从第一性原理让深度学习飞速运行
本文从计算机体系结构的第一性原理出发，深入探讨如何让深度学习模型实现极致的运行效率。作者分析了 GPU 计算、内存带宽、计算核心利用率等关键因素，揭示了从硬件到软件层面优化深度学习性能的系统性方法。内容涵盖硬件原理、编程实践与工程权衡，适合希望理解深度学习性能优化底层逻辑的读者。
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