AI Systems Performance Engineering is a practical and comprehensive guide for enhancing the performance of AI systems across all levels of infrastructure. Amidst the rapid growth of generative models, this book offers engineers, researchers, and developers a wealth of applied optimization strategies. These strategies empower them to collaboratively fine-tune hardware, software components, and algorithms, crafting robust, scalable, and cost-effective solutions for both training and inference.
About the Author
Chris Fregly, a renowned engineering and product leader in performance optimization, provides a step-by-step guide on transforming complex AI systems into high-performance solutions. The book covers topics such as the fine-tuning of CUDA cores on GPUs, the use of PyTorch-based algorithms, and the implementation of distributed training and inference systems across multiple nodes.
Key Topics Covered
GPU Optimization and Scaling
Special attention is given to scaling GPU clusters and managing distributed model training tasks, ensuring efficient resource usage.
High-Performance Inference
Learn about high-performance inference servers and how to reduce latency with modern inference strategies.
Identifying Bottlenecks
Discover how to identify and eliminate performance bottlenecks in complex AI pipelines using leading industry scaling tools.
Full-Stack Optimization
The book emphasizes applying full-stack approaches to ensure the reliable and stable operation of AI systems.
Conclusion
The publication concludes with a detailed checklist of over 175 ready-to-use optimizations, offering practical insights and tools to design and optimize AI systems for maximum throughput and cost efficiency.
