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检索条件"主题词=Program compilers"

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Combining MLIR Dialects with Domain-Specific Architecture for Efficient Regular Expression Matching 25

Combining MLIR Dialects with Domain-Specific Architecture fo...

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

作者： Somaini, Andrea Carloni, Filippo Agosta, Giovanni Santambrogio, Marco D. Conficconi, Davide Politecnico di Milano Milan Italy

ISBN: (纸本)9798400712753

Pattern matching based on Regular Expressions (REs) is a pervasive and challenging computational kernel used in several applications to identify critical information in a data stream. Due to the sequential data dependency of REs and the increasing data volume growth, hardware acceleration is gaining attention to address the limitation of general-purpose architectures. RE-oriented Domain-Specific Architectures (DSAs) combine the flexibility of translating REs into binary code with the efficiency of a specialized architecture, filling the gap between frozen hardware accelerators and the versatility of CPUs/GPUs. However, existing DSAs focus mainly on the efficiency execution challenge while missing the optimization opportunities that a structured compilation infrastructure can provide. This paper proposes a RE-tailored multi-level intermediate representation strategy embodied by the MLIR framework at the compiler level to exploit different abstraction optimizations via two domain-specific dialects, one targeting the abstract representation of REs and the other targeting the underlying domain-specific ISA. Moreover, this paper proposes a novel architectural organization of an open-source state-of-the-art DSA to maximize the parallelization capabilities. Overall, the proposed approach significantly improves execution time by up to 2.26×, energy efficiency by up to 2.30×, and resource usage. © 2025 Copyright held by the owner/author(s).

关键词： program compilers

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An Operational Semantics for Yul 22nd

An Operational Semantics for Yul

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22nd International Conference on Software Engineering and Formal Methods, SEFM 2024

作者： Koutavas, Vasileios Lin, Yu-Yang Tzevelekos, Nikos Trinity College Dublin Dublin Ireland Queen Mary University of London London United Kingdom Lero - Science Foundation Ireland Research Centre for Software Limerick Ireland

ISBN: (纸本)9783031773815

We present a big-step and small-step operational semantics for Yul—the intermediate language used by the Solidity compiler to produce EVM bytecode—in a mathematical notation that is congruous with the literature of programming languages, lends itself to language proofs, and can serve as a precise, widely accessible specification for the language. Our two semantics stay faithful to the original, informal specification of the language but also clarify under-specified cases such as void function calls. Our presentation allows us to prove the equivalence between the two semantics. We also implement the small-step semantics in an interpreter for Yul which avails of optimisations that are provably correct. We have tested the interpreter using tests from the Solidity compiler and our own. We envisage that this work will enable the development of verification and symbolic execution technology directly in Yul, contributing to the Ethereum security ecosystem, as well as aid the development of a provably sound future type system. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： program compilers

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Faster Chaitin-like Register Allocation via Grammatical Decompositions of Control-Flow Graphs 25

Faster Chaitin-like Register Allocation via Grammatical Deco...

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

作者： Cai, Xuran Goharshady, Amir Kafshdar Hitarth, S. Lam, Chun Kit Hong Kong University of Science and Technology Clear Water Bay New Territories Hong Kong University of Oxford Oxford United Kingdom

