Accelerating Scientific Applications with the NVIDIA Grace Hopper Platform

NVIDIA GH200 Grace Hopper Superchip is a breakthrough processor designed from the ground up for giant-scale AI and HPC applications. Building on a decade of GPU acceleration, Grace-Hopper realizes NVIDIA NVLink Chip-to-chip (C2C) interconnection, with a bandwidth of 900 GB/s between the Grace CPU and the Hopper GPU. C2C enables coherent memory at 7x the bandwidth of PCIe Gen 5. This removes the conceptual CPU/GPU memory divide and lowers barriers for scientists accelerating their applications. With more application code executing on GPUs, workload performance becomes increasingly susceptible to non-GPU limiters like data movement and CPU performance (Amdahl s Law). Grace combines 72 Arm Neoverse-V2 cores with NVIDIA Scalable Coherency Fabric (SCF), a distributed cache and mesh fabric with 3.2 TB/s bi-section bandwidth. This high bandwidth mesh enables one NUMA node for all 72 CPU cores, simplifying multi-core programming. Each core implements a 512-bit SVE2 SIMD pipeline for a total CPU FP64 theoretical peak of 7.1 teraflops. When combined with the up to 500 GB/s memory bandwidth of the LPDDR5X, Grace delivers twice the performance-per-Watt of conventional x86-64 CPUs. This session presents HPC and AI workload performance results with a technical deep-dive into the specific features of Grace-Hopper that accelerate each workload. Attendees will gain a deeper understanding of how to extract the performance offered by Grace-Hopper and realize the potential of this innovative, energy-efficient platform for science and industry.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2024-10-31
DOI
https://doi.org/10.5281/zenodo.15293399
Primary Topic
Parallel Computing and Optimization Techniques
Type
article
Field-Weighted Citation Impact
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article

Accelerating Scientific Applications with the NVIDIA Grace Hopper Platform

Zenodo (CERN European Organization for Nuclear Research)
Parallel Computing and Optimization Techniques
article

Accelerating Scientific Applications with the NVIDIA Grace Hopper Platform

article en

Abstract

NVIDIA GH200 Grace Hopper Superchip is a breakthrough processor designed from the ground up for giant-scale AI and HPC applications. Building on a decade of GPU acceleration, Grace-Hopper realizes NVIDIA NVLink Chip-to-chip (C2C) interconnection, with a bandwidth of 900 GB/s between the Grace CPU and the Hopper GPU. C2C enables coherent memory at 7x the bandwidth of PCIe Gen 5. This removes the conceptual CPU/GPU memory divide and lowers barriers for scientists accelerating their applications. With more application code executing on GPUs, workload performance becomes increasingly susceptible to non-GPU limiters like data movement and CPU performance (Amdahl s Law). Grace combines 72 Arm Neoverse-V2 cores with NVIDIA Scalable Coherency Fabric (SCF), a distributed cache and mesh fabric with 3.2 TB/s bi-section bandwidth. This high bandwidth mesh enables one NUMA node for all 72 CPU cores, simplifying multi-core programming. Each core implements a 512-bit SVE2 SIMD pipeline for a total CPU FP64 theoretical peak of 7.1 teraflops. When combined with the up to 500 GB/s memory bandwidth of the LPDDR5X, Grace delivers twice the performance-per-Watt of conventional x86-64 CPUs. This session presents HPC and AI workload performance results with a technical deep-dive into the specific features of Grace-Hopper that accelerate each workload. Attendees will gain a deeper understanding of how to extract the performance offered by Grace-Hopper and realize the potential of this innovative, energy-efficient platform for science and industry.

Zenodo (CERN European Organization for Nuclear Research)
Affordable and clean energy
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Parallel Computing and Optimization Techniques
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