Acceleration of a Meteorological Limited Area Model with Dataflow Engines

Oriato, Diego; Tilbury, Simon; Marrocu, Marino; Pusceddu, Gabriella

doi:10.1109/saahpc.2012.8

Cited by 26 publications

(9 citation statements)

References 6 publications

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“…Wilhelm [2012] analyzes a high-level approach for programming preconditioners for an ocean model in climate simulations on FPGAs but do not manage any actual acceleration. Oriato et al [2012] accelerate a realistic dynamic core of the LAM model using FPGAs. It is a successful trial on reducing resource usage through fixed-point arithmetic.…”

Section: Related Workmentioning

confidence: 99%

Solving the Global Atmospheric Equations through Heterogeneous Reconfigurable Platforms

Gan

Luk

et al. 2015

ACM Trans. Reconfigurable Technol. Syst.

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One of the most essential and challenging components in climate modeling is the atmospheric model. To solve multiphysical atmospheric equations, developers have to face extremely complex stencil kernels that are costly in terms of both computing and memory resources. This article aims to accelerate the solution of global shallow water equations (SWEs), which is one of the most essential equation sets describing atmospheric dynamics. We first design a hybrid methodology that employs both the host CPU cores and the field-programmable gate array (FPGA) accelerators to work in parallel. Through a careful adjustment of the computational domains, we achieve a balanced resource utilization and a further improvement of the overall performance. By decomposing the resource-demanding SWE kernel, we manage to map the double-precision algorithm into three FPGAs. Moreover, by using fixed-point and reduced-precision floating point arithmetic, we manage to build a fully pipelined mixed-precision design on a single FPGA, which can perform 428 floating-point and 235 fixed-point operations per cycle. The mixed-precision design with four FPGAs running together can achieve a speedup of 20 over a fully optimized design on a CPU rack with two eight-core processorsand is 8 times faster than the fully optimized Kepler GPU design. As for power efficiency, the mixed-precision design with four FPGAs is 10 times more power efficient than a Tianhe-1A supercomputer node.

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Section: Related Workmentioning

confidence: 99%

Solving the Global Atmospheric Equations through Heterogeneous Reconfigurable Platforms

Gan

Luk

et al. 2015

ACM Trans. Reconfigurable Technol. Syst.

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show abstract

“…In [5] Oriato et al accelerate the dynamical core of a limited area meteorological model on a DFE platform and report a 74 times speed up compared to a 12-core multi-threaded CPU implementation. Moreover, reduced precision is widely used in various applications fields, such as seismic imaging [6] and atmospheric modelling [7].…”

Section: Introductionmentioning

confidence: 99%

Lower precision for higher accuracy: Precision and resolution exploration for shallow water equations

Targett

Niu

Russell

et al. 2015

2015 International Conference on Field Programmable Technology (FPT)

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Abstract-Accurate forecasts of future climate with numerical models of atmosphere and ocean are of vital importance. However, forecast quality is often limited by the available computational power. This paper investigates the acceleration of a C-grid shallow water model through the use of reduced precision targeting FPGA technology. Using a double-gyre scenario, we show that the mantissa length of variables can be reduced to 14 bits without reducing the accuracy beyond the error inherent in the model. Our reduced precision FPGA implementation runs 5.4 times faster than a double precision FPGA implementation, and 12 times faster than a multi-threaded CPU implementation. Moreover, our reduced precision FPGA implementation uses 39 times less energy than the CPU implementation and can compute a 100x100 grid for the same energy that the CPU implementation would take for a 29x29 grid.

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“…In recent years, we start to see some FPGA-based acceleration for modules within a global model ( [4], [5]), and for regional weather predictions ( [6]). …”

Section: Introductionmentioning

confidence: 99%

“…Wilhelm et al [5] analyze a high-level approach for programming preconditioners for an ocean model in climate simulations on FPGAs but do not manage actual acceleration. Oriato et al [6] accelerate a realistic dynamic core of LAM model using FPGAs. It is a successful trial on reducing resource usage through fixed-point arithmetic.…”

Section: Introductionmentioning

confidence: 99%

Accelerating solvers for global atmospheric equations through mixed-precision data flow engine

Gan

Luk

et al. 2013

2013 23rd International Conference on Field Programmable Logic and Applications

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One of the most essential and challenging components in a climate system model is the atmospheric model. To solve the multi-physical atmospheric equations, developers have to face extremely complex stencil kernels. In this paper, we propose a hybrid CPU-FPGA algorithm that applies single and multiple FPGAs to compute the upwind stencil for the global shallow water equations. Through mixed-precision arithmetic, we manage to build a fully pipelined upwind stencil design on a single FPGA, which can perform 428 floating-point and 235 fixed-point operations per cycle. The CPU-FPGA algorithm using one Virtex-6 FPGA provides 100 times speedup over a 6-core CPU and 4 times speedup over a hybrid node with 12 CPU cores and a Fermi GPU card. The algorithm using four FPGAs provides 330 times speedup over a 6-core CPU; it is also 14 times faster and 9 times more power efficient than the hybrid CPU-GPU node.

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Acceleration of a Meteorological Limited Area Model with Dataflow Engines

Cited by 26 publications

References 6 publications

Solving the Global Atmospheric Equations through Heterogeneous Reconfigurable Platforms

Solving the Global Atmospheric Equations through Heterogeneous Reconfigurable Platforms

Lower precision for higher accuracy: Precision and resolution exploration for shallow water equations

Accelerating solvers for global atmospheric equations through mixed-precision data flow engine

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