Record-Remix-Replay: Hierarchical GPU Kernel Optimization using Evolutionary Search

• URL: http://arxiv.org/abs/2604.11109v1
• Date: Mon, 13 Apr 2026 07:25:12 GMT
• Title: Record-Remix-Replay: Hierarchical GPU Kernel Optimization using Evolutionary Search
• Abstract summary: Record-Remix-Replay (R3) is a hierarchical optimization framework that combines evolutionary search, Bayesian optimization, and record-replay compilation techniques.<n>We show that Record-Remix-Replay can optimize full scientific applications better than traditional approaches over kernel parameters and compiler flags.
• Score: 3.078544805915989
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As high-performance computing and AI workloads become increasingly dependent on GPUs, maintaining high performance across rapidly evolving hardware generations has become a major challenge. Developers often spend months tuning scientific applications to fully exploit new architectures, navigating a complex optimization space that spans algorithm design, source implementation, compiler flags and pass sequences, and kernel launch parameters. Existing approaches can effectively search parts of this space in isolation, such as launch configurations or compiler settings, but optimizing across the full space still requires substantial human expertise and iterative manual effort. In this paper, we present Record-Remix-Replay (R^3), a hierarchical optimization framework that combines LLM-driven evolutionary search, Bayesian optimization, and record-replay compilation techniques to efficiently explore GPU kernel optimizations from source-level implementation choices down to compiler pass ordering and runtime configuration. By making candidate evaluation fast and scalable, our approach enables practical end-to-end search over optimization dimensions that are typically treated separately. We show that Record-Remix-Replay can optimize full scientific applications better than traditional approaches over kernel parameters and compiler flags, while also being nearly an order of magnitude faster than modern evolutionary search approaches.

Related papers

• HIERA: Workload-Aware Planning Across Implementation Spaces for GPU Kernel Optimization [17.600449573236194]
  We propose textscHIERA, a hierarchical search-space planning framework for kernel optimization.<n>textscHIERA constructs contract-augmented task specifications, selects an appropriate implementation space across PyTorch operators, libraries, and custom kernels.<n>Experiments show textscHIERA delivers stronger overall implementation refinement, validity, and optimization performance than existing training-free methods.
  arXiv  Detail & Related papers  (2026-08-21T14:29:20Z)
• GPU Forecasters: Language Models as Selective Surrogates for Kernel Runtime Optimization [73.92934811090163]
  We study how LLMs can serve as selective GPU surrogates for kernel evaluation.<n>We measure whether their forecasts are accurate, calibrated, and practically useful for recovering fast kernels under limited GPU-measurement budgets.<n>Our experiments demonstrate that LLMs can accurately forecast relative kernel performance, that their utility can be improved through reinforcement learning.
  arXiv  Detail & Related papers  (2026-05-29T15:56:08Z)
• Making LLMs Optimize Multi-Scenario CUDA Kernels Like Experts [25.264078143000045]
  Current automated kernel optimization methods focus on machine learning applications, while overlooking broader domains like sparse matrix operations in scientific computing.<n>In this paper, we address the absence of systematic evaluation for multi-scenario settings introducing MSBench.<n>Building on this benchmark, we introduce MSMaster, a hardware-aware system for kernel optimization that leverages GPU profiling information and automatically constructs the full compilation and execution toolchain.
  arXiv  Detail & Related papers  (2026-03-07T12:22:43Z)
• K-Search: LLM Kernel Generation via Co-Evolving Intrinsic World Model [57.440609834690385]
  Existing approaches treat Large Language Models (LLMs) as rapid code generators within evolutionary loops.<n>We propose Search via Co-Evolving World Model and build K-Search based on this method.<n>We evaluate K-Search on diverse, complex kernels FlashInfer, including GQA, MLA, and MoE kernels.
  arXiv  Detail & Related papers  (2026-02-22T11:06:22Z)
• A Two-Stage GPU Kernel Tuner Combining Semantic Refactoring and Search-Based Optimization [9.49293344824955]
  This paper introduces a template-based rewriting layer on top of an agent-driven iterative loop.<n>The proposed method can be extended to deliver automated performance optimization for real production workloads.
  arXiv  Detail & Related papers  (2026-01-19T03:40:12Z)
• STARK: Strategic Team of Agents for Refining Kernels [23.717055490630596]
  We introduce an agentic framework for GPU kernel optimization that explores the design space through multi-agent collaboration.<n>This framework mimics the workflow of expert engineers, enabling LLMs to reason about hardware trade-offs, incorporate profiling feedback, and refine kernels iteratively.<n>We evaluate our approach on KernelBench, a benchmark for LLM-based kernel optimization, and demonstrate substantial improvements over baseline agents.
  arXiv  Detail & Related papers  (2025-10-19T20:41:46Z)
• SwizzlePerf: Hardware-Aware LLMs for GPU Kernel Performance Optimization [4.043444100380313]
  SwizzlePerf automatically generates spatial optimizations for GPU kernels on disaggregated architectures.<n>On a suite of 10 diverse ML and Science kernels, SwizzlePerf can generate swizzling patterns for 9 of the kernels that achieve up to a 2.06x speedup and 70% improvement in L2 hit rate.
  arXiv  Detail & Related papers  (2025-08-27T20:30:43Z)
• CompilerDream: Learning a Compiler World Model for General Code Optimization [58.87557583347996]
  We introduce CompilerDream, a model-based reinforcement learning approach to general code optimization.<n>It comprises a compiler world model that accurately simulates the intrinsic properties of optimization passes and an agent trained on this model to produce effective optimization strategies.<n>It excels across diverse datasets, surpassing LLVM's built-in optimizations and other state-of-the-art methods in both settings of value prediction and end-to-end code optimization.
  arXiv  Detail & Related papers  (2024-04-24T09:20:33Z)
• 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)
• Kernel methods through the roof: handling billions of points efficiently [94.31450736250918]
  Kernel methods provide an elegant and principled approach to nonparametric learning, but so far could hardly be used in large scale problems. Recent advances have shown the benefits of a number of algorithmic ideas, for example combining optimization, numerical linear algebra and random projections. Here, we push these efforts further to develop and test a solver that takes full advantage of GPU hardware.
  arXiv  Detail & Related papers  (2020-06-18T08:16:25Z)
• 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)
• PolyScientist: Automatic Loop Transformations Combined with Microkernels for Optimization of Deep Learning Primitives [55.79741270235602]
  We develop a hybrid solution to the development of deep learning kernels. We use the advanced polyhedral technology to automatically tune the outer loops for performance.
  arXiv  Detail & Related papers  (2020-02-06T08:02:34Z)

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.