Accelerating Weather Prediction Using Near-Memory Reconfigurable Fabric
نویسندگان
چکیده
Ongoing climate change calls for fast and accurate weather modeling. However, when solving large-scale prediction simulations, state-of-the-art CPU GPU implementations suffer from limited performance high energy consumption. These are dominated by complex irregular memory access patterns low arithmetic intensity that pose fundamental challenges to acceleration. To overcome these challenges, we propose evaluate the use of near-memory acceleration using a reconfigurable fabric with high-bandwidth (HBM). We focus on compound stencils kernels in models. By high-level synthesis techniques, develop NERO, an FPGA+HBM-based accelerator connected through OCAPI (Open Coherent Accelerator Processor Interface) IBM POWER9 host system. Our experimental results show NERO outperforms 16-core system 5.3x 12.7x running two different stencil kernels. reduces consumption 12x 35x same over efficiency 1.61 GFLOPS/Watt 21.01 GFLOPS/Watt. conclude employing solutions modeling is promising as means achieve both efficiency.
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ژورنال
عنوان ژورنال: ACM Transactions on Reconfigurable Technology and Systems
سال: 2022
ISSN: ['1936-7414', '1936-7406']
DOI: https://doi.org/10.1145/3501804