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检索条件"主题词=Program compilers"

共 2718 条记录，以下是21-30 订阅

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Peephole Optimization Strategies for Intermediate Code in the Compilation Process 1

Peephole Optimization Strategies for Intermediate Code in th...

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1st International Conference on Intelligent Manufacturing and Cloud Computing, ICIMCC 2024

作者： Guan, Yunxin Yun, Jing Sun, Pengfei Su, Xiaoming College of Data Science and Application Inner Mongolia University of Technology Hohhot China

ISBN: (纸本)9781643685779

In the design of modern compilers, the generation and optimization of intermediate code play a crucial role. Serving as a bridge between source code and target machine code, intermediate code provides ample opportunities for optimization. This paper focuses on porthole optimization techniques, proposing a new optimization strategy aimed at reducing unnecessary computations and memory accesses by analyzing control and data flows within intermediate code, thereby enhancing program execution efficiency. We first outline the various stages of the compilation process, followed by a detailed exploration of the basic principles and implementation methods of porthole optimization. Finally, we validate the effectiveness and advantages of this optimization strategy through experiments. The experimental results indicate significant improvements in the execution performance of intermediate code after the adoption of porthole optimization, providing new insights and directions for compiler optimization. © 2025 The Authors.

关键词： program compilers

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Optimizing Deep Learning Inference Efficiency through Block Dependency Analysis 25

Optimizing Deep Learning Inference Efficiency through Block ...

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30th ACM International Conference on Architectural Support for programming Languages and Operating Systems, ASPLOS 2025

作者： Di, Zhanyuan Wang, Leping Shao, En Ma, Zhaojia Ren, Ziyi Hua, Feng Ma, Lixian Zhao, Jie Tan, Guangming Sun, Ninghui Sklp Institute of Computing Technology Cas Beijing China University of Chinese Academy of Sciences Beijing China Hunan University Changsha China

ISBN: (纸本)9798400710797

Inter-operator optimization in deep neural networks (DNNs) relies on accurate data dependency analysis. Traditional machine learning compilers (MLCs) perform static data dependency analysis at the element and operator levels, leading to two key limitations: complex dependencies that hinder efficient inter-operator optimizations, and overlooked parallelizable computations that underutilize GPU resources. We introduce BlockDepend, a novel MLC framework that addresses these issues through block-level dependency analysis. By examining the lower-level phases of compilation, BlockDepend extracts crucial block-level dependency information, simplifying complex relationships between operators and uncovering hidden parallelization opportunities. This allows for targeted optimization strategies that enhance memory access efficiency and improve GPU utilization. Our experiments demonstrate BlockDepend's effectiveness, achieving speedups of 1.71× and 2.88× compared to NVIDIA TensorRT and AMD MIGraphX, respectively, across various workloads. © 2025 ACM.

关键词： program compilers

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LLM-Vectorizer: LLM-Based Verified Loop Vectorizer 25

LLM-Vectorizer: LLM-Based Verified Loop Vectorizer

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23rd ACM/IEEE International Symposium on Code Generation and Optimization, CGO 2025

作者： Taneja, Jubi Laird, Avery Yan, Cong Musuvathi, Madan Lahiri, Shuvendu K. Microsoft Research Redmond United States University of Toronto Toronto Canada

ISBN: (纸本)9798400712753

Vectorization is a powerful optimization technique that significantly boosts the performance of high performance computing applications operating on large data arrays. Despite decades of research on auto-vectorization, compilers frequently miss opportunities to vectorize code. On the other hand, writing vectorized code manually using compiler intrinsics is still a complex, error-prone task that demands deep knowledge of specific architecture and compilers. In this paper, we evaluate the potential of large-language models (LLMs) to generate vectorized (Single Instruction Multiple Data) code from scalar programs that process individual array elements. We propose a novel finite-state-machine multi-agents based approach that harnesses LLMs and test-based feedback to generate vectorized code. Our findings indicate that LLMs are capable of producing high-performance vectorized code with run-time speedup ranging from 1.1x to 9.4x as compared to the state-of-the-art compilers such as Intel Compiler, GCC, and Clang. To verify the correctness of vectorized code, we use Alive2, a leading bounded translation validation tool for LLVM IR. We describe a few domain-specific techniques to improve the scalability of Alive2 on our benchmark dataset. Overall, our approach is able to verify 38.2% of vectorizations as correct on the TSVC benchmark dataset. © 2025 Copyright held by the owner/author(s).

