Dr. Jarno Mielikainen Profile

Dr. Jarno Mielikainen

Research Specialist at Univ of Wisconsin-Madison

SPIE Involvement:

Author

Publications (30)

Proceedings Article | 20 October 2015 Paper

Performance tuning Weather Research and Forecasting (WRF) Goddard longwave radiative transfer scheme on Intel Xeon Phi

Jarno Mielikainen, Bormin Huang, Allen Huang

Proceedings Volume 9646, 96460H (2015) https://doi.org/10.1117/12.2196527

KEYWORDS: Radiative transfer, Atmospheric modeling, Solar radiation models, Data processing, Physics, Clouds, Solar energy, Aerosols, Computer programming, Absorption

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Proceedings Article | 20 October 2015 Paper

Revisiting Intel Xeon Phi optimization of Thompson cloud microphysics scheme in Weather Research and Forecasting (WRF) model

Jarno Mielikainen, Bormin Huang, Allen Huang

Proceedings Volume 9646, 964605 (2015) https://doi.org/10.1117/12.2196526

KEYWORDS: Computer architecture, Clouds, Data processing, Computer programming, Physics, Particles, Optimization (mathematics), Atmospheric modeling, Binary data, Parallel computing

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Proceedings Article | 21 May 2015 Paper

Optimizing the updated Goddard shortwave radiation Weather Research and Forecasting (WRF) scheme for Intel Many Integrated Core (MIC) architecture

Jarno Mielikainen, Bormin Huang, Allen H.-L. Huang

Proceedings Volume 9501, 95010E (2015) https://doi.org/10.1117/12.2180027

KEYWORDS: Shortwaves, Radiative transfer, Atmospheric modeling, Solar radiation models, Physics, Absorption, Computer programming, Parallel computing, Carbon dioxide, Clouds

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Proceedings Article | 21 May 2015 Paper

Optimizing meridional advection of the Advanced Research WRF (ARW) dynamics for Intel Xeon Phi coprocessor

Jarno Mielikainen, Bormin Huang, Allen H.-L. Huang

Proceedings Volume 9501, 95010Y (2015) https://doi.org/10.1117/12.2180029

KEYWORDS: Physics, Atmospheric modeling, Computer programming, Computer simulations, Profiling, Parallel computing, Data modeling, Computer architecture, Climatology, Clouds

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Proceedings Article | 21 October 2014 Paper

Intel Many Integrated Core (MIC) architecture optimization strategies for a memory-bound Weather and Research Forecasting (WRF) Goddard microphysics scheme

Jarno Mielikainen, Bormin Huang, Allen Huang

Proceedings Volume 9247, 924704 (2014) https://doi.org/10.1117/12.2068358

KEYWORDS: Physics, Clouds, Computer programming, Parallel computing, Atmospheric modeling, Operating systems, Computer architecture, Acoustics, Binary data, Data processing

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Proceedings Article | 21 October 2014 Paper

Optimizing zonal advection of the Advanced Research WRF (ARW) dynamics for Intel MIC

Jarno Mielikainen, Bormin Huang, Allen Huang

Proceedings Volume 9247, 92470M (2014) https://doi.org/10.1117/12.2069314

KEYWORDS: Physics, Atmospheric modeling, Computer simulations, Computer programming, Profiling, Parallel computing, Data modeling, Computer architecture, Climatology, Clouds

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Proceedings Article | 21 October 2014 Paper

Initial results on computational performance of Intel Many Integrated Core (MIC) architecture: implementation of the Weather and Research Forecasting (WRF) Purdue-Lin microphysics scheme

Jarno Mielikainen, Bormin Huang, Allen Huang

Proceedings Volume 9247, 92470C (2014) https://doi.org/10.1117/12.2068360

KEYWORDS: Clouds, Physics, Computer architecture, Computer programming, Parallel computing, Atmospheric modeling, Particles, Acoustics, Binary data, Data processing

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Proceedings Article | 28 May 2014 Paper

Using Intel Xeon Phi to accelerate the WRF TEMF planetary boundary layer scheme

Jarno Mielikainen, Bormin Huang, Allen Huang

Proceedings Volume 9124, 91240T (2014) https://doi.org/10.1117/12.2055040

KEYWORDS: Atmospheric modeling, Physics, Atmospheric physics, Computer programming, Clouds, Turbulence, Convection, Parallel computing, Computer architecture, Acoustics

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Proceedings Article | 28 May 2014 Paper

