Installation

Prerequisites

• Linux or WSL2

• A CUDA-enabled GPU

• MiniConda or Anaconda (required to install the CUDA Toolkit)

Tested environments

All experiments are run on Linux. Windows is not tested.

• NVIDIA A6000 48 GB × 8, Ubuntu 24.04, CUDA 12.4

• NVIDIA H100 96 GB × 2, Red Hat Enterprise Linux 9.5, CUDA 12.6

• NVIDIA H100 80 GB × 8, Ubuntu 24.04, CUDA 12.4

• NVIDIA H200 141 GB × 8, Ubuntu 24.04, CUDA 12.4

Step-by-Step Setup

1. Configure SSH access to GitHub. One dependency — MASE — requires SSH to clone. Follow GitHub’s guide on Connecting to GitHub with SSH and ensure ~/.ssh/config is set up correctly.

2. Clone the repository.

   git clone https://github.com/AICrossSim/NewComputeBench.git
   cd NewComputeBench
   git submodule update --init
   

3. Activate the environment and install dependencies.

   Install the required packages. Choose one option:

   Option 1 — uv (recommended, assumes CUDA is pre-installed on the system):

   uv sync
   uv pip install -e ./submodules/mase
   

   Note

   uv sync reads pyproject.toml and uv.lock to reproduce the exact environment. Run git submodule update --init before this step to ensure the MASE submodule is available.

   Option 2 — conda + pip (use this if CUDA is not pre-installed):

   conda env create -f environment.yaml
   conda activate new-compute
   pip install -r requirements.txt
   pip install -e ./submodules/mase
   

   Note

   The MASE submodule provides the quantization backend used by PIM and other hardware simulation passes.

   Note

   uv can be installed with pip install uv or via the standalone installer:

   curl -LsSf https://astral.sh/uv/install.sh | sh
   

4. (Optional) Log in to Weights & Biases to track experiment metrics.

   wandb login