KForge: Program Synthesis for Diverse AI Hardware Accelerators

• URL: http://arxiv.org/abs/2511.13274v1
• Date: Mon, 17 Nov 2025 11:46:43 GMT
• Title: KForge: Program Synthesis for Diverse AI Hardware Accelerators
• Authors: Taras Sereda, Tom St. John, Burak Bartan, Natalie Serrino, Sachin Katti, Zain Asgar,
• Abstract summary: We present KForge, a platform-agnostic framework built on two collaborative LLM-based agents.<n>A generation agent produces and iteratively refines programs through compilation and correctness feedback, and a performance analysis agent interprets profiling data to guide optimization.<n>This agent-based architecture requires only a single-shot example to target new platforms.
• Score: 5.967639357025406
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: GPU kernels are critical for ML performance but difficult to optimize across diverse accelerators. We present KForge, a platform-agnostic framework built on two collaborative LLM-based agents: a generation agent that produces and iteratively refines programs through compilation and correctness feedback, and a performance analysis agent that interprets profiling data to guide optimization. This agent-based architecture requires only a single-shot example to target new platforms. We make three key contributions: (1) introducing an iterative refinement system where the generation agent and performance analysis agent collaborate through functional and optimization passes, interpreting diverse profiling data (from programmatic APIs to GUI-based tools) to generate actionable recommendations that guide program synthesis for arbitrary accelerators; (2) demonstrating that the generation agent effectively leverages cross-platform knowledge transfer, where a reference implementation from one architecture substantially improves generation quality for different hardware targets; and (3) validating the platform-agnostic nature of our approach by demonstrating effective program synthesis across fundamentally different parallel computing platforms: NVIDIA CUDA and Apple Metal.

Related papers

• PerfGuard: A Performance-Aware Agent for Visual Content Generation [53.591105729011595]
  PerfGuard is a performance-aware agent framework for visual content generation.<n>It integrates tool performance boundaries into task planning and scheduling.<n>It has advantages in tool selection accuracy, execution reliability, and alignment with user intent.
  arXiv  Detail & Related papers  (2026-01-30T05:12:19Z)
• Let It Flow: Agentic Crafting on Rock and Roll, Building the ROME Model within an Open Agentic Learning Ecosystem [90.17610617854247]
  We introduce the Agentic Learning Ecosystem (ALE), a foundational infrastructure that optimize the production pipeline for agentic model.<n>ALE consists of three components: ROLL, a post-training framework for weight optimization; ROCK, a sandbox environment manager for trajectory generation; and iFlow CLI, an agent framework for efficient context engineering.<n>We release ROME, an open-source agent grounded by ALE and trained on over one million trajectories.
  arXiv  Detail & Related papers  (2025-12-31T14:03:39Z)
• Training Report of TeleChat3-MoE [77.94641922160359]
  This technical report mainly presents the underlying training infrastructure that enables reliable and efficient scaling to frontier model sizes.<n>We detail systematic methodologies for operator-level and end-to-end numerical verification accuracy, ensuring consistency across hardware platforms.<n>A systematic parallelization framework, leveraging analytical estimation and integer linear programming, is also proposed to optimize multi-dimensional parallelism configurations.
  arXiv  Detail & Related papers  (2025-12-30T11:42:14Z)
• PerfDojo: Automated ML Library Generation for Heterogeneous Architectures [28.513777562827485]
  We introduce PerfLLM, a novel automatic optimization methodology leveraging Large Language Models (LLMs) and Reinforcement Learning (RL)<n>Central to this is PerfDojo, an environment framing optimization as an RL game using a human-readable, mathematically-inspired code representation that guarantees semantic validity through transformations.<n>We demonstrate PerfLLM's ability to achieve significant performance gains across diverse CPU (x86, Arm, RISC-V) and GPU architectures.
  arXiv  Detail & Related papers  (2025-11-05T16:05:26Z)
• 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)
• AwareCompiler: Agentic Context-Aware Compiler Optimization via a Synergistic Knowledge-Data Driven Framework [42.57224438231615]
  This paper introduces textbfAwareCompiler, an agentic framework for compiler optimization.<n>Three key innovations: structured knowledge integration and dataset construction, knowledge-driven adaptive pass generation, and data-driven hybrid training pipeline.<n> Experimental results on standard benchmarks demonstrate that AwareCompiler significantly outperforms existing baselines in both performance and efficiency.
  arXiv  Detail & Related papers  (2025-10-13T02:02:36Z)
• CompileAgent: Automated Real-World Repo-Level Compilation with Tool-Integrated LLM-based Agent System [52.048087777953064]
  We propose CompileAgent, an agent framework dedicated to repo-level compilation.<n>CompileAgent integrates five tools and a flow-based agent strategy, enabling interaction with software artifacts for compilation instruction search and error resolution.<n>We show that our method significantly improves the compilation success rate, ranging from 10% to 71%.
  arXiv  Detail & Related papers  (2025-05-07T08:59:14Z)
• 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)
• Energy-efficient Task Adaptation for NLP Edge Inference Leveraging Heterogeneous Memory Architectures [68.91874045918112]
  adapter-ALBERT is an efficient model optimization for maximal data reuse across different tasks. We demonstrate the advantage of mapping the model to a heterogeneous on-chip memory architecture by performing simulations on a validated NLP edge accelerator.
  arXiv  Detail & Related papers  (2023-03-25T14:40:59Z)
• ProGraML: Graph-based Deep Learning for Program Optimization and Analysis [16.520971531754018]
  We introduce ProGraML, a graph-based program representation for machine learning. ProGraML achieves an average 94.0 F1 score, significantly outperforming the state-of-the-art approaches. We then apply our approach to two high-level tasks - heterogeneous device mapping and program classification - setting new state-of-the-art performance in both.
  arXiv  Detail & Related papers  (2020-03-23T20:27:00Z)
• Optimizing Streaming Parallelism on Heterogeneous Many-Core Architectures: A Machine Learning Based Approach [16.702537371391053]
  This article presents an automatic approach to derive a good solution for hardware resource partition and task granularity for task-based parallel applications on heterogeneous many-core architectures. Our approach employs a performance model to estimate the resulting performance of the target application under a given resource partition and task granularity configuration. Compared to the single-stream version, our approach achieves a 1.6x and 1.1x speedup on the XeonPhi and the GPU platform, respectively.
  arXiv  Detail & Related papers  (2020-03-05T21:18:21Z)

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.