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*** — An MLIR-based Julia HLS Flow (Work in Progress)

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arXiv 2025年

作者： Short, Benedict McInerney, Ian Wickerson, John Imperial College London United Kingdom

Co-developing scientific algorithms and hardware accelerators requires domain-specific knowledge and large engineering resources. This leads to a slow development pace and high project complexity, which creates a barrier to entry that is too high for the majority of developers to overcome. We are developing a reusable end-to-end compiler toolchain for the Julia language entirely built on permissively-licensed open-source projects. This unifies accelerator and algorithm development by automatically synthesising Julia source code into high-performance Verilog. Copyright © 2025, The Authors. All rights reserved.

关键词： program compilers

Building Bridges: Julia as an MLIR Frontend

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arXiv 2025年

作者： Merckx, Jules

Driven by increasing compute requirements for deep learning models, compiler developers have been looking for ways to target specialised hardware and heterogeneous systems more efficiently. The MLIR project has the goal to offer infrastructure that can be used to develop new compilers and represent code at different levels of abstractions. While MLIR excels at offering developers a way to write new intermediate representations (IRs) and transformations, there is no easy way for end users to generate code in these IRs. In this work, we explore using the Julia programming language as a high-level input language for generating MLIR code. Most importantly, we focus on extensibility, allowing package developers to implement bindings to MLIR dialects in an intuitive and easy-to-use manner. By building on the Julia programming language, and its expressive features such as multiple dispatch and its extensible compiler, we design and implement a framework to generate MLIR code. Additionally, we evaluate this framework in three case studies. Ranging from developing a small domain specific language (DSL) for einsum expressions, to specifying transformations on MLIR code and programming kernels to be run on graphics processing units (GPUs). © 2025, CC BY.

关键词： program compilers

Beyond the Edge of Function: Unraveling the Patterns of Type Recovery in Binary Code

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arXiv 2025年

作者： Gangyang, L.I. Shang, Xiuwei Cheng, Shaoyin Zhang, Junqi Li, H.U. Xu, Z.H.U. Zhang, Weiming Nenghai, Y.U. University of Science and Technology of China Anhui China University of Science and Technology of China Anhui Province Key Laboratory of Digital Security Anhui China

Type recovery is a crucial step in binary code analysis, holding significant importance for reverse engineering and various security applications. Existing works typically simply target type identifiers within binary code and achieve type recovery by analyzing variable characteristics within functions. However, we find that the types in real-world binary programs are more complex and often follow specific distribution patterns. In this paper, to gain a profound understanding of the variable type recovery problem in binary code, we first conduct a comprehensive empirical study. We utilize the TYDA dataset, which includes 163,643 binary programs across four architectures and four compiler optimization options, fully reflecting the complexity and diversity of real-world programs. We carefully study the unique patterns that characterize types and variables in binary code, and also investigate the impact of compiler optimizations on them, yielding many valuable insights. Based on our empirical findings, we propose ByteTR, a framework for recovering variable types in binary code. We decouple the target type set to address the issue of unbalanced type distribution and perform static program analysis to tackle the impact of compiler optimizations on variable storage. In light of the ubiquity of variable propagation across functions observed in our study, ByteTR conducts inter-procedural analysis to trace variable propagation and employs a gated graph neural network to capture long-range data flow Copyright © 2025, The Authors. All rights reserved.

关键词： program compilers

Comparison of Vectorization Capabilities of Different compilers for X86 and ARM CPUs

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arXiv 2025年

作者： Sakib, Nazmus Prabhu, Tarun Santhi, Nandakishore Shalf, John Badawy, Abdel-Hameed A. Klipsch School of ECE New Mexico State University Las CrucesNM88003 United States Los Alamos National Laboratory Los AlamosNM87545 United States Lawrence Berkeley National Laboratory BerkeleyCA94720 United States

Most modern processors contain vector units that simultaneously perform the same arithmetic operation over multiple sets of operands. The ability of compilers to automatically vectorize code is critical to effectively using these units. Understanding this capability is important for anyone writing compute-intensive, high-performance, and portable code. We tested the ability of several compilers to vectorize code on x86 and ARM. We used the TSVC2 suite, with modifications that made it more representative of real-world code. On x86, GCC reported 54% of the loops in the suite as having been vectorized, ICX reported 50%, and Clang, 46%. On ARM, GCC reported 56% of the loops as having been vectorized, ACFL reported 54%, and Clang, 47%. We found that the vectorized code did not always outperform the unvectorized code. In some cases, given two very similar vectorizable loops, a compiler would vectorize one but not the other. We also report cases where a compiler vectorized a loop on only one of the two platforms. Based on our experiments, we cannot definitively say if any one compiler is significantly better than the others at vectorizing code on any given platform. Copyright © 2025, The Authors. All rights reserved.

