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Journal articles on the topic 'Commodity hardware'

Author: Grafiati

Published: 4 June 2021

Last updated: 6 September 2023

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1

TIAN, Donghai, Mo CHEN, Changzhen HU, and Xuanya LI. "Efficient Shellcode Detection on Commodity Hardware." IEICE Transactions on Information and Systems E96.D, no. 10 (2013): 2272–76. http://dx.doi.org/10.1587/transinf.e96.d.2272.

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Nightingale, Edmund B., Daniel Peek, Peter M. Chen, and Jason Flinn. "Parallelizing security checks on commodity hardware." ACM SIGARCH Computer Architecture News 36, no. 1 (March 25, 2008): 308–18. http://dx.doi.org/10.1145/1353534.1346321.

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Nightingale, Edmund B., Daniel Peek, Peter M. Chen, and Jason Flinn. "Parallelizing security checks on commodity hardware." ACM SIGOPS Operating Systems Review 42, no. 2 (March 25, 2008): 308–18. http://dx.doi.org/10.1145/1353535.1346321.

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Nightingale, Edmund B., Daniel Peek, Peter M. Chen, and Jason Flinn. "Parallelizing security checks on commodity hardware." ACM SIGPLAN Notices 43, no. 3 (March 25, 2008): 308–18. http://dx.doi.org/10.1145/1353536.1346321.

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Henry, Patrick J., and Patrick J. Henry. "Implant Hardware— Science or Commodity Development?" Journal of Dental Research 74, no. 1 (January 1995): 301–2. http://dx.doi.org/10.1177/00220345950740010201.

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6

Handigolkar, Lata S., M. L. Kavya, and P. D. Veena. "Iot Based Smart Poultry Farming using Commodity Hardware and Software." Bonfring International Journal of Software Engineering and Soft Computing 6, Special Issue (October 31, 2016): 171–75. http://dx.doi.org/10.9756/bijsesc.8269.

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7

Ghosh, Saibal, and Dharma Agrawal. "A Scalable Cloud Based on Commodity Hardware." Network Protocols and Algorithms 8, no. 4 (January 15, 2017): 72. http://dx.doi.org/10.5296/npa.v8i4.10291.

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The recent explosion in the speed and connectivity of the Internet has opened up the possibility of millions and possibly billions of devices connected together. Combined with the development of small, low power devices, new paradigms in the field of computing have opened up. Traditional passive electronic devices now have rudimentary computing capabilities. The resulting Internet of Things (IoT), comprised of smart interconnected devices is improving our ability to gather ambient information and make informed decisions that directly benefit humanity. However, the ubiquity of these devices also presents an interesting scenario wherein the devices can perform limited general-purpose computations when they are not performing their primary functions. A computational task divided into a large number of smaller, micro tasks, each of which take only a few CPU cycles to complete. By distributing these tasks over a large number of devices, we can achieve a substantial amount of computation with seemingly modest devices. In this work, we explore a mechanism to enable such massively parallel computations in low powered commodity hardware devices through fine-grained task parallelism.

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Szabo, Geza, Istvan Godor, Andras Veres, Szabolcs Malomsoky, and Sandor Molnar. "Traffic Classification over Gbit Speed with Commodity Hardware." Journal of Communications Software and Systems 5, no. 3 (September 21, 2009): 93. http://dx.doi.org/10.24138/jcomss.v5i3.203.

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This paper discusses necessary components of a GPU-assisted traffic classification method, which is capable ofmulti-Gbps speeds on commodity hardware. The majority of the traffic classification is pushed to the GPU to offload the CPU, which then may serve other processing intensive tasks, e.g., traffic capture. The paper presents two massively parallelizable algorithms suitable for GPUs. The first one performs signature search using a modification of Zobrist hashing. The second algorithm supports connection pattern-based analysis and aggregation of matches using a parallel-prefix-sum algorithm adapted to GPU.The performance tests of the proposed methods showed that traffic classification is possible up to approximately 6 Gbps with a commodity PC.

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Ahrenberg, Lukas, Philip Benzie, Marcus Magnor, and John Watson. "Computer generated holography using parallel commodity graphics hardware." Optics Express 14, no. 17 (August 21, 2006): 7636. http://dx.doi.org/10.1364/oe.14.007636.

