NVIDIA Tensor Core 的演变：从 Volta 到 Blackwell

Core Insights - The article emphasizes the rapid evolution of GPU computing capabilities in artificial intelligence and deep learning, driven by Tensor Core technology, which significantly outpaces Moore's Law [1][3] - It highlights the importance of understanding the architecture and programming models of Nvidia's GPUs to grasp the advancements in Tensor Core technology [3] Group 1: Performance Principles - Amdahl's Law defines the maximum speedup achievable through parallelization, emphasizing that performance gains are limited by the serial portion of a task [5] - Strong and weak scaling are discussed, where strong scaling refers to improving performance on a fixed problem size, while weak scaling addresses solving larger problems in constant time [6][8] Group 2: Data Movement and Efficiency - Data movement is identified as a significant performance bottleneck, with the cost of moving data being much higher than computation, leading to the concept of the "memory wall" [10] - Efficient data handling is crucial for maximizing GPU performance, particularly in the context of Tensor Core operations [10] Group 3: Tensor Core Architecture Evolution - The article outlines the evolution of Nvidia's Tensor Core architecture, including Tesla V100, A100, H100, and Blackwell GPUs, detailing the enhancements in each generation [11] - The introduction of specialized instructions like HMMA for half-precision matrix multiplication is highlighted as a key development in Tensor Core technology [18][19] Group 4: Tensor Core Generations - The first generation of Tensor Core in the Volta architecture supports FP16 input and FP32 accumulation, optimizing for mixed-precision training [22][27] - The Turing architecture introduced the second generation of Tensor Core with support for INT8 and INT4 precision, enhancing capabilities for deep learning applications [27] - The Ampere architecture further improved performance with asynchronous data copying and introduced new MMA instructions that reduce register pressure [29][30] - The Hopper architecture introduced Warpgroup-level MMA, allowing for more flexible and efficient operations [39] Group 5: Memory and Data Management - The introduction of Tensor Memory (TMEM) in the Blackwell architecture aims to alleviate register pressure and improve data access efficiency [43] - The article discusses the importance of structured sparsity in enhancing Tensor Core throughput, particularly in the context of the Ampere and Hopper architectures [54][57] Group 6: Performance Metrics - The article provides comparative metrics for Tensor Core performance across different architectures, showing significant improvements in FLOP/cycle and memory bandwidth [59]