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Overview

This PR adds a 2-GPU CI job to ci/matrix.yaml for the libcudacxx project to test interactions with multiple GPUs. It also updates the GPU configuration handling in build and run workflow files to properly support multi-GPU runners.

Changes

.github/actions/workflow-build/build-workflow.py

• Enhanced get_gpu function to validate that each GPU definition includes required fields (name, runner, sm), raising an exception when missing
• Updated generate_dispatch_job_name to use gpu["name"] (with ", " prefix) for GPU job labels instead of constructing uppercase strings from raw GPU tags
• Modified generate_dispatch_job_runner to use gpu["runner"] directly (plus -testing suffix when applicable) instead of using gpu["id"] with a hardcoded -latest-1 suffix

.github/actions/workflow-run-job-linux/action.yml

• Updated Docker GPU access logic on *-gpu-* runners to conditionally handle multi-GPU setups
• For multi-GPU runners (GPU count > 1), requests all GPUs via --gpus all
• For single-GPU runners, continues to use --gpus "device=${NVIDIA_VISIBLE_DEVICES:-}"

ci/matrix.yaml

• Added new multi-GPU CI job for libcudacxx: a test job targeting gpu: h100_2gpu with sm: gpu
• Restructured GPU configuration section to include display name and specific runner label for each GPU entry
• Added h100_2gpu GPU model with associated runner label
• Updated rtxpro6000 entry to include runner label

Walkthrough

The PR updates GPU configuration and handling across CI workflow generation and execution. GPU definitions in the matrix gain structured name and runner fields; build script validation enforces these required fields and uses them for job naming and runner selection; runtime logic detects multi-GPU runners and conditionally selects Docker GPU device access; a new h100_2gpu test entry exercises the updated configuration.

Changes

GPU configuration and multi-GPU execution

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😬 CI Workflow Results

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