Optimizing Weather and Research Forecast (WRF) Thompson cloud microphysics on Intel Many Integrated Core (MIC)

Jarno Mielikainen, Bormin Huang, Allen Huang

Proceedings Volume 9124, 91240Q (2014) https://doi.org/10.1117/12.2055038

KEYWORDS: Clouds, Physics, Computer programming, Particles, Atmospheric modeling, Parallel computing, Computer architecture, Acoustics, Binary data, Atmospheric physics

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Proceedings Article | 23 October 2013 Paper

GPU acceleration experience with RRTMG long wave radiation model

Erik Price, Jarno Mielikainen, Bormin Huang, HungLung Huang, Tsengdar Lee

Proceedings Volume 8895, 88950H (2013) https://doi.org/10.1117/12.2031450

KEYWORDS: Atmospheric modeling, Radiative transfer, Clouds, Earth's atmosphere, Climatology, Shortwaves, Absorption, Computer programming, Electromagnetic radiation, Liquids

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An Atmospheric radiative transfer model calculates radiative transfer of electromagnetic radiation through a planetary atmosphere. Both shortwave radiance and longwave radiance parameterizations in an atmospheric model calculate radiation fluxes and heating rates in the earth-atmospheric system. One radiative transfer model is the rapid radiative transfer model (RRTM), which calculates of longwave and shortwave atmospheric radiative fluxes and heating rates. Longwave broadband radiative transfer code for general circulation model (GCM) applications, RRTMG, is based on the single-column reference code, RRTM. The RRTMG is a validated, correlated k-distribution band model for the calculation of longwave and shortwave atmospheric radiative fluxes and heating rates. The focus of this paper is on the RRTMG long wave (RRTMG_LW) model. In order to improve computational efficiency, RRTMG_LW incorporates several modifications compared to RRTM. In RRTM_LW there are 16 g points in each of the spectral bands for a total of 256 g points. In RRTMG_LW, the number of g points in each spectral band varies from 2 to 16 depending on the absorption in each band. RRTMG_LW employs a computationally efficient correlated-k method for radiative transfer calculations. It contains 16 spectral bands with various number of quadrature points (g points) in each of the bands. In total, there are 140 g points. The radiative effects of all significant atmospheric gases are included in RRTMG_LW. Active gas absorbers include H₂O, O₃, CO₂, CH₄, N₂O, O₂ and four types of halocarbons: CFC-11, CFC-12, CFC-22, and CCL4. RRTMG_LW also treats the absorption and scattering from liquid and ice clouds and aerosols. For cloudysky radiative transfer, a maximum-random cloud overlapping scheme is used. Small scale cloud variability, such as cloud fraction and the vertical overlap of clouds can be represented using a statistical technique in RRTMG_LW. Due to its accuracy, RRTMG_LW has been implemented operationally in many weather forecast and climate models. RRTMG_LW is in operational use in ECMWF weather forecast system, the NCEP global forecast system, the ECHAM5 climate model, Community Earth System Model (CESM) and the weather and forecasting (WRF) model. RRTMG_LW has also been evaluated for use in GFDL climate model. In this paper, we examine the feasibility of using graphics processing units (GPUs) to accelerate the RRTMG_LW as used by the WRF. GPUs can provide a substantial improvement in RRTMG speed by supporting the parallel computation of large numbers of independent radiative calculations. Furthermore, using commodity GPUs for accelerating RRTMG_LW allows getting a much higher computational performance at lower price point than traditional CPUs. Furthermore, power and cooling costs are significantly reduced by using GPUs. A GPU-compatible version of RRTMG was implemented and thorough testing was performed to ensure that the original level of accuracy is retained. Our results show that GPUs can provide significant speedup over conventional CPUs. In particular, Nvidia’s GTX 680 GPU card can provide a speedup of 69x for the compared to its single-threaded Fortran counterpart running on Intel Xeon E5-2603 CPU.

