Cuda Course

This project is a CUDA programming course and technical guide focused on writing and optimizing GPU kernels for hardware acceleration. It provides structured learning resources for using the CUDA platform to execute operations on silicon architectures.

The material covers the optimization of linear algebra kernels and the analysis of machine learning deployment. It includes guidance on identifying acceleration tools, mapping the deep learning ecosystem, and evaluating the frameworks used to move models from research to production environments.

The scope extends to GPU performance optimization and the tracking of machine learning experiments, including the recording of training metrics and model weights.

Features

GPU Programming Courses - Provides a structured learning resource for writing, compiling, and executing GPU kernels using the CUDA platform.

Hardware Acceleration Backends - Explains how high-level framework calls are mapped to low-level silicon instructions via hardware acceleration backends.

GPU Kernel Performance Tuning - Guides the improvement of execution speed by fusing linear algebra operations and generating optimized machine code.

Programming Courses - Offers a comprehensive structured course on programming and optimizing GPU kernels via CUDA.

Hardware Acceleration Guides - Provides a technical overview of low-level languages and compilers used to optimize performance on silicon architectures.

Tool Catalogs - Catalogs the low-level languages and compilers used to optimize performance across various silicon architectures.

Operation-to-Accelerator Mapping - Explains the process of mapping software operations to specific GPU and CPU acceleration backends.

CUDA Kernel Compilers - Covers the use of NVCC to translate CUDA source code into PTX assembly and machine code.

Kernel Fusion Operations - Teaches techniques for combining multiple mathematical operations into single kernels to reduce memory bandwidth overhead.

Source Code Compilers - Provides practical instruction on transforming GPU source code into executable binaries.

GPU Linear Algebra Libraries - Teaches how to increase the execution speed of linear algebra operations through GPU-specific optimizations.

Kernel Optimizations - Provides guidance on increasing execution speed by fusing operations into single GPU kernels for hardware acceleration.

Deep Learning Ecosystems - Organizes the frameworks and hardware architectures that constitute the modern deep learning ecosystem.

Production Machine Learning - Evaluates the frameworks and formats required to move models from research into production environments.

Deployment Analysis - Analyzes different frameworks and toolkits used to move machine learning models from research environments into production hardware.

Model Inference Deployment - Guides the analysis of various tools and formats used to deploy models for production and edge computing.

Deep Learning Framework Comparisons - Provides comparative analysis of research tools versus production libraries across different hardware backends.

Intermediate Representations - Describes the use of PTX as a virtual machine ISA for cross-generational GPU hardware portability.

Infatoshicuda-course

Features

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