ISBN: (纸本)9798400706981

It is well-known that control-flow graphs (CFGs) of structured programs are sparse. This sparsity has been previously formalized in terms of graph parameters such as treewidth and pathwidth and used to design faster parameterized algorithms for numerous compiler optimization, model checking and program analysis tasks. In this work, we observe that the known graph sparsity parameters fail to exactly capture the kind of sparsity exhibited by CFGs. For example, while all structured CFGs have a treewidth of at most 7, not every graph with a treewidth of 7 or less is realizable as a CFG. As a result, current parameterized algorithms are solving the underlying graph problems over a more general family of graphs than the CFGs. To address this problem, we design a new but natural concept of graph decomposition based on a grammar that precisely captures the set of graphs that can be realized as CFGs of programs. We show that our notion of decomposition enables the same type of dynamic programming algorithms that are often used in treewidth/pathwidth-based methods. As two concrete applications, using our grammatical decomposition of CFGs, we provide asymptotically more efficient algorithms for two variants of the classical problem of register allocation as defined by Chaitin, i.e. assigning program variables to a limited number of registers such that variables with intersecting lifetimes are not assigned to the same register. Note that Chaitin's formulation of register allocation does not allow live-range splitting. Our algorithms are asymptotically faster not only in comparison with the non-parameterized solutions for these problems, but also compared to the state-of-the-art treewidth/pathwidth-based approaches in the literature. For minimum-cost register allocation over a fixed number of registers, we provide an algorithm with a runtime of O(|G| ·|V|5.r) where |G| is the size of the program, is the set of program variables and r is the number of registers. In contrast, the

关键词： program compilers

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CROSS: Compiler-Driven Optimization of Sparse DNNs Using Sparse/Dense Computation Kernels 31

CROSS: Compiler-Driven Optimization of Sparse DNNs Using Spa...

引用

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

作者： Liu, Fangxin Huang, Shiyuan Yang, Ning Wang, Zongwu Li, Haomin Jiang, Li Shanghai Jiao Tong University Department of Computer Science and Engineering China Shanghai Qi Zhi Institute China

ISBN: (纸本)9798331506476

As deep learning models continue to grow larger and more complex, exploiting sparsity is becoming one of the most critical areas for enhancing efficiency and scalability. Several methods for leveraging sparsity have been proposed to more effectively balance the trade-off between compression ratio and accuracy. While these methods offer algorithmic advantages, they also introduce significant hardware overhead due to index-based encoding and decoding. In this paper, we propose CROSS, an end-to-end compilation optimization technique to achieve sparse DNN acceleration using GPU computation kernels. The key insight behind CROSS is to exploit parameter distribution locality and reconcile the 'sparse' DNN computation with the high-performance 'dense' computation kernels. Specifically, we perform an in-depth analysis of sparse operations in mainstream DNN computing frameworks. We then decompose the sparse workload into multiple components to create highly efficient, specialized operators with different sparsity levels. Additionally, we introduce a novel sparse graph translation technique to facilitate computation kernel processing of the sparse workload. The resulting CROSS framework can accommodate various sparsity patterns and optimization techniques, delivering an average 2.03 × speedup on inference latency compared to seven state-of-the-art solutions with smaller memory footprints across various models and datasets. © 2025 IEEE.

关键词： program compilers

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PFHaD: Page Fault Handler Assisted Defense Against Data-Oriented Attacks 3rd

PFHaD: Page Fault Handler Assisted Defense Against Data-Orie...

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3rd International Conference on Advances in Computing Research, ACR 2025

作者： Zhang, Tianning Huang, Hao School of Computer Jiangsu University of Science and Technology Zhenjiang China Department of Computer Science and Technology Nanjing University Nanjing China

ISBN: (纸本)9783031876462

As memory corruption vulnerabilities evolve, attackers have shifted focus from traditional control-flow attacks to non-control data attacks, which manipulate data influencing a program′s behavior without altering its control flow. This shift bypasses conventional defenses like Control Flow Integrity (CFI), posing new security risks. In response, we propose a novel defense mechanism called Page Fault Handler-assisted Defense (PFHaD). PFHaD leverages hardware page faults to protect critical data in memory, providing robust defense against non-control data attacks with minimal performance overhead. Unlike traditional defenses, PFHaD also prevents information leakage by making critical data pages inaccessible. The solution does not require modifications to hardware, compilers, or operating systems, making it ideal for commercial-off-the-shelf (COTS) software. Experimental results demonstrate that PFHaD effectively mitigates a wide range of non-control data attacks while maintaining low performance impact, offering a scalable and efficient solution to modern memory corruption threats. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

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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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...

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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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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

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