Proceedings Article | 8 November 2012 Paper

GPU-parallel performance of the community radiative transfer model (CRTM) with the optical depth in absorber space (ODAS)-based transmittance algorithm

Jarno Mielikainen, Bormin Huang, Hung-Lung Huang, Tsengdar Lee

Proceedings Volume 8539, 853903 (2012) https://doi.org/10.1117/12.979077

KEYWORDS: Transmittance, Atmospheric modeling, Radiative transfer, Absorption, Earth's atmosphere, Atmospheric optics, Thermal modeling, Computer programming, Performance modeling, Infrared radiation

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Proceedings Article | 8 November 2012 Paper

On the acceleration of Eta Ferrier Cloud Microphysics Scheme in the Weather Research and Forecasting (WRF) model using a GPU

Melin Huang, Jarno Mielikainen, Bormin Huang, H.-L. Huang, Mitchell Goldberg

Proceedings Volume 8539, 85390K (2012) https://doi.org/10.1117/12.976908

KEYWORDS: Clouds, Data modeling, Humidity, Atmospheric modeling, Visualization, Monte Carlo methods, 3D modeling, Liquids, Clocks, Meteorology

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Proceedings Article | 8 November 2012 Paper

GPU-based parallel implementation of 5-layer thermal diffusion scheme

Melin Huang, Jarno Mielikainen, Bormin Huang, H.-L. Huang, Mitchell Goldberg

Proceedings Volume 8539, 853908 (2012) https://doi.org/10.1117/12.978991

KEYWORDS: Diffusion, Atmospheric physics, Thermal modeling, Atmospheric modeling, Graphics processing units, Data processing, Data modeling, Atmospheric propagation, Computer simulations, Heat flux

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Proceedings Article | 12 October 2011 Paper

Development of the GPU-based Stony-Brook University 5-class microphysics scheme in the weather research and forecasting model

Jarno Mielikainen, Bormin Huang, Allen H.-L. Huang, Mitchell Goldberg

Proceedings Volume 8183, 81830V (2011) https://doi.org/10.1117/12.901829

KEYWORDS: Clouds, Computer simulations, Manufacturing, Atmospheric modeling, Physics, Crystals, Graphics processing units, Technetium, Computer architecture, Atmospheric physics

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Proceedings Article | 12 October 2011 Paper

GPU acceleration of WRF WSM5 microphysics

Jarno Mielikainen, Bormin Huang, Allen H.-L. Huang, Mitchell Goldberg

Proceedings Volume 8183, 81830S (2011) https://doi.org/10.1117/12.901826

KEYWORDS: Clouds, Thermodynamics, Graphics processing units, Crystals, Thermal effects, Manufacturing, Atmospheric modeling, Physics, Meteorology, Artificial intelligence

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Proceedings Article | 21 September 2011 Paper

Massively parallelizing the CIMSS IASI radiative transfer model on GPUs

Jarno Mielikainen, Bormin Huang, Allen H.-L. Huang

Proceedings Volume 8157, 81570G (2011) https://doi.org/10.1117/12.895302

KEYWORDS: Radiative transfer, Transmittance, Atmospheric modeling, Carbon monoxide, Satellites, Infrared radiation, Ozone, Graphics processing units, Meteorological satellites, Methane

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Proceedings Article | 17 September 2011 Paper

Low complexity pixel-based halftone detection

Jiheon Ok, Seong Wook Han, Mielikainen Jarno, Chulhee Lee

Proceedings Volume 8157, 81570N (2011) https://doi.org/10.1117/12.895089

KEYWORDS: Halftones, Printing, Electroluminescence, Neodymium, Scanners, Image segmentation, Internet, Multimedia, Field emission displays, Detection and tracking algorithms

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Proceedings Article | 17 September 2011 Paper

Real-time lossless compression for HDTV video using a GPGPU

Guiwon Seo, Mielikainen Jarno, Sungwook Youn, Chulhee Lee

Proceedings Volume 8157, 81570Z (2011) https://doi.org/10.1117/12.894987

KEYWORDS: Video compression, Video, Computer programming, Video acceleration, Image compression, Parallel processing, Video coding, Video processing, Image processing, Databases

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SPIE Journal Paper | 1 January 2011

Accelerating the RTTOV-7 IASI and AMSU-A radiative transfer models on graphics processing units: evaluating central processing unit/graphics processing unit-hybrid and pure-graphics processing unit approaches

Jarno Mielikainen, Bormin Huang, Hung-Lung Huang, Roger Saunders

JARS, Vol. 5, Issue 1, 051503, (January 2011) https://doi.org/10.1117/12.10.1117/1.3658028

KEYWORDS: Radiative transfer, Atmospheric modeling, Graphics processing units, Infrared radiation, Thermal modeling, Data modeling, Clouds, Transmittance, Computer programming, Satellites

