementation of both intra-process dataflow and interprocess analysis techniques using attribute grammars.

关键词： compiler optimization CONCURRENT PROGRAMMING LANGUAGES INTERPROCESS COMMUNICATION MECHANISMS STATIC ANALYSIS ATTRIBUTE GRAMMARS DATA-FLOW ANALYSIS

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Lost In Translation: Exposing Hidden compiler optimization Opportunities

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COMPUTER JOURNAL 2022年第3期65卷 718-735页

作者： Georgiou, Kyriakos Chamski, Zbigniew Garcia, Andres Amaya May, David Eder, Kerstin Univ Bristol Trustworthy Syst Lab Dept Comp Sci Bristol BS8 1UB Avon England

Existing iterative compilation and machine learning-based optimization techniques have been proven very successful in achieving better optimizations than the standard optimization levels of a compiler. However, they were not engineered to support the tuning of a compiler's optimizer as part of the compiler's daily development cycle. In this paper, we first establish the required properties that a technique must exhibit to enable such tuning. We then introduce an enhancement to the classic nightly routine testing of compilers, which exhibits all the required properties and thus is capable of driving the improvement and tuning of the compiler's common optimizer. This is achieved by leveraging resource usage and compilation information collected while systematically exploiting prefixes of the transformations applied at standard optimization levels. Experimental evaluation using the LLVM v6.0.1 compiler demonstrated that the new approach was able to reveal hidden cross-architecture and architecture-dependent potential optimizations on two popular processors: the Intel i5-6300U and the Arm Cortex-A53-based Broadcom BCM2837 used in the Raspberry Pi 3B+. As a case study, we demonstrate how the insights from our approach enabled us to identify and remove a significant shortcoming of the CFG simplification pass of the LLVM v6.0.1 compiler.

关键词： auto-tuning compilers performance compiler optimization compiler quality resource-aware computing Raspberry Pi

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Towards Compile-Time-Reducing compiler optimization Selection via Machine Learning 21

Towards Compile-Time-Reducing Compiler Optimization Selectio...

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50th International Conference on Parallel Processing (ICPP)

作者： Jayatilaka, Tarindu Ueno, Hideto Georgakoudis, Giorgis Park, EunJung Doerfert, Johannes Univ Moratuwa Moratuwa Sri Lanka Univ Tokyo Tokyo Japan Lawrence Livermore Natl Lab Livermore CA 94550 USA Los Alamos Natl Lab Los Alamos NM USA Argonne Natl Lab Lemont IL USA

ISBN: (纸本)9781450384414

compilers come with a multitude of optimizations to choose from, and the chosen optimizations significantly affect the performance of the code being optimized. Selecting the optimal set of optimizations to apply and determining the order to run them is non-trivial. All of these optimizations closely interact with each other, and an optimization's ability to improve the code heavily depends on how the previous optimizations modified it. The current approach to solve this is to use a one-size-fits-all optimization sequence, that is designed to perform reasonably well for any given source code. In other words, they are not designed to optimize depending on the nature of the code, which usually results in sub-optimal performance. In this paper, we present preliminary work tackling the problem from the perspective of compile-time by adapting the optimization sequence to cater to different program types. We start by analyzing how the source code interacts with the passes, as well as how the passes interact with each other. We use this information and propose two potential methods driven by machine learning to run customized optimization sequences on the source code. First, we look at how we can use a neural network to predict and skip passes that do not optimize the code to improve compilation time. Second, we look at how we can use clustering and predictive models to select custom pass pipelines. We believe that our approach has the potential to replace the current one-size-fits-all approach, with better optimization sequences that are tailored to perform better depending on the code. At the same time, it will allow testing the potential pipelines thoroughly, a practical requirement to gain confidence in the correctness of the compiler.

关键词： compiler optimization phase-ordering machine learning optimization selection

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LLM compiler: Foundation Language Models for compiler optimization 25

LLM COMPILER: Foundation Language Models for Compiler Optimi...

