Accelerated chemical kinetics in the EMAC chemistry-climate model

Christoudias, T.; Alvanos, Michail

doi:10.1109/hpcsim.2016.7568427

Cited by 5 publications

(9 citation statements)

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“…An earlier prototype of the application in this paper is outlined in Christoudias and Alvanos (2016), focusing on the challenges of using GPU accelerators to exploit node-level heterogeneity. This paper significantly expands on the previous work, both in detailed implementation and optimization.…”

Section: Related Developmentsmentioning

confidence: 99%

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GPU-accelerated atmospheric chemical kinetics in the ECHAM/MESSy (EMAC) Earth system model (version 2.52)

Alvanos

Christoudias

2017

Geosci. Model Dev.

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Abstract. This paper presents an application of GPU accelerators in Earth system modeling. We focus on atmospheric chemical kinetics, one of the most computationally intensive tasks in climate-chemistry model simulations. We developed a software package that automatically generates CUDA kernels to numerically integrate atmospheric chemical kinetics in the global climate model ECHAM/MESSy Atmospheric Chemistry (EMAC), used to study climate change and air quality scenarios. A source-to-source compiler outputs a CUDA-compatible kernel by parsing the FORTRAN code generated by the Kinetic PreProcessor (KPP) general analysis tool. All Rosenbrock methods that are available in the KPP numerical library are supported.Performance evaluation, using Fermi and Pascal CUDAenabled GPU accelerators, shows achieved speed-ups of 4.5× and 20.4×, respectively, of the kernel execution time. A node-to-node real-world production performance comparison shows a 1.75× speed-up over the non-accelerated application using the KPP three-stage Rosenbrock solver. We provide a detailed description of the code optimizations used to improve the performance including memory optimizations, control code simplification, and reduction of idle time. The accuracy and correctness of the accelerated implementation are evaluated by comparing to the CPU-only code of the application. The median relative difference is found to be less than 0.000000001 % when comparing the output of the accelerated kernel the CPU-only code.The approach followed, including the computational workload division, and the developed GPU solver code can potentially be used as the basis for hardware acceleration of numerous geoscientific models that rely on KPP for atmospheric chemical kinetics applications.

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Section: Related Developmentsmentioning

confidence: 99%

“…A more coarse-grained approach is to use grid-or boxlevel parallelization (Linford, 2009(Linford, , 2010Christoudias and Alvanos, 2016). The application breaks the grid or box into cells, allowing the calculation of concentrations independently between cells.…”

Section: Related Developmentsmentioning

confidence: 99%

GPU-accelerated atmospheric chemical kinetics in the ECHAM/MESSy (EMAC) Earth system model (version 2.52)

Alvanos

Christoudias

2017

Geosci. Model Dev.

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“…EMAC uses the Kinetic PreProcessor (KPP) (Sandu and Sander, 2006;Damian et al, 2002) open-source general analysis tool to formulate the chemical mechanism. KPP inte-grates very efficient numerical analysis routines and automatically generates FORTRAN and C code that computes the time evolution of chemical species from a specification of the chemical mechanism in a domain-specific language.…”

Section: The Emac Frameworkmentioning

confidence: 99%

“…guage, that transforms the MESSy chemical kinetics FOR-TRAN source code to CUDA source code, suited for running on CUDA-enabled general purpose graphics processing unit (GPGPU) accelerators. The parser transforms the autogenerated FORTRAN code by the KPP (Sandu and Sander, 2006;Damian et al, 2002) into the CUDA-compatible accelerated code, allowing to offload all different numerical integration solvers to GPU accelerators. The parser also makes the appropriate changes in the MESSy software distribution for linking the accelerated code during the compilation phase.…”

Section: Introductionmentioning

confidence: 99%

GPU accelerated atmospheric chemical kinetics in the ECHAM/MESSy (EMAC) Earth system model (version 2.52)

