Description
Bridges-2 combines high-performance computing (HPC), high performance artificial intelligence (HPAI), and large-scale data management to support simulation and modeling, data analytics, community data, and complex workflows. Bridges-2 Accelerated GPU (GPU) nodes are optimized for scalable artificial intelligence (AI; deep learning). They are also available for accelerated simulation and modeling applications. Bridges-2 has four types of GPU nodes: 10 HPE Cray 670 h100-80 nodes, with eight H100-SXM5-80GB GPUs each with 80GB of GPU memory and a total of 2TB RAM per node; 24 HPE v100-32 nodes with eight V100 GPUs with NVLink, each with 32GB of GPU memory and a total of 512GB RAM per node;9 v100-16 nodes containing eight V100 GPUs without NVLink, each with 16GB of GPU memory and a total of 192GB RAM per node; and 3 HPE l40s-48 nodes with 8 L40S GPUs without NVLink, each with 48GB of GPU Memory and a total of 1TB RAM per node.
The nodes are connected to Bridges-2's other compute nodes and its Ocean parallel filesystem and archive by two HDR-200 InfiniBand links, providing 400Gbps of bandwidth to enhance scalability of deep learning training.
RP Description
Bridges-2 GPU is the GPU-accelerated tier of Bridges-2, built from nodes that pair NVIDIA H100, L40S, or V100 GPUs (8 per node, 16 on the DGX-2) with two Intel Xeon CPUs and up to 2 TB of RAM. It is well suited to GPU-accelerated deep learning and molecular dynamics, and is often used for large-scale model training and inference and for biomolecular simulation. Its most heavily used software spans machine-learning frameworks and molecular-dynamics codes.
Top Software (cached)
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Jobs Information
You can run jobs at different sizes and durations on Bridges-2 GPU. The following lists the different queues that you can submit to, describing how many nodes you get, how long you can run, the type of resources you get, and the average wait time.
Jobs are submitted through Slurm. Unless you set otherwise, a job uses a one-hour walltime, up to a 48-hour maximum. Request GPUs by type and count with --gpus=type:n or --gres=gpu:type:n, where the type is one of h100-80, l40s-48, v100-32, or v100-16. In batch mode n is the total GPUs for the job (a multiple of 8); in an interactive session (interact) it is the GPUs per node. Pick a partition by how much of a node you need:
- GPU: whole nodes, all eight GPUs on each (16 on the DGX-2), charged for the full node.
- GPU-shared: 1 to 4 GPUs on a single node shared with other jobs, charged only for the GPUs you request.
- GPU-small: interactive and small or quick jobs on a single V100-32 node.
For submission commands, sample scripts, and node-sharing details, see the Bridges-2 Batch Jobs guide. Partition-specific information is on the GPU and GPU-Shared partitions section of the user guide.