arXiv:2606.04847v1 Announce Type: cross Abstract: Native GPU kernel generation turns high-level tensor programs into executable, efficient low-level code. Existing Large Language Models (LLMs) struggle with this task, while execution-based reinforcement learning suffers from sparse rewards, reward hacking, and training instability. We present MusaCoder, a full-stack training framework for native GPU kernel generation on CUDA and MUSA backends. MusaCoder combines progressive kernel-oriented data synthesis, diversity-preserving rejection fine-tuning, and execution-feedback Reinforcement Learning
The increasing demand for efficient AI model deployment and the limitations of existing GPU kernel generation methods are driving innovation in this specific area.
Efficient native GPU kernel generation is crucial for maximizing AI hardware performance, directly impacting the speed and cost of AI development and deployment.
This advancement suggests an improved ability to optimize AI computations on specific hardware architectures, potentially broadening the competitive landscape for GPU manufacturers beyond NVIDIA's CUDA dominance.
- · Moore Threads
- · GPU manufacturers
- · AI developers
- · High-performance computing (HPC) sector
- · Less optimized GPU architectures
- · Generative AI models without specialized optimization
MusaCoder offers a more robust framework for optimizing AI workloads on Moore Threads GPUs.
Improved GPU kernel generation could reduce the compute overhead for AI training and inference, making AI more accessible and cost-effective.
Enhanced native GPU performance could accelerate the development of more complex AI models and applications, including AI agents and advanced robotics.
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Read at arXiv cs.LG