ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

• URL: http://arxiv.org/abs/2601.04327v1
• Date: Wed, 07 Jan 2026 19:04:53 GMT
• Title: ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation
• Abstract summary: ParaCodex is an HPC-engineer workflow that turns a Codex-based agent into an autonomous OpenMP offload system.<n>We evaluate translation from serial CPU kernels to OpenMP offload kernels on HeCBench, Rodinia, and NAS.<n>We achieve geometric-mean speedups of 3x on HeCBench and 5x on Rodinia, and outperforming a zero-shot Codex baseline on all suites.
• Score: 5.103450268268085
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Parallel programming is central to HPC and AI, but producing code that is correct and fast remains challenging, especially for OpenMP GPU offload, where data movement and tuning dominate. Autonomous coding agents can compile, test, and profile on target hardware, but outputs are brittle without domain scaffolding. We present ParaCodex, an HPC-engineer workflow that turns a Codex-based agent into an autonomous OpenMP GPU offload system using staged hotspot analysis, explicit data planning, correctness gating, and profiling-guided refinement. We evaluate translation from serial CPU kernels to OpenMP GPU offload kernels on HeCBench, Rodinia, and NAS. After excluding five kernels, ParaCodex succeeded on all 31 valid kernels. The generated kernels improved GPU time over reference OpenMP implementations in 25/31 cases, achieving geometric-mean speedups of 3x on HeCBench and 5x on Rodinia, and outperforming a zero-shot Codex baseline on all suites. We also evaluate CUDA to OpenMP offload translation on ParEval, where ParaCodex maintains high compilation and validation rates in code-only and end-to-end settings.

