Multi-core acceleration of chemical kinetics for simulation and prediction

Linford, John C.; Michalakes, John; Vachharajani, Manish; Sandu, Adrian

doi:10.1145/1654059.1654067

Cited by 45 publications

(32 citation statements)

References 22 publications

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“…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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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%

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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“…The literature tends to narrowly focus on specific building blocks like basic linear algebra algorithms and numerical solvers but seldom address the complex development process for high performance computing applications comprehensively [7]. Optimizing kinetic models on emerging computer architectures is unusually difficult because expertise in chemical kinetics and modeling must be combined with a strong understanding of numerical simulation and various parallel and computer architecture paradigms [7].…”

Section: Related Workmentioning

confidence: 99%

“…Optimizing kinetic models on emerging computer architectures is unusually difficult because expertise in chemical kinetics and modeling must be combined with a strong understanding of numerical simulation and various parallel and computer architecture paradigms [7].…”

Section: Related Workmentioning

confidence: 99%

A Framework for Automated Performance Tuning and Code Verification on GPU Computing Platforms

Gehrke

Connors

2011

2011 IEEE International Symposium on Parallel and Distributed Processing Workshops and PHD Forum

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Abstract-Emerging multi-core processor designs create a computing paradigm capable of advancing numerous scientific areas, including medicine, data mining, biology, physics, and earth sciences. However, the trends in multi-core hardware technology have advanced far ahead of the advances in software technology and programmer productivity. For the most part, current scientists only leverage multi-core and GPU (Graphical Processing Unit) computing platforms after painstakingly uncovering the inherent task and data-level parallelism in their application. In many cases, the development does not realize the full potential of the parallel hardware.There exists an opportunity to meet the challenges in optimally mapping scientific application domains to multicore computer systems through the use of compile-time and link-time optimization strategies. We are exploring a code compilation framework that automatically generates and tunes numerical solver codes for optimal performance on graphical processing units. The framework advances computational simulation in kinetic modeling by significantly reducing the execution time of scientific simulations and enabling scientists to compare results to previous models and to extend, modify, and test new models without code changes.

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“…For the atmospheric chemistry models, the RADM2 chemical scheme in the WRF-CHEM model (Grell et al, 2005) was ported to different multi-core platforms. Although the limitation of the on-chip memory exists with the application, the GPU version still gets a speedup of 8.5 × when compared with its serial version (Linford et al, 2009). Similar to the GPU, the first-generation Intel Xeon Phi coprocessor (codenamed Knights Corner or KNC) was connected to the mainboard via the Peripheral Component Interface Express (PCI-E) bus (Xu et al, 2015), and the bandwidth of PCI-E has become the new performance bottleneck for some memory bandwidth-bounded software applications, e.g.…”

Section: Introductionmentioning

confidence: 99%

GNAQPMS v1.1: accelerating the Global Nested Air Quality Prediction Modeling System (GNAQPMS) on Intel Xeon Phi processors

Wang

Chen

et al. 2017

Geosci. Model Dev.

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Abstract. The Global Nested Air Quality Prediction Modeling System (GNAQPMS) is the global version of the Nested Air Quality Prediction Modeling System (NAQPMS), which is a multi-scale chemical transport model used for air quality forecast and atmospheric environmental research. In this study, we present the porting and optimisation of GNAQPMS on a second-generation Intel Xeon Phi processor, codenamed "Knights Landing" (KNL). Compared with the firstgeneration Xeon Phi coprocessor (codenamed Knights Corner, KNC), KNL has many new hardware features such as a bootable processor, high-performance in-package memory and ISA compatibility with Intel Xeon processors. In particular, we describe the five optimisations we applied to the key modules of GNAQPMS, including the CBM-Z gas-phase chemistry, advection, convection and wet deposition modules. These optimisations work well on both the KNL 7250 processor and the Intel Xeon E5-2697 V4 processor. They include (1) updating the pure Message Passing Interface (MPI) parallel mode to the hybrid parallel mode with MPI and OpenMP in the emission, advection, convection and gas-phase chemistry modules; (2) fully employing the 512 bit wide vector processing units (VPUs) on the KNL platform; (3) reducing unnecessary memory access to improve cache efficiency; (4) reducing the thread local storage (TLS) in the CBM-Z gas-phase chemistry module to improve its OpenMP performance; and (5) changing the global communication from writing/reading interface files to MPI functions to improve the performance and the parallel scalability. These optimisations greatly improved the GNAQPMS performance. The same optimisations also work well for the Intel Xeon Broadwell processor, specifically E5-2697 v4. Compared with the baseline version of GNAQPMS, the optimised version was 3.51 × faster on KNL and 2.77 × faster on the CPU. Moreover, the optimised version ran at 26 % lower average power on KNL than on the CPU. With the combined performance and energy improvement, the KNL platform was 37.5 % more efficient on power consumption compared with the CPU platform. The optimisations also enabled much further parallel scalability on both the CPU cluster and the KNL cluster scaled to 40 CPU nodes and 30 KNL nodes, with a parallel efficiency of 70.4 and 42.2 %, respectively.

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Multi-core acceleration of chemical kinetics for simulation and prediction

Cited by 45 publications

References 22 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)

A Framework for Automated Performance Tuning and Code Verification on GPU Computing Platforms

GNAQPMS v1.1: accelerating the Global Nested Air Quality Prediction Modeling System (GNAQPMS) on Intel Xeon Phi processors

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