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检索条件"任意字段=Proceedings of Annual IEEE/ACM International Symposium on Code Generation and Optimization"

共 545 条记录，以下是1-10 订阅

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CGO 2025 - proceedings of the 23rd acm/ieee international symposium on code generation and optimization

CGO 2025 - Proceedings of the 23rd ACM/IEEE International Sy...

引用

23rd acm/ieee international symposium on code generation and optimization, CGO 2025

ISBN: (纸本)9798400712753

The proceedings contain 48 papers. The topics discussed include: synthesis of sorting kernels;tensorize: fast synthesis of tensor programs from legacy code using symbolic tracing, sketching and solving;enhancing deployment-time predictive model robustness for code analysis and optimization;pattern matching in ai compilers and its formalization;scalar interpolation: a better balance between vector and scalar execution for SuperScalar architectures;Vega: automatically generating compiler backends using a pre-trained transformer model;IntelliGen: instruction-level auto-tuning for tensor program with monotonic memory optimization;Calibro: compilation-assisted linking-time binary code outlining for code size reduction in android applications;and accelerating LLMs using an efficient GEMM Library and target-aware optimizations on real-world PIM devices.

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Enhancing Deployment-Time Predictive Model Robustness for code Analysis and optimization 25

Enhancing Deployment-Time Predictive Model Robustness for Co...

引用

23rd acm/ieee international symposium on code generation and optimization, CGO 2025

作者： Wang, Huanting Lenihan, Patrick Wang, Zheng School of Computer Science University of Leeds Leeds United Kingdom

ISBN: (纸本)9798400712753

Supervised machine learning techniques have shown promising results in code analysis and optimization problems. However, a learning-based solution can be brittle because minor changes in hardware or application workloads – such as facing a new CPU architecture or code pattern – may jeopardize decision accuracy, ultimately undermining model robustness. We introduce Prom, an open-source library to enhance the robustness and performance of predictive models against such changes during deployment. Prom achieves this by using statistical assessments to identify test samples prone to mispredictions and using feedback on these samples to improve a deployed model. We showcase Prom by applying it to 13 representative machine learning models across 5 code analysis and optimization tasks. Our extensive evaluation demonstrates that Prom can successfully identify an average of 96% (up to 100%) of mispredictions. By relabeling up to 5% of the Prom-identified samples through incremental learning, Prom can help a deployed model achieve a performance comparable to that attained during its model training phase. © 2025 Copyright held by the owner/author(s).

关键词： Reliability analysis

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Tensorize: Fast Synthesis of Tensor Programs from Legacy code using Symbolic Tracing, Sketching and Solving 25

Tensorize: Fast Synthesis of Tensor Programs from Legacy Cod...

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23rd acm/ieee international symposium on code generation and optimization, CGO 2025

作者： Brauckmann, Alexander Jaulmes, Luc de Souza Magalhães, José W. Polgreen, Elizabeth O’Boyle, Michael F.P. University of Edinburgh United Kingdom

ISBN: (纸本)9798400712753

Tensor domain specific languages (DSLs) achieve substantial performance due to high-level compiler optimization and hardware acceleration. However, to achieve such performance for existing applications requires the programmer to manual rewrite their legacy code in evolving Tensor DSLs. Prior efforts to automate this translation face significant scalability issues which greatly reduces their applicability to real-world code. This paper presents Tensorize, a novel MLIR-based compiler approach to automatically lift legacy code to high level Tensor DSLs using program synthesis. Tensorize uses a symbolic trace of the legacy program as a specification and automatically selects sketches from the target Tensor DSLs to drive the program synthesis. It uses an algebraic solver to rapidly simplify the specification, resulting in a fast, automatic approach that is correct by design. We evaluate Tensorize on several legacy code benchmarks and compare against state-of-the-art techniques. Tensorize is able to lift more code than prior schemes, is an order of magnitude faster in synthesis time, and guarantees correctness by construction. © 2025 Copyright held by the owner/author(s).

关键词： Program compilers

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Postiz: Extending Post-increment Addressing for Loop optimization and code Size Reduction 25

Postiz: Extending Post-increment Addressing for Loop Optimiz...

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23rd acm/ieee international symposium on code generation and optimization, CGO 2025

作者： Fan, Enming Guan, Xiaofeng Hu, Fan Shi, Heng Zhou, Hao Yao, Jianguo Shanghai Enflame Technology Co. Ltd. Shanghai China Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9798400712753

Memory access instructions with auto-addressing modes are prevalent in various Instruction Set Architectures (ISAs), yet their use in compilers remains limited. Existing methods address code optimization in one of two ways: they either focus on reducing code size, but are constrained to basic block-level optimizations and may not fully exploit architectural benefits, or they optimize loop performance, often neglecting the advantages of post-increment instructions and focusing primarily on innermost loops while leaving outer loops unoptimized. To address these shortcomings and meet the needs of real-world Machine Learning (ML) applications, we introduce Postiz, a novel post-increment loop optimization technique. Postiz extends post-increment optimizations beyond traditional limits, incorporating enhancements for inner loops, cross-loop regions, and nested loop structures. Through a profitability analysis, Postiz optimizes code judiciously, leveraging architectural advantages and reducing code size without compromising improvement made by other optimizations. Our experiments show that Postiz is effective, achieving an optimization coverage of 98.04% on MobileNet and BERT benchmarks. In comparison to default LLVM optimization, Postiz generates approximately four times more post-increment instructions. Moreover, it reduces code size by an average of 9.45% across various platforms. These improvements represent significant advancements over current methods, showcasing Postiz’s potential to enhance compiler optimizations in a meaningful way. © 2025 Copyright held by the owner/author(s).