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The radiative transfer for television operational vertical sounder (RTTOV) is a widely-used radiative transfer model (RTM) for calculation of radiances for satellite infrared and microwave sensors, including the 8461-channel infrared atmospheric sounding interferometer (IASI) and the 15-band Advanced Microwave Sounding Unit-A (AMSU-A). In the era of hyperspectral sounders with thousands of spectral channels, the computation of the RTM becomes more time-consuming. The RTM performance in operational numerical weather prediction systems still limits the number of used channels in hyperspectral sounders to only a few hundred. To take full advantage of such high-resolution infrared observations, a computationally efficient radiative transfer model is needed to facilitate satellite data assimilation. In this paper, we develop the parallel implementation of the RTTOV-7 IASI and AMSU-A RTMs to run the predictor module on CPUs in pipeline with the transmittance and radiance modules on NVIDIA many-core graphics processing units (GPUs). We show that concurrent execution of RTTOV-7 IASI RTM on CPU and GPU, in addition to asynchronous data transfer from CPU to GPU, allows the GPU accelerated code running on the 240-core NVIDIA Tesla C1060 to reach a speedup of 461× and 1793× for 1- and 4-GPU configurations, respectively. To compute one day's amount of 1,296,000 IASI spectra, the CPU code running on the host AMD Phenom II X4 940 CPU core with 3.0 GHz will take 2.8 days. Thus, GPU acceleration reduced running time to 8.75 and 2.25 min on 1- and 4-GPU configurations, respectively. Speedup for the RTTOV AMSU-A RTM varied from 29× to 75× for 1 and 4 GPUs, respectively.To further boost the speedup of a multispectral RTM, we developed a novel pure-GPU version of the RTTOV AMSU-A RTM where the predictor module also runs on GPUs to achieve a 96% reduction in the host-to-device data transfer. The speedups for the pure-GPU AMSU-A RTM are significantly increased to 56× and 125× for 1- and 4-GPU configurations, respectively.

SPIE Journal Paper | 1 September 2010

Constant coefficients linear prediction for lossless compression of ultraspectral sounder data using a graphics processing unit

Jarno Mielikainen, Risto Honkanen, Bormin Huang, Pekka Toivanen, Chulhee Lee

JARS, Vol. 4, Issue 01, 041774, (September 2010) https://doi.org/10.1117/12.10.1117/1.3496907

KEYWORDS: Image compression, Graphics processing units, Data compression, Computer programming, Visualization, Image processing, Data storage, Matrices, Computer architecture, Clocks

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Proceedings Article | 24 August 2010 Paper

Clustered linear prediction for lossless compression of hyperspectral images using adaptive prediction length

Jarno Mielikainen

Proceedings Volume 7810, 78100M (2010) https://doi.org/10.1117/12.863227

KEYWORDS: Hyperspectral imaging, Image compression, Satellite communications, Satellites, Data compression, Data communications, Lithium, Current controlled current source, Modulation, Calibration

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Proceedings Article | 24 August 2010 Paper

Accelerating the RTTOV-7 radiative transfer model on graphics processing units

Jarno Mielikainen, Bormin Huang, Allen Huang, Roger Saunders

Proceedings Volume 7810, 781012 (2010) https://doi.org/10.1117/12.863272

KEYWORDS: Radiative transfer, Atmospheric modeling, Graphics processing units, Thermal modeling, Clouds, Infrared radiation, Transmittance, Ocean optics, Interferometers, Computer architecture

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Proceedings Article | 31 August 2009 Paper

GPUs for data parallel spectral image compression

Jarno Mielikainen, Risto Honkanen, Pekka Toivanen, Bormin Huang

Proceedings Volume 7455, 74550C (2009) https://doi.org/10.1117/12.828135

KEYWORDS: Data compression, Image compression, Data storage, Computer architecture, Computer programming, Satellite communications, Satellites, Data communications, Current controlled current source, Imaging systems

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Proceedings Article | 5 September 2008 Paper

Optimal granule ordering for lossless compression of ultraspectral sounder data

Jarno Mielikainen, Pekka Toivanen

Proceedings Volume 7084, 708404 (2008) https://doi.org/10.1117/12.795260

KEYWORDS: Image compression, Data compression, Infrared radiation, Quantization, Algorithm development, Principal component analysis, Hyperspectral imaging, Electroluminescence, Computer science, Atmospheric sensing

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SPIE Journal Paper | 1 January 2005

Parallel implementation of linear prediction model for lossless compression of hyperspectral airborne visible infrared imaging spectrometer images

Jarno Mielikäinen, Pekka Toivanen

JEI, Vol. 14, Issue 01, 013010, (January 2005) https://doi.org/10.1117/12.10.1117/1.1867998