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34th International Conference on compiler Construction

作者： Cummins, Chris Seeker, Volker Grubisic, Dejan Roziere, Baptiste Gehring, Jonas Synnaeve, Gabriel Leather, Hugh Meta Menlo Pk CA 94025 USA Meta Paris France

ISBN: (纸本)9798400714078

Large Language Models (LLMs) have demonstrated remarkable capabilities across a variety of software engineering and coding tasks. However, their application in the domain of code and compiler optimization remains underexplored. Training LLMs is resource-intensive, requiring substantial GPU hours and extensive data collection, which can be prohibitive. To address this gap, we introduce LLM compiler, a suite of robust, openly available, pre-trained models specifically designed for compiler tasks. Built on the foundation of CODE LLAMA, LLM compiler enhances the understanding of compiler intermediate representations (IRs), assembly language, and optimization techniques. The models have been trained on a vast corpus of 546 billion tokens of LLVM-IR and assembly code and have undergone instruction fine-tuning to interpret compiler behavior. To demonstrate the utility of these research tools, we also present fine-tuned versions of the models with enhanced capabilities in optimizing code size and disassembling from x86_64 and ARM assembly back into LLVM-IR. These achieve 77 % of the optimising potential of an autotuning search, and 45 % disassembly round trip (14 % exact match). LLM compiler is released under a bespoke commercial license to allow wide reuse and is available in two sizes: 7 billion and 13 billion parameters. Our aim is to provide scalable, cost-effective foundational models for further research and development in compiler optimization by both academic researchers and industry practitioners. Since we released LLM compiler the community has quantized, repackaged, and downloaded the models over 250k times.

关键词： Large Language Models compiler optimization Code optimization Pre-trained Models LLVM-IR

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A Novel Technique for Orchestration of compiler optimization Functions Using Branch and Bound Strategy

A Novel Technique for Orchestration of Compiler Optimization...

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IEEE International Advance Computing Conference

作者： Desai, Nikita P. Dharmsinh Desai Univ IT Dept Nadiad Gujarat India

ISBN: (纸本)9781424429271

Code optimization involves the application of rules and algorithms to program code, with the goal of making it faster, smaller, more efficient, and so on. Applying the right compiler optimizations to a particular program can have a significant impact on program performance. The effectiveness of compiler optimizations is determined by the combination of target architectures, target application, and the compilation environment, which is defined by the setting of the compiler optimizations and compiler heuristics. Finding a compiler setting which is optimal requires a delicate tradeoff between these factors. Due to the non-linear interaction of compiler optimizations, however, determining the best setting is nontrivial. The trivial solution of trying all combinations of techniques would be infeasible, as it is of complexity O (2(n)) even for "n" on-off optimizations. There have been several proposed techniques that search the space of compiler options to find good solutions;however such approaches can be expensive. In current compilers, through command line arguments, the user must decide which optimizations are to be applied in a given compilation run. Clearly, this is not a long-term solution. As compiler optimizations get increasingly numerous and complex, this problem must find an automated solution. In this paper, a new technique is suggested, which prunes the large search space using branch and bound technique, so that only the area in search space which is most beneficial is given higher priority for further exploration whereas the least promising regions are straightaway pruned off, thus saving time by not exploring those regions which give a handful or negligible benefits. Also further probes of the search space tree are halted, once it is determined that the relative improvement in the time is not considerable as compared to the cost incurred for further probes. The time complexity of proposed method under worst case is found to be of O (n(2)) and under best

关键词： Branch and bound strategy compiler optimization Exponential Search space Orchestration of optimization functions

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The Correctness-Security Gap in compiler optimization

The Correctness-Security Gap in Compiler Optimization

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IEEE Security and Privacy Workshops (SPW)

作者： D'Silva, Vijay Payer, Mathias Song, Dawn Google Inc San Francisco CA 94105 USA Purdue Univ W Lafayette IN 47907 USA Univ Calif Berkeley Berkeley CA 94720 USA

