Writing High-Performance AI Kernels in Mojo

Name: Writing High-Performance AI Kernels in Mojo
Price: 85 NPR
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Course Details

Language: Mojo

Duration:

Difficulty:

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

Introduction to High-Performance AI Kernels

3 Chapters - 0/3 Completed

What Are AI Kernels? – Definition, purpose, and use cases in ML.

Importance of Performance Optimization – Why kernel efficiency impacts training and inference.

Overview of Mojo’s Low-Level Capabilities – SIMD, GPU acceleration, and memory management.

Efficient Tensor Operations

3 Chapters - 0/3 Completed

Understanding Tensor Memory Layouts – Row-major vs column-major, contiguous memory.

Optimizing Basic Tensor Operations – Element-wise operations, broadcasting, and reduction.

Leveraging Mojo Built-in Functions for Speed – Using optimized tensor primitives.

SIMD and Vectorized Computation

3 Chapters - 0/3 Completed

What is SIMD and Why It Matters – Single instruction, multiple data concept.

Writing SIMD-Enabled Functions in Mojo – Practical examples and patterns.

Performance Gains and Benchmarks – Measuring speedups from vectorization.

GPU Acceleration in Mojo

3 Chapters - 0/3 Completed

Introduction to GPU Programming – Basics of GPU architecture and parallelism.

Offloading Tensor Operations to GPU – Syntax, memory transfers, and kernels.

Optimizing GPU Workloads – Minimizing memory bottlenecks and kernel launch overhead.

Memory and Resource Management

3 Chapters - 0/3 Completed

Understanding Memory Bottlenecks – CPU vs GPU memory, caching, and reuse.

Manual Memory Optimization – Allocators, freeing memory, and avoiding leaks.

Profiling and Debugging Performance – Tools to analyze execution and memory usage.

Advanced Kernel Techniques

3 Chapters - 0/3 Completed

Writing High-Performance AI Kernels in Mojo

Course Details

Requirements

Content

Introduction to High-Performance AI Kernels

What Are AI Kernels? – Definition, purpose, and use cases in ML.

Importance of Performance Optimization – Why kernel efficiency impacts training and inference.

Overview of Mojo’s Low-Level Capabilities – SIMD, GPU acceleration, and memory management.

Efficient Tensor Operations

Understanding Tensor Memory Layouts – Row-major vs column-major, contiguous memory.

Optimizing Basic Tensor Operations – Element-wise operations, broadcasting, and reduction.

Leveraging Mojo Built-in Functions for Speed – Using optimized tensor primitives.

SIMD and Vectorized Computation

What is SIMD and Why It Matters – Single instruction, multiple data concept.

Writing SIMD-Enabled Functions in Mojo – Practical examples and patterns.

Performance Gains and Benchmarks – Measuring speedups from vectorization.

GPU Acceleration in Mojo

Introduction to GPU Programming – Basics of GPU architecture and parallelism.

Offloading Tensor Operations to GPU – Syntax, memory transfers, and kernels.

Optimizing GPU Workloads – Minimizing memory bottlenecks and kernel launch overhead.

Memory and Resource Management

Understanding Memory Bottlenecks – CPU vs GPU memory, caching, and reuse.

Manual Memory Optimization – Allocators, freeing memory, and avoiding leaks.

Profiling and Debugging Performance – Tools to analyze execution and memory usage.

Advanced Kernel Techniques

Loop Unrolling and Instruction Pipelining – Boosting CPU/GPU efficiency.

Combining Multi-Threading and SIMD – Hybrid approaches for maximum speed.

Case Study: Optimizing a Convolution Kernel – End-to-end performance improvements.

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