Automate CUDA toolchain setup, GPU-aware MPI, and scheduler integration for HPC workflows.
npx skills add https://github.com/SciMate-AI/HPC-Skills --skill hpc-gpu-stack
Please help me install this Agent Skill. Skill: hpc-gpu-stack Source: https://github.com/SciMate-AI/HPC-Skills/tree/main/skills/hpc-gpu-stack Command: npx skills add https://github.com/SciMate-AI/HPC-Skills --skill hpc-gpu-stack
This Skill addresses the complexities of building, reviewing, debugging, and launching CUDA- and GPU-accelerated HPC workflows, ensuring compatibility and performance optimization.
Build and launch a CUDA application with GPU acceleration using the hpc-gpu-stack skill.
High-intent search queries and answers about installing and using this skill.
To configure the CUDA toolchain for HPC workflows, you need to manage compiler compatibility and set up the build environment. This ensures reproducible build and launch sequences for CUDA applications.
GPU-aware MPI enables direct memory transfers between GPUs across nodes, while rank-to-GPU mapping assigns specific MPI ranks to designated GPUs. This optimizes communication and memory management in HPC environments.
Yes, you can integrate GPU allocation with Slurm schedulers. This integration manages GPU visibility and resource distribution, ensuring that CUDA-accelerated HPC workflows receive the required GPU resources.
GPU-accelerated simulations experience performance issues and memory errors due to suboptimal stream behavior or memory management. Runtime debugging and profiling can identify these bottlenecks and guide error recovery strategies.
The best way to debug and profile CUDA applications is by using runtime profiling to analyze GPU memory and stream behavior. This identifies performance bottlenecks and provides error recovery guidance for HPC workflows.
QuantumBFSGenerate standardized TOML profiles and verify SSH access for HPC clusters.
m0atAnalyze CUDA kernel performance bottlenecks with Nsight Systems and Nsight Compute.
KangOxfordA structured guide for deploying and debugging multi-node JAX applications, emphasizing GPU cluster-specific considerations.
SciMate-AIGuide building, debugging, and launching MPI applications in HPC environments.