关键词： Program compilers

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Proteus: Portable Runtime optimization of GPU Kernel Execution with Just-in-Time Compilation 25

Proteus: Portable Runtime Optimization of GPU Kernel Executi...

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23rd acm/ieee international symposium on code generation and optimization, CGO 2025

作者： Georgakoudis, Giorgis Parasyris, Konstantinos Beckingsale, David Lawrence Livermore National Laboratory Livermore United States

ISBN: (纸本)9798400712753

In High-performance computing (HPC) fast application execution is the primary objective. HPC software is written in high-performance languages (C/C++, Fortran) and is statically compiled Ahead-of-Time (AOT) using optimizing compilers to generate fast code. AOT compilation optimizes source code with only limited information available at compile time, which precludes possible optimization leveraging runtime information. We propose Proteus, an easy-to-use, portable, and lightweight Just-In-Time (JIT) compilation approach to optimize GPU kernels at runtime. Proteus dynamically extracts, compiles, and optimizes language-agnostic LLVM IR to reduce compilation overhead while enhancing portability and versatility compared to language-specific solutions. Using a minimally intrusive annotation-based interface, Proteus specializes GPU kernels for input arguments and launch parameters. Evaluation on a diverse set of programs on AMD and NVIDIA GPUs shows that Proteus achieves significant end-to-end speedup, up to 2.8× for AMD and 1.78× on NVIDIA, over AOT optimization, while outperforming CUDA-specific Jitify with an average 1.23× speedup, thanks to reduced overhead and faster binary code in certain cases. © 2025 Copyright held by the owner/author(s).

关键词： FORTRAN (programming language)

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IntelliGen: Instruction-Level Auto-tuning for Tensor Program with Monotonic Memory optimization 25

IntelliGen: Instruction-Level Auto-tuning for Tensor Program...

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23rd acm/ieee international symposium on code generation and optimization, CGO 2025

作者： Ma, Zixuan Wang, Haojie Xing, Jingze Huang, Shuhong Zheng, Liyan Zhang, Chen Cao, Huanqi Huang, Kezhao Zhai, Mingshu Tang, Shizhi Wang, Penghan Zhai, Jidong Tsinghua University China Qingcheng.AI China

ISBN: (纸本)9798400712753

Tensor compilers play a critical role in optimizing deep neural networks (DNNs), with memory performance emerging as a key bottleneck in code generation for DNN models. Existing tensor compilers are constrained by inefficient auto-tuning algorithms. They either must deploy coarse-grained descriptions, thus miss potential optimization, or struggle with vast search spaces, rendering auto-tuning inapplicable. Tensor compilers require a more holistic optimization of memory performance to overcome these constraints. To address this issue, we focus our optimization objective on memory performance, which allows us to design monotonic optimization methods, significantly enhancing the efficiency of auto-tuning and thus enabling auto-tuning on a fine-granularity description. Based on these observations, we propose IntelliGen, a tensor compiler with instruction-level auto-tuning and monotonic memory optimization. We design an instruction-level graph description, iGraph, and a monotonic optimization method for optimization on iGraph. Benefiting from auto-tuning techniques with fine-grained description, IntelliGen demonstrates significant speedup of up to 3.13×, 3.55×, and 16.9× (averaging 1.46×, 1.85×, and 2.30×, respectively) on NVIDIA GPUs, AMD GPUs, and Cambricon MLUs over the most efficient existing frameworks. © 2025 Copyright held by the owner/author(s).

关键词： Tensors

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Calibro: Compilation-Assisted Linking-Time Binary code Outlining for code Size Reduction in Android Applications 25

Calibro: Compilation-Assisted Linking-Time Binary Code Outli...

