This repository serves as a practical guide and collection of code examples for optimizing algorithms in CUDA. It targets developers and researchers looking to improve the performance of their GPU-accelerated applications by exploring various CUDA optimization techniques and implementations. The project offers insights into efficient CUDA kernel design, memory access patterns, and leveraging hardware features for maximum throughput.

How It Works

The project is structured into distinct directories, each focusing on a specific optimization technique or algorithm. It demonstrates optimizations for element-wise operations, reductions, atomic operations, and specific kernels like upsample_nearest_2d and index_add. The implementations often draw inspiration from or directly adapt code from frameworks like PyTorch and OneFlow, highlighting performance gains through detailed benchmarks and bandwidth utilization metrics. The core approach involves analyzing existing efficient implementations and providing standalone, optimized CUDA kernels.

Quick Start & Requirements

• Installation: Primarily involves compiling CUDA C++ code. Specific build instructions are usually found within each subdirectory's README.
• Prerequisites: NVIDIA GPU, CUDA Toolkit, C++ compiler. Some examples may require PyTorch or OneFlow for comparison or integration.
• Resources: Requires a CUDA-enabled GPU. Setup time varies per example, but core CUDA compilation is generally quick.

Highlighted Details

• Demonstrates significant performance and bandwidth improvements for element-wise operations using OneFlow's template compared to naive implementations.
• Showcases FastAtomicAdd achieving 3-4x speedup for half type vector dot products by utilizing half2 atomics.
• Provides optimized upsample_nearest_2d kernels from OneFlow, showing improved bandwidth and reduced latency over PyTorch equivalents.
• Includes detailed notes and code for optimizing index_add operations in PyTorch.

Maintenance & Community

The repository is maintained by BBuf and includes links to related learning resources and other GitHub projects by the author. Community engagement is primarily through GitHub stars and issues.

Licensing & Compatibility

The repository's licensing is not explicitly stated in the README. Code snippets are often derived from other frameworks, implying potential compatibility considerations based on their respective licenses.

Limitations & Caveats

The project focuses on specific optimization examples and may not cover all CUDA optimization scenarios. Performance gains are benchmarked on specific hardware (e.g., A100 PCIE 40G) and may vary on different GPU architectures. The content is presented as learning notes, and users should verify the applicability and correctness for their specific use cases.