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Setoain, Javier, Manuel Prieto, Christian Tenllado, Antonio Plaza, and Francisco Tirado. "Parallel Morphological Endmember Extraction Using Commodity Graphics Hardware." IEEE Geoscience and Remote Sensing Letters 4, no. 3 (July 2007): 441–45. http://dx.doi.org/10.1109/lgrs.2007.897398.

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Mishra, Varun, Gunnar Pope, Sarah Lord, Stephanie Lewia, Byron Lowens, Kelly Caine, Sougata Sen, Ryan Halter, and David Kotz. "Continuous Detection of Physiological Stress with Commodity Hardware." ACM Transactions on Computing for Healthcare 1, no. 2 (April 15, 2020): 1–30. http://dx.doi.org/10.1145/3361562.

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12

Yang, Ruigang, and Marc Pollefeys. "A versatile stereo implementation on commodity graphics hardware." Real-Time Imaging 11, no. 1 (February 2005): 7–18. http://dx.doi.org/10.1016/j.rti.2005.04.002.

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Casper, Jared, Tayo Oguntebi, Sungpack Hong, Nathan G. Bronson, Christos Kozyrakis, and Kunle Olukotun. "Hardware acceleration of transactional memory on commodity systems." ACM SIGARCH Computer Architecture News 39, no. 1 (March 17, 2011): 27–38. http://dx.doi.org/10.1145/1961295.1950372.

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Casper, Jared, Tayo Oguntebi, Sungpack Hong, Nathan G. Bronson, Christos Kozyrakis, and Kunle Olukotun. "Hardware acceleration of transactional memory on commodity systems." ACM SIGPLAN Notices 46, no. 3 (March 17, 2011): 27–38. http://dx.doi.org/10.1145/1961296.1950372.

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Casper, Jared, Tayo Oguntebi, Sungpack Hong, Nathan G. Bronson, Christos Kozyrakis, and Kunle Olukotun. "Hardware acceleration of transactional memory on commodity systems." ACM SIGPLAN Notices 47, no. 4 (June 1, 2012): 27. http://dx.doi.org/10.1145/2248487.1950372.

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16

Bencivenni, Marco, Daniela Bortolotti, Angelo Carbone, Alessandro Cavalli, Andrea Chierici, Stefano Dal Pra, Donato De Girolamo, et al. "Performance of 10 Gigabit Ethernet Using Commodity Hardware." IEEE Transactions on Nuclear Science 57, no. 2 (April 2010): 630–41. http://dx.doi.org/10.1109/tns.2009.2032264.

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Bletsas, Aggelos, and Andy Lippman. "Implementing cooperative diversity antenna arrays with commodity hardware." IEEE Communications Magazine 44, no. 12 (December 2006): 33–40. http://dx.doi.org/10.1109/mcom.2006.273097.

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18

Winfree, Kyle N., and Gregory Dominick. "Modeling Clinically Validated Physical Activity Assessments Using Commodity Hardware." IEEE Journal of Biomedical and Health Informatics 22, no. 2 (March 2018): 335–45. http://dx.doi.org/10.1109/jbhi.2017.2787461.

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19

Kaufman, Arie, Zhe Fan, and Kaloian Petkov. "Implementing the lattice Boltzmann model on commodity graphics hardware." Journal of Statistical Mechanics: Theory and Experiment 2009, no. 06 (June 18, 2009): P06016. http://dx.doi.org/10.1088/1742-5468/2009/06/p06016.

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20

Yang, Ruigang, and Greg Welch. "Fast Image Segmentation and Smoothing Using Commodity Graphics Hardware." Journal of Graphics Tools 7, no. 4 (January 2002): 91–100. http://dx.doi.org/10.1080/10867651.2002.10487576.

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21

Ren, Bin, Youngjoon Jo, Sriram Krishnamoorthy, Kunal Agrawal, and Milind Kulkarni. "Efficient execution of recursive programs on commodity vector hardware." ACM SIGPLAN Notices 50, no. 6 (August 7, 2015): 509–20. http://dx.doi.org/10.1145/2813885.2738004.

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22

Nguyen, Viet Anh, Jiangbo Lu, Shengkui Zhao, Douglas L. Jones, and Minh N. Do. "Teleimmersive Audio-Visual Communication Using Commodity Hardware [Applications Corner]." IEEE Signal Processing Magazine 31, no. 6 (November 2014): 118–36. http://dx.doi.org/10.1109/msp.2014.2340232.