关键词： program compilers

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Relax: Composable Abstractions for End-to-End Dynamic Machine Learning 25

Relax: Composable Abstractions for End-to-End Dynamic Machin...

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30th ACM International Conference on Architectural Support for programming Languages and Operating Systems, ASPLOS 2025

作者： Lai, Ruihang Shao, Junru Feng, Siyuan Lyubomirsky, Steven Hou, Bohan Lin, Wuwei Ye, Zihao Jin, Hongyi Jin, Yuchen Liu, Jiawei Jin, Lesheng Cai, Yaxing Jiang, Ziheng Wu, Yong Park, Sunghyun Srivastava, Prakalp Roesch, Jared Mowry, Todd C. Chen, Tianqi Carnegie Mellon University Pittsburgh United States OpenAI San Francisco United States Shanghai Jiao Tong University Shanghai China Nvidia Santa Clara United States University of Washington Seattle United States Hyperbolic San Francisco United States University of Illinois Urbana-Champaign Champaign United States ByteDance Seattle United States Netflix Los Gatos United States

ISBN: (纸本)9798400710797

Dynamic shape computations have become critical in modern machine learning workloads, especially in emerging large language models. The success of these models has driven the demand for their universal deployment across a diverse set of backend environments. In this paper, we present Relax, a compiler abstraction for optimizing end-to-end dynamic machine learning workloads. Relax introduces a cross-level abstraction that encapsulates computational graphs, loop-level tensor programs, and external library calls in a single representation. Relax also introduces first-class symbolic shape annotations to track dynamic shape computations globally across the program, enabling dynamic shape-aware cross-level optimizations. We build an end-to-end compilation framework using the proposed approach to optimize dynamic shape models. Experimental results on LLMs show that Relax delivers performance competitive with state-of-the-art systems across various GPUs and enables deployment of emerging models to a broader set of emerging environments, including mobile phones, embedded devices, and web browsers. © 2025 ACM.

关键词： program compilers

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Composing Distributed Computations Through Task and Kernel Fusion 25

Composing Distributed Computations Through Task and Kernel F...

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30th ACM International Conference on Architectural Support for programming Languages and Operating Systems, ASPLOS 2025

作者： Yadav, Rohan Sundram, Shiv Lee, Wonchan Garland, Michael Bauer, Michael Aiken, Alex Kjolstad, Fredrik Stanford University StanfordCA United States Nvidia Santa ClaraCA United States

ISBN: (纸本)9798400706981

We introduce Diffuse, a system that dynamically performs task and kernel fusion in distributed, task-based runtime systems. The key component of Diffuse is an intermediate representation of distributed computation that enables the necessary analyses for the fusion of distributed tasks to be performed in a scalable manner. We pair task fusion with a JIT compiler to fuse together the kernels within fused tasks. We show empirically that Diffuse's intermediate representation is general enough to be a target for two real-world, task-based libraries (cuPyNumeric and Legate Sparse), letting Diffuse find optimization opportunities across function and library boundaries. Diffuse accelerates unmodified applications developed by composing task-based libraries by 1.86x on average (geo-mean), and by between 0.93x - 10.7x on up to 128 GPUs. Diffuse also finds optimization opportunities missed by the original application developers, enabling high-level Python programs to match or exceed the performance of an explicitly parallel MPI library. © 2025 ACM.

关键词： program compilers

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accparser: A Standalone OpenACC Parser and Its Usage on Mapping OpenACC to OpenMP Directives 25th

accparser: A Standalone OpenACC Parser and Its Usage on Ma...

