Bridges-2, a resource of Pittsburgh Supercomputing Center, is designed for converged HPC + AI + Data. Its custom topology is optimized for data-centric HPC, AI, and HPDA (High Performance Data Analytics). An extremely flexible software environment along with community data collections and BDaaS (Big Data as a Service) provide the tools necessary for modern pioneering research. The data management system, Ocean, consists of two-tiers, disk and tape, transparently managed as a single, highly usable namespace.
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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 Regular Memory (RM) nodes provide extremely powerful general-purpose computing, machine learning and data analytics, AI inferencing, and pre- and post-processing. Each Bridges RM node consists of two AMD EPYC “Rome” 7742 64-core CPUs, 256-512GB of RAM, and 3.84TB NVMe SSD. 488 Bridges-2 RM nodes have 256GB RAM, and 16 have 512GB RAM for more memory-intensive applications (see also Bridges-2 Extreme Memory nodes, each of which has 4TB of RAM). Bridges-2 RM nodes are connected to other Bridges-2 compute nodes and its Ocean parallel filesystem and archive by HDR-200 InfiniBand.
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 GPU nodes each contain 8 NVIDIA Tesla V100-32GB SXM2 GPUs, providing 40,960 CUDA cores and 5,120 tensor cores. In addition, each node holds 2 Intel Xeon Gold 6248 CPUs; 512GB of DDR4-2933 RAM; and 7.68TB NVMe SSD. They 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.
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 Extreme Memory (EM) nodes enable memory-intensive genome sequence assembly, graph analytics, in-memory databases, statistics, and other applications that need a large amount of memory and for which distributed-memory implementations are not available. Bridges-2 Extreme Memory (EM) nodes each consist of 4 Intel Xeon Platinum 8260M “Cascade Lake” CPUs, 4TB of DDR4-2933 RAM, 7.68TB NVMe SSD. They 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 read or write data from each EM node.
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 GPU nodes each contain 8 NVIDIA Tesla V100-32GB SXM2 GPUs, providing 40,960 CUDA cores and 5,120 tensor cores. In addition, each node holds 2 Intel Xeon Gold 6248 CPUs; 512GB of DDR4-2933 RAM; and 7.68TB NVMe SSD. They 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.
CI Links
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Ask.CI Recent Topics
| Topics | Last Update |
|---|---|
| Bridges-2 suitability for computational chemistry workloads (mainly Gaussian)? | 07/26/23 |
| How do access Bridges2 globus endpoint | 05/26/23 |
| AlphaFold 2 Bridges | 09/14/22 |
| About the ACCESS-Bridges category | 08/29/22 |