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23

Greenhalgh, Adam, Felipe Huici, Mickael Hoerdt, Panagiotis Papadimitriou, Mark Handley, and Laurent Mathy. "Flow processing and the rise of commodity network hardware." ACM SIGCOMM Computer Communication Review 39, no. 2 (March 31, 2009): 20–26. http://dx.doi.org/10.1145/1517480.1517484.

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24

Stensland, Håkon Kvale, Martin Alexander Wilhelmsen, Vamsidhar Reddy Gaddam, Asgeir Mortensen, Ragnar Langseth, Carsten Griwodz, and Pål Halvorsen. "Using a Commodity Hardware Video Encoder for Interactive Applications." International Journal of Multimedia Data Engineering and Management 6, no. 3 (July 2015): 17–31. http://dx.doi.org/10.4018/ijmdem.2015070102.

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Over the last years, video streaming has become one of the most dominant Internet services. Due to the increased availability of high-speed Internet access, multimedia services are becoming more interactive. Examples of such applications are both cloud gaming (OnLive, 2014) and systems where users can interact with high-resolution content (Gaddam et al., 2014). During the last few years, programmable hardware video encoders have been built into commodity hardware such as CPUs and GPUs. One of these encoders is evaluated in a scenario where individual streams are delivered to the end users. The results show that the visual video quality and the frame size of the hardware-based encoder are comparable to a software-based approach. To evaluate a complete system, a proposed streaming pipeline has been implemented into Quake III. It was found that running the game on a remote server and streaming the video output to a client web browser located in a typical home environment is possible and enjoyable. The interaction latency is measured to be less than 90 ms, which is below what is reported for OnLive in a similar environment

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25

Serrano, Pablo, Pablo Salvador, Vincenzo Mancuso, and Yan Grunenberger. "Experimenting With Commodity 802.11 Hardware: Overview and Future Directions." IEEE Communications Surveys & Tutorials 17, no. 2 (2015): 671–99. http://dx.doi.org/10.1109/comst.2015.2417493.

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26

Dodoo, Emma R., Brittney Hill, Austin Garcia, Adam Kohl, Anastacia MacAllister, Jonathan Schlueter, and Eliot Winer. "Evaluating Commodity Hardware and Software for Virtual Reality Assembly Training." Electronic Imaging 2018, no. 3 (January 28, 2018): 468–1. http://dx.doi.org/10.2352/issn.2470-1173.2018.03.ervr-468.

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27

Bartoň, Radek. "Modern Algorithms for Real-Time Terrain Visualization on Commodity Hardware." Geoinformatics FCE CTU 5 (May 7, 2010): 5–22. http://dx.doi.org/10.14311/gi.5.1.

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The amount of input data acquired from a remote sensing equipment is rapidly growing. Interactive visualization of those datasets is a necessity for their correct interpretation. With the ability of modern hardware to display hundreds of millions of triangles per second, it is possible to visualize the massive terrains at one pixel display error on HD displays with interactive frame rates when batched rendering is applied. Algorithms able to do this are an area of intensive research and a topic of this article. The paper first explains some of the theory around the terrain visualization, categorizes its algorithms according to several criteria and describes six of the most significant methods in more details.

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28

Diegues, Nuno, Paolo Romano, and Luís Rodrigues. "On the energy and performance of commodity hardware transactional memory." ACM SIGMETRICS Performance Evaluation Review 42, no. 1 (June 20, 2014): 547–48. http://dx.doi.org/10.1145/2637364.2592030.

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29

Centioli, C., G. Bracco, S. Eccher, F. Iannone, A. Maslennikov, M. Panella, and V. Vitale. "Commodity hardware and open source solutions in FTU data management." Fusion Engineering and Design 71, no. 1-4 (June 2004): 195–200. http://dx.doi.org/10.1016/j.fusengdes.2004.04.033.

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30

Cai, Chao, Zhen Chen, Jun Luo, Linwei Zhu, and Menglan Hu. "HackMan: hacking commodity millimeter-wave hardware for a measurement study." Wireless Networks 26, no. 7 (June 27, 2020): 5411–25. http://dx.doi.org/10.1007/s11276-020-02402-3.

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31

Yang, Ruigang, Greg Welch, and Gary Bishop. "Real-Time Consensus-Based Scene Reconstruction Using Commodity Graphics Hardware+." Computer Graphics Forum 22, no. 2 (June 2003): 207–16. http://dx.doi.org/10.1111/1467-8659.00661.