引用

25th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2024

作者： Yi, Xinyao Wang, Anjia Yan, Yonghong University of North Carolina at Charlotte CharlotteNC28262 United States Intel Corporation HillsboroOR97124 United States

ISBN: (纸本)9789819642069

Heterogeneous computing with accelerators has emerged as an effective approach to high-performance computing. Directive-based programming models such as OpenMP and OpenACC simplify parallel programming for GPU accelerators by enabling compilers to translate directive-annotated code into GPU-optimized code automatically. This paper presents accparser, a standalone and unified OpenACC parser built using ANTLR 4. Designed for both C/C++ and Fortran, accparser provides a complete grammar for OpenACC 3.3, making it a valuable tool for compiler developers who aim to implement OpenACC program transformations and code generation. It supports the syntax and semantic verification of OpenACC constructs and helps to interpret the OpenACC standard. Additionally, it can be leveraged as a tool to assist in creating compiler passes that convert OpenACC programs into OpenMP programs, enabling full utilization of OpenMP’s compiler optimizations and runtime support. The source code for accparser is available under the 2-Clause BSD License at https://***/passlab/accparser. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： program compilers

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Asdf: A Compiler for Qwerty, a Basis-Oriented Quantum programming Language 25

Asdf: A Compiler for Qwerty, a Basis-Oriented Quantum Progra...

引用

23rd ACM/IEEE International Symposium on Code Generation and Optimization, CGO 2025

作者： Adams, Austin J. Khan, Sharjeel Bhamra, Arjun S. Abusaada, Ryan R. Cabrera, Anthony M. Hoechst, Cameron C. Humble, Travis S. Young, Jeffrey S. Conte, Thomas M. Georgia Institute of Technology AtlantaGA United States Oak Ridge National Laboratory Oak RidgeTN United States

ISBN: (纸本)9798400712753

Qwerty is a high-level quantum programming language built on bases and functions rather than circuits. This new paradigm introduces new challenges in compilation, namely synthesizing circuits from basis translations and automatically specializing adjoint or predicated forms of functions. This paper presents Asdf, an open-source compiler for Qwerty that answers these challenges in compiling basis-oriented languages. Enabled with a novel high-level quantum IR implemented in the MLIR framework, our compiler produces OpenQASM 3 or QIR for either simulation or execution on hardware. Our compiler is evaluated by comparing the fault-tolerant resource requirements of generated circuits with other compilers, finding that Asdf produces circuits with comparable cost to prior circuit-oriented compilers. © 2025 Copyright held by the owner/author(s).

关键词： program compilers

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EFFACT: A Highly Efficient Full-Stack FHE Acceleration Platform 31

EFFACT: A Highly Efficient Full-Stack FHE Acceleration Platf...

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31st IEEE International Symposium on High Performance Computer Architecture, HPCA 2025

作者： Huang, Yi Gong, Xinsheng Kong, Xiangyu Chen, Dibei Zhu, Jianfeng Zhu, Wenping Li, Liangwei Gao, Mingyu Wei, Shaojun Zhang, Aoyang Liu, Leibo Tsinghua University China

ISBN: (纸本)9798331506476

Fully Homomorphic Encryption (FHE) is a set of powerful cryptographic schemes that allows computation to be performed directly on encrypted data with an unlimited depth. Despite FHE's promising in privacy-preserving computing, yet in most FHE schemes, ciphertext generally blows up thousands of times compared to the original message, and the massive amount of data load from off-chip memory for bootstrapping and privacy-preserving machine learning applications (such as HELR, ResNet-20), both degrade the performance of FHE-based computation. Several hardware designs have been proposed to address this issue, however, most of them require enormous resources and power. An acceleration platform with easy programmability, high efficiency, and low overhead is a prerequisite for practical application. This paper proposes EFFACT, a highly efficient full-stack FHE acceleration platform with a compiler that provides comprehensive optimizations and vector-friendly hardware. We start by examining the computational overhead across different real-world benchmarks to highlight the potential benefits of reallocating computing resources for efficiency enhancement. Then we make a design space exploration to find an optimal SRAM size with high utilization and low cost. On the other hand, EFFACT features a novel optimization named streaming memory access which is proposed to enable high throughput with limited SRAMs. Regarding the software-side optimization, we also propose a circuit-level function unit reuse scheme, to substantially reduce the computing resources without performance degradation. Moreover, we design novel NTT and automorphism units that are suitable for a cost-sensitive and highly efficient architecture, leading to low area. For generality, EFFACT is also equipped with an ISA and a compiler backend that can support several FHE schemes like CKKS, BGV, and BFV. We provide both FPGA and ASIC versions of EFFACT. On account of our full stack design, FPGA-EFFACT outperforms the

