Bridges-2

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.

The Bridges-2 Ocean data management system provides a unified, high-performance filesystem for active project data, archive, and resilience. Ocean consists of two tiers, disk and tape, transparently managed by HPE DMF as a single, highly usable namespace.

Ocean's disk subsystem, for active project data, is a high-performance, internally resilient Lustre parallel filesystem with 15PB of usable capacity, configured to deliver up to 129GB/s and 142GB/s of read and write bandwidth, respectively.

Ocean's tape subsystem, for archive and additional resilience, is a high-performance tape library with 7.2PB of uncompressed capacity (estimated 8.6PB compressed, with compression done transparently in hardware with no performance overhead), configured to deliver 50TB/hour.

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.