Alvanos¹,

Christoudias²

2017

Preprint

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Abstract. This paper presents an application of GPU accelerators in Earth system modelling. We focus on atmospheric chemical kinetics, one of the most computationally intensive tasks in climate-chemistry model simulations. We developed a software package that automatically generates CUDA kernels to numerically integrate atmospheric chemical kinetics in the global climate model ECHAM/MESSy Atmospheric Chemistry (EMAC), used to study climate change and air quality scenarios. A source-to-source compiler outputs a CUDA compatible kernel, by parsing the FORTRAN code generated by the Kinetic Pre-Processor (KPP) general analysis tool. All Rosenbrock methods that are available in the KPP numerical library are supported. Performance evaluation, using Fermi and Pascal CUDA-enabled GPU accelerators shows achieved speedups of 4.5× and 22.4× respectively of the kernel execution time. A node-to-node real-world production performance comparison shows a 1.75× speed-up over the non-accelerated application using the KPP 3-stage Rosenbrock solver. We provide a detailed description of the code optimizations used to improve the performance including memory optimizations, control code simplification, and reduction of idle time. The accuracy and correctness of the accelerated implementation are evaluated by comparing to the CPU-only version of the application. The relative difference is found to be less than 0.00005 % when comparing the output of the accelerated kernel the CPU-only code, within the target level of relative accuracy (relative error tolerance) of 0.1 %. The approach followed, including the computational workload division and the developed GPU solver code can potentially be used as the basis for hardware acceleration of numerous geoscientific models that rely on KPP for atmospheric chemical kinetics applications.

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“…To address this computational challenge, this paper presents a source-to-source parser that transforms the output of KPP from FORTRAN to GPU accelerated code by generating a CUDA [7] compatible solver [3]. The goal is to significantly improve the performance of numerical chemical kinetics (in terms of time-to-solution and problem complexity) in climate simulation models using GPU accelerators.…”

Section: Introductionmentioning

confidence: 99%

MEDINA: MECCA Development in Accelerators – KPP Fortran to CUDA source-to-source Pre-processor

Alvanos

Christoudias

2017

JORS

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The global climate model ECHAM/MESSy Atmospheric Chemistry (EMAC) is a modular global model that simulates climate change and air quality scenarios. The application includes different sub-models for the calculation of chemical species concentrations, their interaction with land and sea, and the human interaction. The paper presents a source-to-source parser that enables support for Graphics Processing Units (GPU) by the Kinetic Pre-Processor (KPP) general purpose open-source software tool. The requirements of the host system are also described. The source code of the source-to-source parser is available under the MIT License [1].Keywords: GPU; CUDA; Chemical Kinetics; Climate modeling; Atmospheric Chemistry Funding Statement: The research leading to these results has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 675121 and grant agreement No 676629. This work was also supported by the Cy-Tera Project, which is co-funded by the European Regional Development Fund and the Republic of Cyprus through the Research Promotion Foundation.Alvanos and Theodoros: MEDINA Art. 13, p. 2 of 4 up to 2 KB when indirect accesses are used. All the methods that are available in the KPP numerical library under MECCA are supported.The computation data structures are subdivided in runtime-specified arrays of columns in the atmosphere, with the memory of each array transferred to the GPU global memory and each grid box calculated on a separate GPU core to achieve massive parallelization, as shown in Figure 1. The CUDA chemical kinetics solver comprises three steps, also presented diagrammatically as a flow chart in Figure 2: 1. The first step is the calculation of the reaction rate coefficients. The variable values are stored in a global array inside the GPU and used in the computational kernels. 2. The second step is the most computationally demanding, including mostly linear algebra functions for the ODE solvers. The kernel selects the variation of the Rosenbrock solver method inside the GPU using an array of constant values in the memory. 3. The third step kernel is used for statistical reduction, and demands limited computational time compared with other kernels.There are two files required to enable the GPU utilization: i) f2c_alpha.py and ii) kpp_integrate_cuda_ prototype.cu. The pre-processor is executed by running python f2c_alpha.py in the messy/util directory. When offloading to GPUs, the number of cells must not exceed 12288. The application calculates the number of cells by multiplying the number of columns by the number of levels for the atmosphere. The user can specify the number of columns by using the NVL[1] (NPROMA) runtime parameter in the configuration of the EMAC. Quality controlTo ensure the quality of the code, we conduct unit testing by comparing the GPU accelerated with a pure Fortran simulation for one model year, using 155 species and 310 reactions. We compare the output of chemical element concentrations between the CPU only and accelerated v...

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Accelerated chemical kinetics in the EMAC chemistry-climate model

Cited by 5 publications

References 5 publications

GPU-accelerated atmospheric chemical kinetics in the ECHAM/MESSy (EMAC) Earth system model (version 2.52)

GPU-accelerated atmospheric chemical kinetics in the ECHAM/MESSy (EMAC) Earth system model (version 2.52)

GPU accelerated atmospheric chemical kinetics in the ECHAM/MESSy (EMAC) Earth system model (version 2.52)

MEDINA: MECCA Development in Accelerators – KPP Fortran to CUDA source-to-source Pre-processor

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