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32

Sorensen, T. S., T. Schaeffter, K. O. Noe, and M. S. Hansen. "Accelerating the Nonequispaced Fast Fourier Transform on Commodity Graphics Hardware." IEEE Transactions on Medical Imaging 27, no. 4 (April 2008): 538–47. http://dx.doi.org/10.1109/tmi.2007.909834.

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33

Gallo, Luigi, and Alessio Pierluigi Placitelli. "High-Fidelity Visualization of Large Medical Datasets on Commodity Hardware." ISRN Biomedical Engineering 2013 (June 27, 2013): 1–9. http://dx.doi.org/10.1155/2013/892967.

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Recent advances in CT and MRI static and dynamic scanning techniques have led to great improvements in the resolution and size of volumetric medical datasets, and this trend is still ongoing. However, the explosion of dataset size prevents clinicians from taking advantage of an interactive, high-resolution exploration of volumetric medical data on commodity hardware, due to the memory constraints of modern graphics cards. This paper presents a hybrid CPU-GPU volume ray-casting method and some hybrid-based inspection tools aimed at providing interactive, medical-quality visualization using an ordinary desktop PC. Experimental results show that the hybrid method provides a near-interactive high-fidelity visualization of large medical datasets even if only limited hardware resources are available.

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34

Xu, Fang, and Klaus Mueller. "Real-time 3D computed tomographic reconstruction using commodity graphics hardware." Physics in Medicine and Biology 52, no. 12 (May 17, 2007): 3405–19. http://dx.doi.org/10.1088/0031-9155/52/12/006.

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35

Barczyk, A., D. Bortolotti, A. Carbone, J. P. Dufey, D. Galli, B. Gaidioz, D. Gregori, et al. "High rate packets transmission on ethernet LAN using commodity hardware." IEEE Transactions on Nuclear Science 53, no. 3 (June 2006): 810–16. http://dx.doi.org/10.1109/tns.2006.874546.

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36

Fang Xu and K. Mueller. "Accelerating popular tomographic reconstruction algorithms on commodity PC graphics hardware." IEEE Transactions on Nuclear Science 52, no. 3 (June 2005): 654–63. http://dx.doi.org/10.1109/tns.2005.851398.

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37

Hansen, Michael S., David Atkinson, and Thomas S. Sorensen. "Cartesian SENSE andk-t SENSE reconstruction using commodity graphics hardware." Magnetic Resonance in Medicine 59, no. 3 (March 2008): 463–68. http://dx.doi.org/10.1002/mrm.21523.

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38

Ahrenberg, Lukas, Andrew J. Page, Bryan M. Hennelly, John B. McDonald, and Thomas J. Naughton. "Using Commodity Graphics Hardware for Real-Time Digital Hologram View-Reconstruction." Journal of Display Technology 5, no. 4 (April 2009): 111–19. http://dx.doi.org/10.1109/jdt.2009.2013159.

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39

Khawla, Tadist, Mrabti Fatiha, Zahi Azeddine, and Najah Said. "A Blast implementation in Hadoop MapReduce using low cost commodity hardware." Procedia Computer Science 127 (2018): 69–75. http://dx.doi.org/10.1016/j.procs.2018.01.099.

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40

Suriansyah, Mohamad Iqbal, Heru Sukoco, and Mohamad Solahudin. "Weed Detection Using Fractal-Based Low Cost Commodity Hardware Raspberry Pi." Indonesian Journal of Electrical Engineering and Computer Science 2, no. 2 (May 1, 2016): 426. http://dx.doi.org/10.11591/ijeecs.v2.i2.pp426-430.

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Conventional weed control system is usually used by spraying herbicides uniformly throughout the land. Excessive use of herbicides on an ongoing basis can produce chemical waste that is harmful to plants and soil. The application of precision agriculture farming in the detection process in order to control weeds using Computer Vision On Farm becomes interesting, but it still has some problems due to computer size and power consumption. Raspberry Pi is one of the minicomputer with low price and low power consumption. Having computing like a desktop computer with the open source Linux operating system can be used for image processing and weed fractal dimension processing using OpenCV library and C programming. This research results the best fractal computation time when performing the image with dimension size of 128 x 128 pixels. It is about 7 milliseconds. Furthermore, the average speed ratio between personal computer and Raspberry Pi is 0.04 times faster. The use of Raspberry Pi is cost and power consumption efficient compared to personal computer.