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23rd acm/ieee international symposium on code generation and optimization, CGO 2025

作者： Liang, Zhanhao Sun, Hanming Shang, Wenhan Yuan, Mengting Fu, Jingqin Ma, Jiang Xue, Chun Jason Li, Qingan Wuhan University Wuhan China Wuhan Broadcasting and Television Station Wuhan China Guangdong OPPO Mobile Telecommunications Crop. Ltd. Dongguan China MBZUAI Masdar United Arab Emirates

ISBN: (纸本)9798400712753

Recent Android systems have employed pre-compilation technology to boost app launch speed and runtime performance. However, this generates large OAT files that over-consume scarce memory and storage resources in mobile devices. This paper conducts an evaluation of code redundancy in popular production android applications and observes that the code redundancy is up to 25%. To reduce the code size via redundancy elimination, this paper proposes Calibro, a compilation-assisted linking-time binary code outlining method. Calibro consists of two parts, the Compilation-Time code Outlining (CTO) and the Linking-Time Binary code Outlining (LTBO) with information collected at compilation-time. Additionally, a paralleled suffix tree method is proposed to reduce the building time overhead, and a hot function filtering method is proposed to effectively mitigate run-time performance degradation caused by code outlining. Experimental results show that compared to the baseline (the original AOSP version with all available code size optimization enabled), the proposed approach reduces code size in Android applications by more than 15.19% on average, with negligible runtime performance degradation and tolerable building time overhead. Hence the proposed code outlining approach is promising for production deployment. © 2025 Copyright held by the owner/author(s).

关键词： Binary codes

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code generation for Cryptographic Kernels using Multi-word Modular Arithmetic on GPU 25

Code Generation for Cryptographic Kernels using Multi-word M...

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23rd acm/ieee international symposium on code generation and optimization, CGO 2025

作者： Zhang, Naifeng Franchetti, Franz Carnegie Mellon University Pittsburgh United States

ISBN: (纸本)9798400712753

Fully homomorphic encryption (FHE) and zero-knowledge proofs (ZKPs) are emerging as solutions for data security in distributed environments. However, the widespread adoption of these encryption techniques is hindered by their significant computational overhead, primarily resulting from core cryptographic operations that involve large integer arithmetic. This paper presents a formalization of multi-word modular arithmetic (MoMA), which breaks down large bit-width integer arithmetic into operations on machine words. We further develop a rewrite system that implements MoMA through recursive rewriting of data types, designed for compatibility with compiler infrastructures and code generators. We evaluate MoMA by generating cryptographic kernels, including basic linear algebra subprogram (BLAS) operations and the number theoretic transform (NTT), targeting various GPUs. Our MoMA-based BLAS operations outperform state-of-the-art multi-precision libraries by orders of magnitude, and MoMA-based NTTs achieve near-ASIC performance on commodity GPUs. © 2025 Copyright held by the owner/author(s).

关键词： Number theory

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Qubit Movement-Optimized Program generation on Zoned Neutral Atom Processors 25

Qubit Movement-Optimized Program Generation on Zoned Neutral...

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23rd acm/ieee international symposium on code generation and optimization, CGO 2025

作者： Jang, Enhyeok Kim, Youngmin Kim, Hyungseok Choi, Seungwoo Huang, Yipeng Ro, Won Woo Yonsei University Seoul Korea Republic of Rutgers University New Brunswick United States

ISBN: (纸本)9798400712753

A zoned neutral atom architecture achieves exceptional fidelity by segregating the execution spaces of 1- and 2-qubit gates, being a promising candidate for high-accuracy quantum systems. Unfortunately, naïvely applying programs designed for static qubit topologies to zoned architectures may result in most execution time being consumed by intra-zone travels of atoms. To address this, we introduce Mantra (Minimizing trAp movemeNts for aTom aRray Architectures), which rewrites quantum programs to reduce the interleaving of single- and two-qubit gates. Mantra incorporates three strategies: (i) a fountain-shaped controlled-Z (CZ) chain, (ii) ZZ-interaction protocol without a 1-qubit gate, and (iii) preemptive gate scheduling. Mantra reduces inter-zone movements by 68%, physical gate counts by 35%, and improves circuit fidelities by 17% compared to the standard executions. © 2025 Copyright held by the owner/author(s).

关键词： Qubits

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MTE4JNI: A Memory Tagging Method to Protect Java Heap Memory from Illicit Native code Access 25

MTE4JNI: A Memory Tagging Method to Protect Java Heap Memory...

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23rd acm/ieee international symposium on code generation and optimization, CGO 2025

作者： Chen, Huinan Ma, Jiang Xue, Chun Jason Li, Qingan Wuhan University Wuhan China Guangdong OPPO Mobile Telecommunications Corp. Ltd. Dongguan China MBZUAI Masdar United Arab Emirates

ISBN: (纸本)9798400712753

With the proliferation of mobile devices in daily life, ensuring the security and performance of these devices has become crucial. On Android, the Java Native Interface (JNI) acts as a bridge, allowing native libraries to directly access Java heap memory via raw pointers, bypassing Java’s built-in safety checks. While this offers powerful functionality and performance, it also threatens the memory safety of the Java heap. Recently, Memory Tagging Extension (MTE) is introduced into the ARM architectures to enhance memory safety, reducing software vulnerabilities caused by illegal memory operations. This paper proposes MTE4JNI, an MTE-based JNI checking method, to protect Java heap memory from illicit native code access. Experimental results on real Android devices demonstrate that, compared to the currently employed guarded copy method, the proposed MTE4JNI method provides superior memory safety protection, while significantly reducing the runtime overhead on average by 11x and 27x for single-threaded and multi-threaded environments, respectively. © 2025 Copyright held by the owner/author(s).

关键词： Java programming language

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