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

• URL: http://arxiv.org/abs/2510.10119v1
• Date: Sat, 11 Oct 2025 08:52:01 GMT
• Title: IntrinTrans: LLM-based Intrinsic Code Translator for RISC-V Vector
• Authors: Liutong Han, Zhiyuan Tan, Hongbin Zhang, Pengcheng Wang, Chu Kang, Mingjie Xing, Yanjun Wu,
• Abstract summary: Translating existing vectorized intrinsic code onto RVV intrinsics is a practical and effective approach.<n>Current cross-architecture translation largely relies on manual rewriting, which is time-consuming and error-prone.<n>We present IntrinTrans, a multi-agent approach that utilizes compile-and-test feedback to translate intrinsic code across architectures automatically.
• Score: 9.678932711610244
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The use of intrinsic functions to exploit hardware-specific capabilities is an important approach for optimizing library performance. Many mainstream libraries implement a large number of vectorized algorithms on Arm or x86 SIMD intrinsic functions. With the rapid expansion of the RISC-V hardware-software ecosystem, there is a growing demand for support of the RISC-V Vector (RVV) extension. Translating existing vectorized intrinsic code onto RVV intrinsics is a practical and effective approach. However, current cross-architecture translation largely relies on manual rewriting, which is time-consuming and error-prone. Furthermore, while some rule-based methods can reduce the need for manual intervention, their translation success rate is limited by incomplete rule coverage and syntactic constraints, and the performance suffers from inadequate utilization of RVV-specific features. We present IntrinTrans, a LLM-based multi-agent approach that utilizes compile-and-test feedback to translate intrinsic code across architectures automatically, and further optimizes the generated RVV intrinsics using register-usage information derived from liveness analysis. To evaluate the effectiveness of our approach, we collected 34 vectorized algorithm cases from open-source libraries. Each case includes an Arm Neon intrinsics implementation and a RVV intrinsics implementation contributed by the open-source community, together with correctness and performance tests. Our experiments show that advanced LLMs produce semantically correct RISC-V Vector intrinsics in most cases within a limited number of iterations, and in some cases achieve up to 5.93x the performance of the native implementation from the open-source community.

