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Menulis High-Performance AI Kernels di Mojo

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Course Details

Language

Mojo

Category

Programming

Certificate

Yes

Requirements

  • 1. Strong understanding of Mojo programming fundamentals.
  • 2. Knowledge of linear algebra, tensors, and AI/ML concepts.
  • 3. Familiarity with concurrency, memory management, and GPU basics.

Content

Reviews

01

Pengantar ke Tinggi Performance AI Kernels

3 Chapters - 0/3 Completed

- Definisi, tujuan, dan menggunakan kasus di ML.

Importance of Performance Optimization - Mengapa efisiensi kernel mempengaruhi pelatihan dan kesimpulan.

Tinjauan dari Mojo 's Lower-Level Kapabilitas - SIMD, percepatan GPU, dan manajemen memori.

02

Operasi Tensor Efisien

3 Chapters - 0/3 Completed

Memahami Tensor Memory Layouts - Row-major vs kolumn- besar, memori kontiguous.

Optimasi Operasi Tensor Dasar - Elemen - bijaksana operasi, penyiaran, dan pengurangan.

Leveraging Mojo Built-in Fungsi untuk Kecepatan - Menggunakan primitif tensor teroptimalkan.

03

SIMD dan Vectorized Computation

3 Chapters - 0/3 Completed

Apa itu SIMD dan Mengapa Ini Matters - instruksi tunggal, beberapa data konsep.

Menulis SIMD-Diaktifkan Fungsi dalam Mojo - Contoh praktis dan pola.

Performa Gains dan Benchmarks - Mengukur speedups dari vektorisasi.

04

Akselerasi GPU di Mojo

3 Chapters - 0/3 Completed

Pengantar ke Pemrograman GPU - Basics arsitektur GPU dan paralel.

Meluncurkan Operasi Tensor ke GPU - Syntax, transfer memori, dan kernel.

Optimisasi GPU Workloads - meminimalkan memori bottlenecks dan peluncuran kernel overhead.

05

Manajemen Memori dan Sumber Daya

3 Chapters - 0/3 Completed

Memahami memori botol - CPU vs memori GPU, caching, dan menggunakan kembali.

Manual Memory Optimisasi - Allocators, membebaskan memori, dan menghindari kebocoran.

Profiling and Debugging Performance - Perkakas untuk menganalisis eksekusi dan penggunaan memori.

06

Teknik Kernel Lanjutan

3 Chapters - 0/3 Completed

Loop Unrolling and Instruction Pipelining - Boosting CPU / GPU efisiensi.

Menggabungkan Multi- Threading dan SIMD - Hibrid pendekatan untuk kecepatan maksimum.

Studi Kasus: Optimizing a Convolution Kernel - End-to-end perbaikan kinerja.