Paper: GPU Forecasters: Language Models as Selective Surrogates for Kernel Runtime Optimization
TL;DR · AI Summary
This paper proposes a new method using language models to predict GPU kernel runtime performance, demonstrating superior results compared to traditional methods in multiple benchmarks.
Key Takeaways
- Introduces the GPU Forecasters method, predicting GPU kernel performance using l
- Experiments show this method is 2-3 times faster than traditional approaches.
- Applicable to GPU optimization in AI training and inference scenarios.
Outline
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Introduces the performance bottlenecks of GPU-intensive tasks and limitations of existing optimization methods.
Describes how language models are used as selective surrogates to predict GPU kernel runtime performance.
Demonstrates the advantages of this method in prediction accuracy and speed across multiple benchmarks.
Discusses potential applications of this technology in AI training, inference, and resource scheduling.
Mindmap
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- GPU 性能优化
- 背景
- 计算密集型任务挑战
- 现有方法局限性
- 方法
- GPU Forecasters
- 语言模型预测
- 结果
- 速度提升 2-3 倍
- 多基准测试验证
- 应用
- AI 训练优化
- 资源调度改进
Highlights
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The paper proposes a novel approach using language models to predict GPU kernel runtime performance, significantly improving optimization efficiency.
Experiments show that this method is 2-3 times faster than traditional methods in performance prediction.
Applicable to GPU resource optimization in AI training and inference, reducing latency and increasing throughput.
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