Related papers

• AsmEvo: Agentic Assembly-Level Optimization of AMD GPU Kernels with Functional Equivalence Verification [21.700338424307517]
  We present AsmEvo, an agentic assembly-levelaware for AMD GPU kernels.<n>AsmEvo combines code-object recovery, metadata rebuilding, profiling-guided hot-window editing, correctness-gated timing, and patch fallback.<n>On MI308X, AsmEvo improves 29 of 30 selectedBench kernels, reaching 1.35x-mean and 3.88x maximum speedup.
  arXiv  Detail & Related papers  (2026-08-21T03:34:22Z)
• Are LLM-Generated GPU Kernels Production-Ready? A Trace-Driven Benchmark and Optimization Agent [10.241408048987038]
  Existing GPU kernel generation benchmarks draw problems from synthetic or curated sources that diverge from deployed workloads.<n>We present Atrex-Bench, a benchmark whose 30 operators and 440 shapes are sampled directly from full-cluster production inference traces of compute-limited, memory-rich GPUs.
  arXiv  Detail & Related papers  (2026-07-16T03:49:50Z)
• Kernel-Smith: A Unified Recipe for Evolutionary Kernel Optimization [48.656549870801285]
  Kernel-Smith is a framework for high-performance GPU kernel and operator generation.<n>On the agent side, Kernel-Smith maintains a population of executable candidates and iteratively improves them.<n>On the training side, we convert long-horizon evolution trajectories into step-centric supervision and reinforcement learning signals.
  arXiv  Detail & Related papers  (2026-03-30T12:12:49Z)
• StitchCUDA: An Automated Multi-Agents End-to-End GPU Programing Framework with Rubric-based Agentic Reinforcement Learning [26.264303471292845]
  We propose StitchCUDA, a multi-agent framework for end-to-end GPU program generation.<n> Experiments show that StitchCUDA achieves nearly 100% success rate on end-to-end programming tasks, with 1.72x better speedup than the multi-agent baseline.
  arXiv  Detail & Related papers  (2026-03-03T06:04:49Z)
• CUDA Agent: Large-Scale Agentic RL for High-Performance CUDA Kernel Generation [51.72529978689561]
  Agent is a large-scale agentic reinforcement learning system that develops kernel expertise through three components.<n>Agent delivers 100%, 100%, and 92% faster rate over torchcompile on KernelBench.
  arXiv  Detail & Related papers  (2026-02-27T18:58:05Z)
• CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization [36.794824560677064]
  CudaForge is a training-free multi-agent workflow for kernel generation and optimization.<n>By leveraging base models like OpenAI-o3, CudaForge achieves 97.6% correctness generated kernels and an average 1.68$times$ speedup.
  arXiv  Detail & Related papers  (2025-10-23T22:52:00Z)
• FlexCTC: GPU-powered CTC Beam Decoding With Advanced Contextual Abilities [16.660841429852333]
  We present a novel open-source FlexCTC toolkit for fully-based beam decoding, designed for Connectionist Temporal Classification (CTC) models.<n>Developed entirely in Python and PyTorch, it offers a fast, user-friendly, and alternative to traditional C++, or WFST-based GPUs.<n>It also supports advanced contextualization techniques, including GPU-powered N-gram language model fusion and phrase-level boosting.
  arXiv  Detail & Related papers  (2025-08-10T12:15:57Z)
• CUDA-LLM: LLMs Can Write Efficient CUDA Kernels [9.287036563375617]
  Large Language Models (LLMs) have demonstrated strong capabilities in general-purpose code generation.<n>We propose a novel framework called textbfFeature SearchReinforcement (FSR) FSR jointly optimize compilation and functional correctness.
  arXiv  Detail & Related papers  (2025-06-10T10:51:03Z)
• Cheddar: A Swift Fully Homomorphic Encryption Library Designed for GPU Architectures [2.613335121517245]
  Fully homomorphic encryption (FHE) frees cloud computing from privacy concerns by enabling secure computation on encrypted data.<n>We present Cheddar, a high-performance FHE library for GPU, achieving substantial speedups over previous GPU implementations.
  arXiv  Detail & Related papers  (2024-07-17T23:49:18Z)
• Let the Code LLM Edit Itself When You Edit the Code [50.46536185784169]
  underlinetextbfPositional textbfIntegrity textbfEncoding (PIE)<n>PIE reduces computational overhead by over 85% compared to the standard full recomputation approach.<n>Results demonstrate that PIE reduces computational overhead by over 85% compared to the standard full recomputation approach.
  arXiv  Detail & Related papers  (2024-07-03T14:34:03Z)
• Hardware-Aware Parallel Prompt Decoding for Memory-Efficient Acceleration of LLM Inference [23.633481089469836]
  Auto-regressive decoding of Large Language Models (LLMs) results in significant overheads in their hardware performance.<n>We propose a novel parallel prompt decoding that requires only $0.0002$% trainable parameters, enabling efficient training on a single A100-40GB GPU in just 16 hours.<n>Our approach demonstrates up to 2.49$times$ speedup and maintains a minimal memory overhead of just $0.0004$%.
  arXiv  Detail & Related papers  (2024-05-28T22:19:30Z)
• INR-Arch: A Dataflow Architecture and Compiler for Arbitrary-Order Gradient Computations in Implicit Neural Representation Processing [66.00729477511219]
  Given a function represented as a computation graph, traditional architectures face challenges in efficiently computing its nth-order gradient. We introduce INR-Arch, a framework that transforms the computation graph of an nth-order gradient into a hardware-optimized dataflow architecture. We present results that demonstrate 1.8-4.8x and 1.5-3.6x speedup compared to CPU and GPU baselines respectively.
  arXiv  Detail & Related papers  (2023-08-11T04:24:39Z)
• Harnessing Deep Learning and HPC Kernels via High-Level Loop and Tensor Abstractions on CPU Architectures [67.47328776279204]
  This work introduces a framework to develop efficient, portable Deep Learning and High Performance Computing kernels. We decompose the kernel development in two steps: 1) Expressing the computational core using Processing Primitives (TPPs) and 2) Expressing the logical loops around TPPs in a high-level, declarative fashion. We demonstrate the efficacy of our approach using standalone kernels and end-to-end workloads that outperform state-of-the-art implementations on diverse CPU platforms.
  arXiv  Detail & Related papers  (2023-04-25T05:04:44Z)
• HDCC: A Hyperdimensional Computing compiler for classification on embedded systems and high-performance computing [58.720142291102135]
  This work introduces the name compiler, the first open-source compiler that translates high-level descriptions of HDC classification methods into optimized C code. name is designed like a modern compiler, featuring an intuitive and descriptive input language, an intermediate representation (IR), and a retargetable backend. To substantiate these claims, we conducted experiments with HDCC on several of the most popular datasets in the HDC literature.
  arXiv  Detail & Related papers  (2023-04-24T19:16:03Z)
• Providing Meaningful Data Summarizations Using Examplar-based Clustering in Industry 4.0 [67.80123919697971]
  We show, that our GPU implementation provides speedups of up to 72x using single-precision and up to 452x using half-precision compared to conventional CPU algorithms. We apply our algorithm to real-world data from injection molding manufacturing processes and discuss how found summaries help with steering this specific process to cut costs and reduce the manufacturing of bad parts.
  arXiv  Detail & Related papers  (2021-05-25T15:55:14Z)

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.