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Even to this date, most earth system models are coded in Fortran, especially those used at the largest compute scales. Our ocean model Veros takes a different approach: it is implemented using the high-level programming language Python. Besides numerous usability advantages, this allows us to leverage modern high-performance frameworks that emerged in tandem with the machine learning boom. By interfacing with the JAX library, Veros is able to run high-performance simulations on both central processing units (CPU) and graphical processing unit (GPU) through the same code base, with full support for distributed architectures. On CPU, Veros is able to match the performance of a Fortran reference, both on a single process and on hundreds of CPU cores. On GPU, we find that each device can replace dozens to hundreds of CPU cores, at a fraction of the energy consumption. We demonstrate the viability of using GPUs for earth system modeling by integrating a global 0.1° eddy-resolving setup in single precision, where we achieve 1.3 model years per day on a single compute instance with 16 GPUs, comparable to over 2,000 Fortran processes.""";
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The system would be the climate change counterpart to Cambridge-1, the world’s most powerful AI supercomputer for healthcare research. We unveiled Cambridge-1 earlier this year in the U.K. and it’s being used by a number of leading healthcare companies.""";
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• Handling the Big Data Problem
• Machine Learning: The Silver Bullet?
• Three Cs needed to pull this off""";
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A high-resolution (1/20∘) global ocean general circulation model with graphics processing unit (GPU) code implementations is developed based on the LASG/IAP Climate System Ocean Model version 3 (LICOM3) under a heterogeneous-compute interface for portability (HIP) framework. The dynamic core and physics package of LICOM3 are both ported to the GPU, and three-dimensional parallelization (also partitioned in the vertical direction) is applied. The HIP version of LICOM3 (LICOM3-HIP) is 42 times faster than the same number of CPU cores when 384 AMD GPUs and CPU cores are used. LICOM3-HIP has excellent scalability; it can still obtain a speedup of more than 4 on 9216 GPUs compared to 384 GPUs. In this phase, we successfully performed a test of 1/20∘ LICOM3-HIP using 6550 nodes and 26 200 GPUs, and on a large scale, the model's speed was increased to approximately 2.72 simulated years per day (SYPD). By putting almost all the computation processes inside GPUs, the time cost of data transfer between CPUs and GPUs was reduced, resulting in high performance. Simultaneously, a 14-year spin-up integration following phase 2 of the Ocean Model Intercomparison Project (OMIP-2) protocol of surface forcing was performed, and preliminary results were evaluated. We found that the model results had little difference from the CPU version. Further comparison with observations and lower-resolution LICOM3 results suggests that the 1/20∘ LICOM3-HIP can reproduce the observations and produce many smaller-scale activities, such as submesoscale eddies and frontal-scale structures.""";
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Clouds represent a key uncertainty in future climate projection. While explicit cloud resolution remains beyond our computational grasp for global climate, we can incorporate important cloud effects through a computational middle ground called the Multi-scale Modeling Framework (MMF), also known as Super Parameterization. This algorithmic approach embeds high-resolution Cloud Resolving Models (CRMs) to represent moist convective processes within each grid column in a Global Climate Model (GCM). The MMF code requires no parallel data transfers and provides a self-contained target for acceleration. This study investigates the performance of the Energy Exascale Earth System Model-MMF (E3SM-MMF) code on the OLCF Summit supercomputer at an unprecedented scale of simulation. Hundreds of kernels in the roughly 10K lines of code in the E3SM-MMF CRM were ported to GPUs with OpenACC directives. A high-resolution benchmark using 4600 nodes on Summit demonstrates the computational capability of the GPU-enabled E3SM-MMF code in a full physics climate simulation.""";
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