Related papers

• Prism: Efficient Test-Time Scaling via Hierarchical Search and Self-Verification for Discrete Diffusion Language Models [96.0074341403456]
  Inference-time compute has re-emerged as a practical way to improve LLM reasoning.<n>Most test-time scaling (TTS) algorithms rely on autoregressive decoding.<n>We propose Prism, an efficient TTS framework for dLLMs.
  arXiv  Detail & Related papers  (2026-02-02T09:14:51Z)
• VecIntrinBench: Benchmarking Cross-Architecture Intrinsic Code Migration for RISC-V Vector [8.59222474360646]
  Translating intrinsic functions to RISC-V Vector (RVV) intrinsic functions across architectures is currently a mainstream approach.<n>There is currently no benchmark that comprehensively evaluates the intrinsic migration capabilities for the RVV extension.<n>We propose VecIntrinBench, the first intrinsic benchmark encompassing RVV extensions.
  arXiv  Detail & Related papers  (2025-11-24T08:11:10Z)
• Run, Ruminate, and Regulate: A Dual-process Thinking System for Vision-and-Language Navigation [52.11339614452127]
  Vision-and-Language Navigation (VLN) requires an agent to dynamically explore complex 3D environments following human instructions.<n>Recent research underscores the potential of harnessing large language models (LLMs) for VLN, given their commonsense knowledge and general reasoning capabilities.<n>We propose a novel dual-process thinking framework dubbed R3, integrating LLMs' generalization capabilities with VLN-specific expertise in a zero-shot manner.
  arXiv  Detail & Related papers  (2025-11-18T04:32:00Z)
• QiMeng-NeuComBack: Self-Evolving Translation from IR to Assembly Code [52.66657751895655]
  Large Language Models (LLMs) offer a compelling new paradigm: Neural Compilation.<n>This paper introduces NeuComBack, a novel benchmark dataset specifically designed for IR-to-assembly compilation.<n>We propose a self-evolving prompt optimization method that enables LLMs to evolve their internal prompt strategies.
  arXiv  Detail & Related papers  (2025-11-03T03:20:26Z)
• Sample-Efficient Online Learning in LM Agents via Hindsight Trajectory Rewriting [92.57796055887995]
  We introduce ECHO, a prompting framework that adapts hindsight experience replay from reinforcement learning for language model agents.<n> ECHO generates optimized trajectories for alternative goals that could have been achieved during failed attempts.<n>We evaluate ECHO on stateful versions of XMiniGrid, a text-based navigation and planning benchmark, and PeopleJoinQA, a collaborative information-gathering enterprise simulation.
  arXiv  Detail & Related papers  (2025-10-11T18:11:09Z)
• Retrofitting Control Flow Graphs in LLVM IR for Auto Vectorization [0.14323566945483493]
  We introduce a novel vectorization pipeline featuring two specialized IR extensions: SIR, which encodes high-level structural information, and VIR, which explicitly represents dependencies through data dependency analysis.<n>Our proposed vectorization pipeline achieves significant performance improvements, delivering speedups of up to 53% and 58% compared to LLVM and GCC, respectively.
  arXiv  Detail & Related papers  (2025-10-06T15:11:41Z)
• VerlTool: Towards Holistic Agentic Reinforcement Learning with Tool Use [78.29315418819074]
  We introduce VerlTool, a unified and modular framework that addresses limitations through systematic design principles.<n>Our framework formalizes ARLT as multi-turn trajectories with multi-modal observation tokens (text/image/video), extending beyond single-turn RLVR paradigms.<n>The modular plugin architecture enables rapid tool integration requiring only lightweight Python definitions.
  arXiv  Detail & Related papers  (2025-09-01T01:45:18Z)
• SimdBench: Benchmarking Large Language Models for SIMD-Intrinsic Code Generation [7.839161849517216]
  Large Language Models show promise in assisting programmers with the challenges of SIMD intrinsic programming.<n>Existing code-generation benchmarks focus on only scalar code, and it is unclear how LLMs perform in generating vectorized code using SIMD intrinsics.<n>We propose SimdBench, the first code benchmark specifically designed for SIMD-intrinsic code generation.
  arXiv  Detail & Related papers  (2025-07-21T03:55:41Z)
• Tensor Program Optimization for the RISC-V Vector Extension Using Probabilistic Programs [0.6242215470795112]
  We present a workflow based on the TVM compiler to efficiently map AI workloads onto RISC-V vector units.<n>Our proposal shows a mean improvement of 46% in execution latency when compared against the autovectorization feature of GCC.<n>We open-sourced our proposal for the community to expand it to target other RISC-V extensions.
  arXiv  Detail & Related papers  (2025-07-02T08:15:33Z)
• Beyond the Edge of Function: Unraveling the Patterns of Type Recovery in Binary Code [55.493408628371235]
  We propose ByteTR, a framework for recovering variable types in binary code.<n>In light of the ubiquity of variable propagation across functions, ByteTR conducts inter-procedural analysis to trace variable propagation and employs a gated graph neural network to capture long-range data flow dependencies for variable type recovery.
  arXiv  Detail & Related papers  (2025-03-10T12:27:05Z)
• LLM-Vectorizer: LLM-based Verified Loop Vectorizer [12.048697450464935]
  Large-language models (LLMs) can generate vectorized code from scalar programs that process individual array elements. LLMs are capable of producing high performance vectorized code with run-time speedup ranging from 1.1x to 9.4x. Our approach is able to verify 38.2% of vectorizations as correct on the TSVC benchmark dataset.
  arXiv  Detail & Related papers  (2024-06-07T07:04:26Z)
• QTRAN++: Improved Value Transformation for Cooperative Multi-Agent Reinforcement Learning [70.382101956278]
  QTRAN is a reinforcement learning algorithm capable of learning the largest class of joint-action value functions. Despite its strong theoretical guarantee, it has shown poor empirical performance in complex environments. We propose a substantially improved version, coined QTRAN++.
  arXiv  Detail & Related papers  (2020-06-22T05:08:36Z)
• PolyDL: Polyhedral Optimizations for Creation of High Performance DL primitives [55.79741270235602]
  We present compiler algorithms to automatically generate high performance implementations of Deep Learning primitives. We develop novel data reuse analysis algorithms using the polyhedral model. We also show that such a hybrid compiler plus a minimal library-use approach results in state-of-the-art performance.
  arXiv  Detail & Related papers  (2020-06-02T06:44:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.