Porting the COSMO Weather Model to Manycore CPUs

Thaler, Felix; Moosbrugger, Stefan; Osuna, Carlos; Bianco, Mauro; Vogt, Hannes; Afanasyev, Anton; Mosimann, Lukas; Fuhrer, Oliver; Schulthess, T. C.; Hoefler, Torsten

doi:10.1145/3324989.3325723

Cited by 25 publications

(21 citation statements)

References 14 publications

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“…thescientific code). Other projects decide to rely on domain-specific third-party frameworks (e.g., Firedrake [5], GridTools [6], and PSyclone [7]) or more general lowerlevel libraries (e.g., Alpaka [8], Kokkos [9], and RAJA [10]), which is commonly referred to as separation of concerns, because the scientific concerns are separated from concerns related to the (heterogeneous) massively parallel hardware.…”

Section: Established Approachesmentioning

confidence: 99%

Leveraging HPC accelerator architectures with modern techniques — hydrologic modeling on GPUs with ParFlow

Hokkanen

Kollet

Kraus³

et al. 2021

Comput Geosci

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Rapidly changing heterogeneous supercomputer architectures pose a great challenge to many scientific communities trying to leverage the latest technology in high-performance computing. Many existing projects with a long development history have resultedin a large amount of code that is not directly compatible with the latest accelerator architectures. Furthermore, due to limited resources of scientific institutions, developing and maintaining architecture-specific ports is generally unsustainable. In order to adapt to modern accelerator architectures, many projects rely on directive-based programming models or build the codebase tightly around a third-party domain-specific language or library. This introduces external dependencies out of control of theproject. The presented paper tackles the issue by proposing a lightweight application-side adaptor layer for compute kernels and memory management resulting in a versatile and inexpensive adaptation of new accelerator architectures with little drawbacks.A widely used hydrologic model demonstrates that such an approach pursued more than 20 years ago is still paying off with modern accelerator architectures as demonstrated by a very significant performance gain from NVIDIA A100 GPUs, high developer productivity, and minimally invasive implementation; all while the codebase is kept well maintainable in the long-term.

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Section: Established Approachesmentioning

confidence: 99%

Leveraging HPC accelerator architectures with modern techniques — hydrologic modeling on GPUs with ParFlow

Hokkanen

Kollet

Kraus³

et al. 2021

Comput Geosci

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“…In addition, performance and portability across multiple architectures with a single source code requires NWP models to shift to domain-specific-language (DSL)-based approaches, such as Kokkos (Bertagna et al, 2019), Psyclone (Adams et al, 2019), and Stella/GridTools (Fuhrer et al, 2014; Thaler et al, 2019). Fault tolerance efforts requiring code modifications will more naturally interface with DSLs, see Section 3.3.4 in this paper for an example.…”

Section: Discussionmentioning

confidence: 99%

Resilience and fault tolerance in high-performance computing for numerical weather and climate prediction

Benacchio

Bonaventura

Altenbernd

et al. 2021

The International Journal of High Performance Computing Applica

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Progress in numerical weather and climate prediction accuracy greatly depends on the growth of the available computing power. As the number of cores in top computing facilities pushes into the millions, increased average frequency of hardware and software failures forces users to review their algorithms and systems in order to protect simulations from breakdown. This report surveys hardware, application-level and algorithm-level resilience approaches of particular relevance to time-critical numerical weather and climate prediction systems. A selection of applicable existing strategies is analysed, featuring interpolation-restart and compressed checkpointing for the numerical schemes, in-memory checkpointing, user-level failure mitigation and backup-based methods for the systems. Numerical examples showcase the performance of the techniques in addressing faults, with particular emphasis on iterative solvers for linear systems, a staple of atmospheric fluid flow solvers. The potential impact of these strategies is discussed in relation to current development of numerical weather prediction algorithms and systems towards the exascale. Trade-offs between performance, efficiency and effectiveness of resiliency strategies are analysed and some recommendations outlined for future developments.

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“…Third, we provide a discussion section (Section 5) that provides various insights and takeaways while designing HBM-based FPGA accelerators, which we believe would be useful for future FPGA architects and programmers. Fourth, we implement and evaluate the copy stencil [161] (Figure 3), a stencil from the COSMO model to benchmark the peak performance on a platform. Fifth, we use the OC-Accel framework 3 instead of the SNAP framework for OCAPI accelerator development and deployment.…”

Section: At T Ainable Perform Ance Is Const Rained By M Em Ory Bandwi...mentioning

confidence: 99%

“…The central part of the COSMO model (called dynamical core or dycore) solves the Euler equations on a curvilinear grid and applies implicit discretization in the vertical dimension (i.e., parameters are dependent on each other at the same time instance [42]) and explicit discretization in the horizontal dimension (i.e., a solution is dependent on the previous system state [42]). The use of diferent discretizations leads to three computational patterns [161]: 1) horizontal stencils, 2) tridiagonal solvers in the vertical dimension, and 3) pointwise computation. These computational kernels are compound stencil kernels that operate on a three-dimensional grid [79].…”

Section: Introductionmentioning

confidence: 99%

Accelerating Weather Prediction Using Near-Memory Reconfigurable Fabric

Singh

Diamantopoulos

Gómez-Luna

et al. 2022

ACM Trans. Reconfigurable Technol. Syst.

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Ongoing climate change calls for fast and accurate weather and climate modeling. However, when solving large-scale weather prediction simulations, state-of-the-art CPU and GPU implementations suffer from limited performance and high energy consumption. These implementations are dominated by complex irregular memory access patterns and low arithmetic intensity that pose fundamental challenges to acceleration. To overcome these challenges, we propose and evaluate the use of near-memory acceleration using a reconfigurable fabric with high-bandwidth memory (HBM). We focus on compound stencils that are fundamental kernels in weather prediction models. By using high-level synthesis techniques, we develop NERO, an FPGA+HBM-based accelerator connected through OCAPI (Open Coherent Accelerator Processor Interface) to an IBM POWER9 host system. Our experimental results show that NERO outperforms a 16-core POWER9 system by 5.3 × and 12.7 × when running two different compound stencil kernels. NERO reduces the energy consumption by 12 × and 35 × for the same two kernels over the POWER9 system with an energy efficiency of 1.61 GFLOPS/Watt and 21.01 GFLOPS/Watt. We conclude that employing near-memory acceleration solutions for weather prediction modeling is promising as a means to achieve both high performance and high energy efficiency.

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Porting the COSMO Weather Model to Manycore CPUs

Cited by 25 publications

References 14 publications

Leveraging HPC accelerator architectures with modern techniques — hydrologic modeling on GPUs with ParFlow

Leveraging HPC accelerator architectures with modern techniques — hydrologic modeling on GPUs with ParFlow

Resilience and fault tolerance in high-performance computing for numerical weather and climate prediction

Accelerating Weather Prediction Using Near-Memory Reconfigurable Fabric

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