带大家建立一个对机器学习编译的基本概念.

为了公平对比性能都不是一件容易的事情. 各个框架的runtime都可能存在一些不同配置, 需要把他们安排到统一基准线去对比才有意义.

熟悉一下ampl的语法.

关于ortools中Constraints Solver的内部逻辑.

关于Model-Driven Optimization For Tensor Computations论文的阅读笔记.

自从2020年Apple发布的芯片M1/M2/M3, 至少提供了四种不同的方式可以执行高负载的计算任务:

1. 标准的ARMv8 SIMD/NEON向量指令集.

2. 苹果尚未公开文档的AMX(Apple Matrix Co-processor)指令集, 由CPU发射, 在特殊的加速器上运行.

3. 神经网络处理器ANE(Apple Neural Engine)

4. Metal GPU

在M1 Max上单核计算单精度浮点矩阵乘法时, 使用SIMD指令集可达到102 GFLOPS左右的性能, 而使用AMX指令集最多可达到1475 GFLOPS! 本文就来带领大家一同探索AMX指令集, 学习如何解锁这剩下的14倍算力.

Since 2020, Apple has published M1/M2/M3. They have at least four different ways to perform high-intensity computing tasks.

1. Standard arm NEON instructions.

2. Undocumented AMX (Apple Matrix Co-processor) instructions. Issued by the CPU and performed on the co-processor.

3. Apple Neural Engine

4. Metal GPU

If we use ARM NEON instructions to accelerate the sgemm kernel on the single core of the M1 Max, It can achieve a performance of around 102 GFLOPS. But if use AMX instructions it can achieve 1475 GFLOPS!

In this article, I will introduce how you can leverage the AMX instructions to unlock the potential performance of Apple Silicon. And the all code I used in here (Verified on M2 Pro). This article refers to the work of Peter Cawley et al, which contains more instructions and usage methods.

研究一下在macos中如何编译bundle文件并动态加载并运行.

学习mlir中Affine Fusion Pass, 主要关注依赖分析部分.

学习TileFlow这篇论文中是如何进行多个内存层级的tiling.