KForge: Program Synthesis for Diverse AI Hardware Accelerators
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908659419709440 |
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| author | Sereda, Taras John, Tom St. Bartan, Burak Serrino, Natalie Katti, Sachin Asgar, Zain |
| author_facet | Sereda, Taras John, Tom St. Bartan, Burak Serrino, Natalie Katti, Sachin Asgar, Zain |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_13274 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | KForge: Program Synthesis for Diverse AI Hardware Accelerators Sereda, Taras John, Tom St. Bartan, Burak Serrino, Natalie Katti, Sachin Asgar, Zain Machine Learning Artificial Intelligence Multiagent Systems Performance Software Engineering 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. |
| title | KForge: Program Synthesis for Diverse AI Hardware Accelerators |
| topic | Machine Learning Artificial Intelligence Multiagent Systems Performance Software Engineering |
| url | https://arxiv.org/abs/2511.13274 |