关键词： program compilers

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A Priori Loop Nest Normalization: Automatic Loop Scheduling in Complex Applications 25

A Priori Loop Nest Normalization: Automatic Loop Scheduling ...

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23rd ACM/IEEE International Symposium on Code Generation and Optimization, CGO 2025

作者： Trümper, Lukas Schaad, Philipp Ates, Berke Calotoiu, Alexandru Copik, Marcin Hoefler, Torsten Daisytuner Darmstadt Germany ETH Zurich Zurich Switzerland

ISBN: (纸本)9798400712753

The same computations are often expressed differently across software projects and programming languages. In particular, how computations involving loops are expressed varies due to the many possibilities to permute and compose loops. Since each variant may have unique performance properties, automatic approaches to loop scheduling must support many different optimization recipes. In this paper, we propose a priori loop nest normalization to align loop nests and reduce the variation before the optimization. Specifically, we define and apply normalization criteria, mapping loop nests with different memory access patterns to the same canonical form. Since the memory access pattern is susceptible to loop variations and critical for performance, this normalization allows many loop nests to be optimized by the same optimization recipe. To evaluate our approach, we apply the normalization with optimizations designed for only the canonical form, improving the performance of many different loop nest variants. Across multiple implementations of 15 benchmarks using different languages, we outperform a baseline compiler in C on average by a factor of 21.13, state-of-the-art auto-schedulers such as Polly and the Tiramisu auto-scheduler by 2.31 and 2.89, as well as performance-oriented Python-based frameworks such as NumPy, Numba, and DaCe by 9.04, 3.92, and 1.47. Furthermore, we apply the concept to the CLOUDSC cloud microphysics scheme, an actively used component of the Integrated Forecasting System, achieving a 10% speedup over the highly-tuned Fortran code. © 2025 Copyright held by the owner/author(s).

关键词： program compilers

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DialEgg: Dialect-Agnostic MLIR Optimizer using Equality Saturation with Egglog 25

DialEgg: Dialect-Agnostic MLIR Optimizer using Equality Satu...

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23rd ACM/IEEE International Symposium on Code Generation and Optimization, CGO 2025

作者： Zayed, Abd-El-Aziz Dubach, Christophe McGill University Montreal Canada McGill University Mila Montreal Canada

ISBN: (纸本)9798400712753

MLIR’s ability to optimize programs at multiple levels of abstraction is key to enabling domain-specific optimizing compilers. However, expressing optimizations remains tedious. Optimizations can interact in unexpected ways, making it hard to unleash full performance. Equality saturation promises to solve these challenges. First, it simplifies the expression of optimizations using rewrite rules. Secondly, it considers all possible optimization interactions, through saturation, selecting the best program variant. Despite these advantages, equality saturation remains absent from production compilers such as MLIR. This paper proposes to integrate Egglog, a recent equality saturation engine, with MLIR, in a dialect-agnostic manner. This paper shows how the main MLIR constructs such as operations, types or attributes can be modeled in Egglog. It also presents DialEgg, a tool that pre-defines a large set of common MLIR constructs in Egglog and automatically translates between the MLIR and Egglog program representations. This paper uses a few use cases to demonstrate the potential for combining equality saturation and MLIR. © 2025 Copyright held by the owner/author(s).

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