MLSys

Event Core This tutorial provides a comprehensive deep dive into modern GPU programming techniques tailored for machine learning systems, focusing on hardware-aware optimization to break through performance bottlenecks in training and inference. Bagua Insight ▶ The Downward Shift in Programming: As compute demands for LLMs skyrocket, relying solely on high-level frameworks like PyTorch is no longer sufficient. Proficiency in intermediate-level languages like Triton is rapidly becoming the gold standard for infrastructure engineers. ▶ The Memory Wall Dilemma: The frontier of GPU optimization has shifted from raw FLOPS to memory hierarchy management. Operator fusion and optimized memory access patterns are now the primary levers for minimizing latency in production-grade AI systems. Actionable Advice Engineering leads should prioritize Triton for custom kernel development to strike an optimal balance between CUDA-level performance and Python-native developer velocity. System architects must double down on operator fusion strategies to mitigate memory bandwidth bottlenecks, which remains the most effective path to scaling throughput without increasing hardware overhead.