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41

Dimitriou, Antonis G., Aggelos Bletsas, and John N. Sahalos. "Room-Coverage Improvements in UHF RFID with Commodity Hardware [Wireless Corner]." IEEE Antennas and Propagation Magazine 53, no. 1 (February 2011): 175–94. http://dx.doi.org/10.1109/map.2011.5773609.

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42

Gutiérrez, Pablo D., Miguel Lastra, José M. Benítez, and Francisco Herrera. "SMOTE-GPU: Big Data preprocessing on commodity hardware for imbalanced classification." Progress in Artificial Intelligence 6, no. 4 (May 15, 2017): 347–54. http://dx.doi.org/10.1007/s13748-017-0128-2.

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43

Kwon, Dongup, Wonsik Lee, Dongryeong Kim, Junehyuk Boo, and Jangwoo Kim. "SmartFVM: A Fast, Flexible, and Scalable Hardware-based Virtualization for Commodity Storage Devices." ACM Transactions on Storage 18, no. 2 (May 31, 2022): 1–27. http://dx.doi.org/10.1145/3511213.

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A computational storage device incorporating a computation unit inside or near its storage unit is a highly promising technology to maximize a storage server’s performance. However, to apply such computational storage devices and take their full potential in virtualized environments, server architects must resolve a fundamental challenge: cost-effective virtualization . This critical challenge can be directly addressed by the following questions: (1) how to virtualize two different hardware units (i.e., computation and storage), and (2) how to integrate them to construct virtual computational storage devices, and (3) how to provide them to users. However, the existing methods for computational storage virtualization severely suffer from their low performance and high costs due to the lack of hardware-assisted virtualization support. In this work, we propose SmartFVM-Engine , an FPGA card designed to maximize the performance and cost-effectiveness of computational storage virtualization. SmartFVM-Engine introduces three key ideas to achieve the design goals. First, it achieves high virtualization performance by applying hardware-assisted virtualization to both computation and storage units. Second, it further improves the performance by applying hardware-assisted resource orchestration for the virtualized units. Third, it achieves high cost-effectiveness by dynamically constructing and scheduling virtual computational storage devices. To the best of our knowledge, this is the first work to implement a hardware-assisted virtualization mechanism for modern computational storage devices.

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44

Takeda, K., S. J. Newman, J. Kenny, and M. Zyskowski. "Convergence: Commodity flight simulation and the future." Aeronautical Journal 112, no. 1136 (October 2008): 599–607. http://dx.doi.org/10.1017/s0001924000002566.

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Abstract The development of commodity flight simulation, in the form of PC game technology, continues to advance at a rapid pace. Indeed, the software industry is now being driven primarily by the requirements of gaming, digital media, and other entertainment applications. This has largely been due to the commoditisation of computer hardware, which is apparent when considering recent trends in central processing unit and graphics processor development. The flight simulation industry has benefited from this trend of hardware commoditisation, and will continue to do so for the foreseeable future. It is, however, yet to fully realise the potential for leveraging commodity-off-the-shelf (COTS) software. In this paper the opportunities presenting themselves for the next 25 years of flight simulation are discussed, as the aviation and games software industry’s requirements converge. A SWOT (strengths-weaknesses-opportunities-threats) analysis of the commodity flight simulation software industry is presented, including flight modelling, scenery generation, multiplayer technology, artificial intelligence, mission planning, and event handling. Issues such as data portability, economics, licensing, intellectual-property, interoperability, developer extensibility, robustness, qualification, and maintainability are addressed. Microsoft Flight Simulator is used as a case study of how commodity flight simulation has been extended to include extensive programmatic access to its core engine. Examples are given on how the base platform of this application can be extended by third-party developers and the power this extensibility model provides to the industry. This paper is presented to highlight particular technology trends in the commodity flight simulation industry, the fidelity that commodity flight simulations can provide, and to provide a high-level overview of the strengths and weaknesses thereof.

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45

Liu, Mouli. "Design of Commodity Settlement System Based on Deep Learning." Journal of Physics: Conference Series 2066, no. 1 (November 1, 2021): 012007. http://dx.doi.org/10.1088/1742-6596/2066/1/012007.