ISBN: (纸本)9781479999330

There is a significant body of work devoted to testing, verifying, and certifying the correctness of optimizing compilers. The focus of such work is to determine if source code and optimized code have the same functional semantics. In this paper, we introduce the correctness-security gap, which arises when a compiler optimization preserves the functionality of but violates a security guarantee made by source code. We show with concrete code examples that several standard optimizations, which have been formally proved correct, inhabit this correctness-security gap. We analyze this gap and conclude that it arises due to techniques that model the state of the program but not the state of the underlying machine. We propose a broad research programme whose goal is to identify, understand, and mitigate the impact of security errors introduced by compiler optimizations. Our proposal includes research in testing, program analysis, theorem proving, and the development of new, accurate machine models for reasoning about the impact of compiler optimizations on security.

关键词： compiler optimization formal correctness security compilers Security safety distance Source code research programmes concrete specification

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Bosehedral: compiler optimization for Bosonic Quantum Computing 51

Bosehedral: Compiler Optimization for Bosonic Quantum Comput...

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ACM/IEEE 51st Annual International Symposium on Computer Architecture (ISCA)

作者： Zhou, Junyu Liu, Yuhao Shi, Yunong Javadi-Abhari, Ali Li, Gushu Univ Penn Dept Comp & Informat Sci 200 S 33Rd St Philadelphia PA 19104 USA AWS Quantum Technol New York NY USA IBM Quantum New York NY USA

ISBN: (纸本)9798350326598;9798350326581

Bosonic quantum computing, based on the infinite-dimensional qumodes, has shown promise for various practical applications that are classically hard. However, the lack of compiler optimizations has hindered its full potential. This paper introduces Bosehedral, an efficient compiler optimization framework for (Gaussian) Boson sampling on Bosonic quantum hardware. Bosehedral overcomes the challenge of handling infinite-dimensional qumode gate matrices by performing all its program analysis and optimizations at a higher algorithmic level, using a compact unitary matrix representation. It optimizes qumode gate decomposition and logical-to-physical qumode mapping, and introduces a tunable probabilistic gate dropout method. Overall, Bosehedral significantly improves the performance by accurately approximating the original program with much fewer gates. Our evaluation shows that Bosehedral can largely reduce the program size but still maintain a high approximation fidelity, which can translate to significant end-to-end application performance improvement.

关键词： Bosonic Quantum Computing Gaussian Boson Sampling compiler optimization

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Mitigating the compiler optimization Phase-Ordering Problem using Machine Learning 12

Mitigating the Compiler Optimization Phase-Ordering Problem ...

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ACM International Conference on Object Oriented Programming Systems Languages and Applications

作者： Kulkarni, Sameer Cavazos, John Univ Delaware Newark DE 19716 USA

ISBN: (纸本)9781450315616

Today's compilers have a plethora of optimizations to choose from, and the correct choice of optimizations can have a significant impact on the performance of the code being optimized. Furthermore, choosing the correct order in which to apply those optimizations has been a long standing problem in compilation research. Each of these optimizations interacts with the code and in turn with all other optimizations in complicated ways. Traditional compilers typically apply the same set of optimization in a fixed order to all functions in a program, without regard the code being optimized. Understanding the interactions of optimizations is very important in determining a good solution to the phase-ordering problem. This paper develops a new approach that automatically selects good optimization orderings on a per method basis within a dynamic compiler. Our approach formulates the phase-ordering problem as a Markov process and uses a characterization of the current state of the code being optimized to creating a better solution to the phase ordering problem. Our technique uses neuro-evolution to construct an artificial neural network that is capable of predicting beneficial optimization ordering for a piece of code that is being optimized. We implemented our technique in Jikes RVM and achieved significant improvements on a set of standard Java benchmarks over a well-engineered fixed order.

关键词： compilers optimization Code generation Connectionism and neural nets Phase Ordering compiler optimization Machine learning Neural Networks Java Jikes RVM Code Feature Generation

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