AI Systems Performance Engineering Optimizing Model Training and Inference Workloads with GPUs, CUDA, and PyTorch (Chris Fregly)（Z-Library）

Author： Chris Fregly

Elevate your AI system performance capabilities with this definitive guide to unlocking peak efficiency across every layer of your AI infrastructure. In today’s era of ever-growing generative models, AI Systems Performance Engineering equips professionals with actionable strategies to co-optimize hardware, software, and algorithms for high-performance and cost-effective AI systems. Authored by Chris Fregly, a performance-focused engineering and product leader, this comprehensive resource transforms complex systems into streamlined, high-impact AI solutions. Inside, you’ll discover step-by-step methodologies for fine-tuning GPU CUDA kernels, PyTorch-based algorithms, and multinode training and inference systems. You’ll also master the art of scaling GPU clusters for high performance, distributed model training jobs, and inference servers. • Codesign and optimize hardware, software, and algorithms to achieve maximum throughput and cost savings • Implement cutting-edge inference strategies that reduce latency and boost throughput in real-world settings • Utilize industry-leading scalability tools and frameworks • Profile, diagnose, and eliminate performance bottlenecks across complex AI pipelines • Integrate full stack optimization techniques for robust, reliable AI system performance Whether you’re an engineer, researcher, or developer, AI Systems Performance Engineering gives you a holistic roadmap for building resilient, scalable, and cost-effective AI systems that excel in both training and inference.