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Abstract In order to solve the problem that the salesmen need to scan the bar code of commodity price one by one in supermarket settlement system, a commodity settlement system is proposed. The system hardware includes smart phone, PC and LCD. The image information is obtained after the smart phone scans goods, then the acquired image is uploaded to the upper computer, which identifies commodity and obtains the unit price of goods through the image, finally, the payment QR code is displayed on the LCD screen. Without scanning the cods again at the checkout counter, the payment could be accomplished in a manner that saved time and manpower cost.

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46

West, Richard, Puneet Zaroo, Carl A. Waldspurger, and Xiao Zhang. "Online cache modeling for commodity multicore processors." ACM SIGOPS Operating Systems Review 44, no. 4 (December 13, 2010): 19–29. http://dx.doi.org/10.1145/1899928.1899931.

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47

Swift, Michael M., Brian N. Bershad, and Henry M. Levy. "Improving the reliability of commodity operating systems." ACM SIGOPS Operating Systems Review 37, no. 5 (December 2003): 207–22. http://dx.doi.org/10.1145/1165389.945466.

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48

Tappero, Fabrizio, Rosa Alsina-Pagès, Leticia Duboc, and Francesc Alías. "Leveraging Urban Sounds: A Commodity Multi-Microphone Hardware Approach for Sound Recognition." Proceedings 4, no. 1 (March 8, 2019): 55. http://dx.doi.org/10.3390/ecsa-5-05756.

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City noise and sound are measured and processed with the purpose of drawing appropriate government legislation and regulations, ultimately aimed at contributing to a healthier environment for humans. The primary use of urban noise analysis is carried out with the main purpose of reporting or denouncing, to the appropriate authorities, a misconduct or correct a misuse of council resources. We believe that urban sounds carry more information than what it is extracted to date. In this paper we present a cloud-based urban sound analysis system for the capturing, processing and trading of urban sound-based information. By leveraging modern artificial intelligence algorithms running on a FOG computing city infrastructure, we will show how the presented solution can offer a valuable solution for exploiting urban sound information. A specific focus is given to the hardware implementation of the sound sensor and its multimicrophone architecture. We discuss how the presented architecture is designed to allow the trading of sound information between independent parties, transparently, using cloud-based sound processing APIs running on an inexpensive consumer-grade microphone.

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49

Bonelli, Nicola, Stefano Giordano, and Gregorio Procissi. "Enif-Lang: A Specialized Language for Programming Network Functions on Commodity Hardware." Journal of Sensor and Actuator Networks 7, no. 3 (August 7, 2018): 34. http://dx.doi.org/10.3390/jsan7030034.

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The maturity level reached by today’s commodity platforms makes even low-cost PCs viable alternatives to dedicated hardware to implement real network functions without sacrificing performance. Indeed, the availability of multi-core processing packages and multi-queue network interfaces that can be managed by accelerated I/O frameworks, provides off-the-shelf servers with the necessary power capability for running a broad variety of network applications with near hardware-class performance. At the same time, the introduction of the Software Defined Networks (SDN) and the Network Functions Virtualization (NFV) paradigms call for new programming abstractions and tools to allow this new class of network devices to be flexibly configured and functionally repurposed from the network control plane. The paper presents the ongoing work towards Enif-Lang (Enhanced Network processIng Functional Language), a functional language for programming network functions over generic middleboxes running the Linux operating system. The language addresses concurrent programming by design and is targeted at developing simple stand-alone applications as well as pre-processing stages of packet elaborations. Enif-Lang is implemented as a Domain Specific Language embedded in the Haskell language and inherits the main principles of its ancestor, including the strong typedness and the concept of function compositions. Complex network functions are implemented by composing a set of elementary operations (primitives) by means of a compact yet expressive language grammar. Throughout the paper, the description of the design principles and features of Enif-Lang are accompanied by examples and use cases. In addition, a preliminary performance assessment is carried out to prove the effectiveness of the language for developing practical applications with the performance level required by 5G systems and the Tactile Internet.

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50

Brandvik, T., and G. Pullan. "Acceleration of a two-dimensional Euler flow solver using commodity graphics hardware." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 221, no. 12 (December 1, 2007): 1745–48. http://dx.doi.org/10.1243/09544062jmes813ft.

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The implementation of a two-dimensional Euler solver on graphics hardware is described. The graphics processing unit is highly parallelized and uses a programming model that is well suited to flow computation. Results for a transonic turbine cascade test-case are presented. For large grids (106 nodes) a 40 times speed-up compared with a Fortran implementation on a contemporary CPU is observed.

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