风花雪月

Implements efficient block sparse attention mechanism through antidiagonal scoring, achieving 13.5x speedup in long-context Transformer models while maintaining high accuracy across natural language, video understanding, and video generation domains.

Accelerates large language model inference through modular attention reuse technology, achieving 8x GPU inference and 60x CPU inference TTFT latency improvements, particularly suitable for long-context applications like document QA and recommendation systems.

CUDA编译技术详解，包括nvcc编译器参数配置、虚拟架构和真实架构的区别、PTX和CUBIN文件生成，以及多架构兼容性编译策略。

CUDA memory hierarchy explained, including register file, L1 cache, shared memory, constant cache, L2 cache, global memory, local memory, texture and constant memory characteristics and usage.

CUDA流编程技术，包括流的创建、同步、销毁等基本操作，以及流优先级设置、非阻塞流使用等高级特性，帮助实现GPU并行计算优化。

CUTLASS convolution implementation explained, including convolution parameter definitions (K, C, R, S), Conv2dProblemSize configuration, output size calculation formulas, and CUTLASS library applications in convolution operations.

GEMM矩阵乘法优化技术详解，包括基础概念、向量内积和外积优化方法、双缓冲技术等核心优化策略，帮助提升GPU上矩阵运算性能。

In-depth analysis of CUTLASS GEMM implementation, including MmaPolicy and MmaBase template class design, shared memory management, tensor references, warp-level GEMM operations, and other core code structures and implementation details.

In-depth analysis of GPU architectures, covering NVIDIA GPU characteristics including Ampere A100, Turing, Volta, SM counts, CUDA cores, Tensor Core configurations, memory bandwidth, and detailed technical specifications comparison.

GPU instruction throughput and latency analysis, detailing performance characteristics of different instruction types and instruction execution capabilities per SM, providing important reference data for GPU programming optimization.