Can it help with memory leak detection on GPUs?

Yes, it provides guardrails for RAII wrappers in C++ and recommends workflows using compute-sanitizer to identify memory leaks and illegal accesses.

Does this guide support modern CUDA versions?

Yes, the skill is designed for CUDA 11+ and includes modern features like __syncwarp() and cooperative groups logic.

How does it handle CUDA error management?

The skill enforces the use of macro wrappers for every API call and requires post-kernel launch synchronization to ensure errors are caught immediately during development.

How does this skill improve GPU kernel performance?

It provides patterns for coalesced memory access, shared memory tiling, and warp-level primitives that minimize memory latency and maximize SM occupancy.

CUDA Development Guide

Name: CUDA Development Guide
Author: ar4mirez

byar4mirez

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Data Science & ML

Optimizes CUDA and GPU computing development with performance-focused guardrails and industry-standard implementation patterns.

The CUDA Development Guide skill equips Claude with specialized knowledge for high-performance GPU computing, focusing on NVIDIA's CUDA platform. It provides a comprehensive framework for writing efficient .cu and .cuh files by enforcing strict guardrails for memory management, kernel design, and error handling. By utilizing this skill, developers can implement complex patterns like grid-stride loops, shared memory tiling, and warp-level primitives while ensuring optimal SM occupancy and memory bandwidth. It is particularly useful for building performance-critical applications in deep learning, scientific computing, and high-performance computing (HPC) where hardware-level optimization is paramount.

Key Features

013 GitHub stars

02Integrated profiling workflows for Nsight Systems and Nsight Compute analysis.

03Hardware-aware kernel design including occupancy calculation and warp-level primitives.

04Standardized grid-stride loop implementations for arbitrary data size handling.

05Automated CUDA API error-checking and kernel launch validation patterns.

06Memory hierarchy optimization strategies for global, shared, and constant memory.

Use Cases

01Optimizing legacy C++ codebases for massive parallelism and GPU throughput.

02Debugging complex memory access patterns and race conditions in HPC applications.

03Developing custom high-performance GPU kernels for deep learning and AI model acceleration.

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add ar4mirez/samuel cuda-guide

For use in Claude.ai and ChatGPT

Download Skill