关键词： program compilers

Bridging the PLC Binary Analysis Gap: A Cross-Compiler Dataset and Neural Framework for Industrial Control Systems

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arXiv 2025年

作者： Achamyeleh, Yonatan Gizachew Yu, Shih-Yuan Araya, Gustavo Quirós Al Faruque, Mohammad Abdullah University of California Irvine IrvineCA United States Siemens Technology PrincetonNJ United States

Industrial Control Systems (ICS) rely heavily on programmable Logic Controllers (PLCs) to manage critical infrastructure, yet analyzing PLC executables remains challenging due to diverse proprietary compilers and limited access to source code. To bridge this gap, we introduce PLC-BEAD, a comprehensive dataset containing 2431 compiled binaries from 700+ PLC programs across four major industrial compilers (CoDeSys, GEB, OpenPLC-V2, OpenPLC-V3). This novel dataset uniquely pairs each binary with its original Structured Text source code and standardized functionality labels, enabling both binary-level and source-level analysis. We demonstrate the dataset’s utility through PLCEmbed, a transformer-based framework for binary code analysis that achieves 93% accuracy in compiler provenance identification and 42% accuracy in fine-grained functionality classification across 22 industrial control categories. Through comprehensive ablation studies, we analyze how compiler optimization levels, code patterns, and class distributions influence model performance. We provide detailed documentation of the dataset creation process, labeling taxonomy, and benchmark protocols to ensure reproducibility. Both PLCBEAD and PLCEmbed are released as open-source resources to foster research in PLC security, reverse engineering, and ICS forensics, establishing new baselines for data-driven approaches to industrial cybersecurity. © 2025, CC BY.

关键词： program compilers

Accelerating Large Language Models through Partially Linear Feed-Forward Network

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arXiv 2025年

作者： Hu, Gansen Wang, Zhaoguo Wei, Jinglin Huang, Wei Chen, Haibo Institute of Parallel and Distributed Systems SEIEE Shanghai Jiao Tong University China

Large language models (LLMs) demonstrate remarkable capabilities but face deployment challenges due to their massive parameter counts. While existing compression techniques like pruning can reduce model size, it leads to significant accuracy degradation under high compression ratios. We present a novel perspective inspired by constant folding in compiler optimization. Our approach enables parameter reduction by treating activation functions in LLMs as linear functions. However, recent LLMs use complex non-linear activations like GELU that prevent direct application of this technique. We propose TARDIS, which enables optimization of LLMs with non-linear activations by partially approximating them with linear functions in frequently occurring input ranges. For outlier inputs, TARDIS employs an online predictor to dynamically fall back to original computations. Our experiments demonstrate that TARDIS achieves 80% parameter reduction in feed-forward networks, while significantly outperforming state-of-the-art pruning methods Wanda and RIA with up to 65% higher accuracy. In practical deployments for a 7B model, TARDIS achieves 1.6× end-to-end inference speedup when integrated with the vLLM serving system, and 1.4× speedup with the widely adopted HuggingFace implementation, while incurring only a 10.9% accuracy trade-off. © 2025, CC BY.

关键词： program compilers

Data-efficient Performance Modeling via Pre-training

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arXiv 2025年

作者： Liu, Chunting Baghdadi, Riyadh New York University Abu Dhabi Abu Dhabi United Arab Emirates

Performance models are essential for automatic code optimization, enabling compilers to predict the effects of code transformations on performance and guide search for optimal transformations. Building state-of-the-art performance models with deep learning, however, requires vast labeled datasets of random programs - an expensive and time-consuming process, stretching over months. This paper introduces a self-supervised pre-training scheme with autoencoders to reduce the need for labeled data. By pre-training on a large dataset of random programs, the autoencoder learns representations of code and transformations, which are then used to embed programs for the performance model. Implemented in the Tiramisu autoscheduler, our approach improves model accuracy with less data. For example, to achieve a MAPE of 20.72%, the original model requires 18 million data points, whereas our method achieves a similar MAPE of 22.44% with only 3.6 million data points, reducing data requirements by 5×. Copyright © 2025, The Authors. All rights reserved.

关键词： program compilers

TFHE-Coder: Evaluating LLM-agentic Fully Homomorphic Encryption Code Generation

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arXiv 2025年

作者： Kumar, Mayank Xue, Jiaqi Zheng, Mengxin Lou, Qian University of Central Florida United States

Fully Homomorphic Encryption over the torus (TFHE) enables computation on encrypted data without decryption, making it a cornerstone of secure and confidential computing. Despite its potential in privacy-preserving machine learning, secure multi-party computation, private blockchain transactions, and secure medical diagnostics, its adoption remains limited due to cryptographic complexity and usability challenges. While various TFHE libraries and compilers exist, practical code generation remains a hurdle. We propose a compiler-integrated framework to evaluate LLM inference and agentic optimization for TFHE code generation, focusing on logic gates and ReLU activation. Our methodology assesses error rates, compilability, and structural similarity across open and closed-source LLMs. Results highlight significant limitations in off-the-shelf models, while agentic optimizations—such as retrieval-augmented generation (RAG) and few-shot prompting—reduce errors and enhance code fidelity. This work establishes the first benchmark for TFHE code generation, demonstrating how LLMs, when augmented with domain-specific feedback, can bridge the expertise gap in FHE code generation. © 2025, CC BY-SA.

关键词： program compilers

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

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arXiv 2025年

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

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, CC BY.

关键词： program compilers