arXiv subjects

Liutong Han

Publications and source records attributed to Liutong Han.

3 recordsLinked to original sources

arXiv2025

VecIntrinBench: Benchmarking Cross-Architecture Intrinsic Code Migration for RISC-V Vector

Intrinsic functions are specialized functions provided by the compiler that efficiently operate on architecture-specific hardware, allowing programmers to write optimized code in a high-level language that fully exploits hardware features. Using intrinsics to vectorize core code blocks is a standard optimization method in high-performance libraries, often requiring specific vector optimization implementations for multiple mainstream architectures. The promising RISC-V software ecosystem has a significant demand for algorithm library migration and adaptation. Translating existing intrinsic functions to RISC-V Vector (RVV) intrinsic functions across architectures is currently a mainstream approach. Rule-based intrinsic mapping methods and LLM-based code generation can help developers address the code migration challenge. However, existing intrinsic code benchmarks focus on mainstream SIMD intrinsics and lack support for the emerging RISC-V architecture. There is currently no benchmark that comprehensively evaluates the intrinsic migration capabilities for the RVV extension. To fill this gap, we propose VecIntrinBench, the first intrinsic benchmark encompassing RVV extensions. It includes 50 function-level tasks from open source repositories, implemented as scalars, RVV intrinsics, Arm Neon intrinsics, and x86 intrinsics, along with comprehensive functional and performance test cases. We systematically evaluated various code migration approaches on VecIntrinBench, yielding a series of insightful findings. The results demonstrate that advanced Large Language Models (LLMs) achieve a similar effect as rule-based mapping approaches for RISC-V code migration, while also delivering superior performance. We further analyze the reasons and identify future directions for LLM development in the code migration field. The VecIntrinBench is open-sourced to benefit the broader community and developers.

cs.SE↗

arXiv2025

IntrinTrans: LLM-based Intrinsic Code Translator for RISC-V Vector

The use of intrinsic functions to leverage hardware-specific capabilities is a crucial approach for optimizing library performance. Many mainstream libraries implement a large number of vectorized algorithms on Arm or x86 SIMD (Single-Instruction, Multiple-Data) intrinsic functions. Translating existing vectorized intrinsic code into the intrinsics of an emerging architecture is a practical and effective approach. However, current cross-architecture translation largely relies on manual rewriting or rule-based mapping methods, which are both time-consuming and prone to errors. We present \texttt{IntrinTrans}, a LLM-based agent that utilizes compile-and-test feedback to translate intrinsic code across architectures automatically, and further optimizes the generated intrinsics using register-usage information derived from liveness analysis. To evaluate the effectiveness of our method, we used \texttt{IntrinTrans} to translate the open-source benchmark from Arm Neon Intrinsic to the emerging RISC-V Vector (RVV) Intrinsic implementation and compared its performance with that of the native RVV implementation. Our experiments show that advanced LLMs can generate semantically correct RVV Intrinsic functions with only a finite number of iterations. Depending on the base LLMs, the pass rate ranges from 47% to 100%, achieving performance similar to the native implementation (0.85x to 1.28x).

cs.SE↗

arXiv2024

A Method for Efficient Heterogeneous Parallel Compilation: A Cryptography Case Study

In the era of diminishing returns from Moores Law, heterogeneous computing systems have emerged as a vital approach to enhance computational efficiency. This paper introduces a novel MLIR-based dialect, named hyper, designed to optimize data management and parallel computation across diverse hardware architectures. The hyper dialect abstracts the complexities of heterogeneous computing by providing a unified compilation framework that efficiently schedules tasks and manages data communication. To demonstrate its capabilities, we present HETOCompiler, a cryptography-focused compiler prototype that implements multiple hash algorithms and enables their execution on heterogeneous systems. The proposed approach achieves performance improvements over existing programming models for heterogeneous computing (OpenCL), offering an average speedup of 1.93x, 1.18x, and 1.12x for SHA-1, MD5, and SM3 algorithms, respectively. Our findings highlight the potential of the hyper dialect in harnessing the full computational power of heterogeneous devices, advancing the field of compiler design for heterogeneous systems.

cs.CR↗