KEYWORDS: Image compression, Chromium, Spectroscopy, Hyperspectral imaging, Visible radiation, Infrared imaging, Infrared spectroscopy, Thermal modeling, Image processing, Information technology

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SPIE Journal Paper | 1 April 2003

Linear prediction in lossless compression of hyperspectral images

Jarno Mielikainen, Pekka Toivanen, Arto Kaarna

OE, Vol. 42, Issue 04, (April 2003) https://doi.org/10.1117/12.10.1117/1.1557174

KEYWORDS: Image compression, Hyperspectral imaging, Principal component analysis, Quantization, Wavelet transforms, Optical engineering, Chromium, Information technology, Spectroscopy, Algorithms

Proceedings Article | 13 March 2003 Paper

Improvement to an interband version of the linear prediction approach for hyperspectral image compression

Jarno Mielikainen, Pekka Toivanen

Proceedings Volume 4885, (2003) https://doi.org/10.1117/12.463164

KEYWORDS: Image compression, Chromium, Hyperspectral imaging, Principal component analysis, Quantization, Wavelet transforms, Spectroscopy, Algorithms, Image sensors, Information technology

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Proceedings Article | 13 March 2003 Paper

Noise reduction methods for hyperspectral images

Pekka Toivanen, Arto Kaarna, Jarno Mielikainen, Mikko Laukkanen

Proceedings Volume 4885, (2003) https://doi.org/10.1117/12.463167

KEYWORDS: Digital filtering, Denoising, Optical filters, Image filtering, Gaussian filters, Filtering (signal processing), Hyperspectral imaging, Edge detection, Data modeling, Image processing

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Proceedings Article | 2 August 2002 Paper

Lossless hyperspectral image compression via linear prediction

Jarno Mielikainen, Arto Kaarna, Pekka Toivanen

Proceedings Volume 4725, (2002) https://doi.org/10.1117/12.478794

KEYWORDS: Image compression, Principal component analysis, Hyperspectral imaging, Quantization, Spectroscopy, Wavelet transforms, Statistical analysis, Information technology, Multispectral imaging, Image storage

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Proceedings Article | 2 August 2002 Paper

Noise reduction in multispectral images using the self-organizing map

Pekka Toivanen, Mikko Laukkanen, Arto Kaarna, Jarno Mielikainen

Proceedings Volume 4725, (2002) https://doi.org/10.1117/12.478751

KEYWORDS: Image filtering, Digital filtering, Denoising, Gaussian filters, Multispectral imaging, Fourier transforms, Nonlinear filtering, Optical filters, Brain mapping, Neurons

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Showing 5 of 30 publications

Conference Committee Involvement (18)

High-Performance Computing in Geoscience and Remote Sensing

13 September 2018 | Berlin, Germany

High-Performance Computing in Geoscience and Remote Sensing

12 September 2017 | Warsaw, Poland

High-Performance Computing in Geoscience and Remote Sensing

28 September 2016 | Edinburgh, United Kingdom

Remotely Sensed Data Compression, Communications, and Processing XII

20 April 2016 | Baltimore, MD, United States

High-Performance Computing in Remote Sensing

21 September 2015 | Toulouse, France

Satellite Data Compression, Communications, and Processing XI

23 April 2015 | Baltimore, MD, United States

High-Performance Computing in Remote Sensing

22 September 2014 | Amsterdam, Netherlands

Satellite Data Compression, Communications, and Processing X

8 May 2014 | Baltimore, MD, United States

High-Performance Computing in Remote Sensing III

25 September 2013 | Dresden, Germany

Satellite Data Compression, Communications, and Processing IX

26 August 2013 | San Diego, California, United States

High-Performance Computing in Remote Sensing

26 September 2012 | Edinburgh, United Kingdom

Satellite Data Compression, Communications, and Processing VIII

12 August 2012 | San Diego, California, United States

High-Performance Computing in Remote Sensing

19 September 2011 | Prague, Czech Republic

Satellite Data Compression, Communications, and Processing VII

23 August 2011 | San Diego, California, United States

Satellite Data Compression, Communications, and Processing VI

3 August 2010 | San Diego, California, United States

Satellite Data Compression, Communication, and Processing V

4 August 2009 | San Diego, California, United States

Satellite Data Compression, Communication, and Processing IV

10 August 2008 | San Diego, California, United States

Satellite Data Compression, Communications, and Archiving

31 July 2005 | San Diego, California, United States

Showing 5 of 18 Conference Committees

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