ENHANCEMENT OF TEGRA TABLET'S COMPUTATIONAL PERFORMANCE BY GEFORCE DESKTOP AND WIFI Di Zhao The Ohio State University GPU Technology Conference 2014, March 24-27 2014, San Jose California 1 TEGRA-WIFI-GEFORCE Tegra tablet and Geforce desktop are one of the most popular home based computing platforms; Wifi is the most popular home networking platform; Wildly applied to entertainment, healthcare, gaming, etc; 1.1 DEVELOPMENT OF TEGRA AND GEFORCE Core GFLOPS (single) Tegra 4 CPU 4+1/GPU ~ 80 72 Tegra K1 CPU 4+1/192 ~ 180 CUDA core TITAN 2688 CUDA ~ 4500 core TITAN Z 5760 CUDA ~ 8000 core 1.2 DEVELOPMENT OF WIFI Protocol Theoretical Frequency (G) Release Date Speed (M) 802.11 1 − 2 2.4 1997-06 802.11a 6 − 54 5 1999-09 802.11b 1 − 11 2.4 1999-09 802.11g 6 − 54 2.4 2003-06 802.11n 15 − 150 2.4/5 2009-10 802.11ac < 866.7 5 2014-01 802.11ad < 6912 60 2012-12 IEEE 802.11 Network Standards THE PROBLEM Tegra has limited GFLOPS, when the applications exceed Tegra’s computational ability GFLOPS, what should we do? Mobile applications such as computer graphics or healthcare often result in heavy computation; Mobile applications have time constraints because user do not want to wait for seconds; Geforce has large GFLOPS, and Tegra can be supported by Geforce and Wifi? In this talk, experiences of Tegra-Wifi-Geforce are introduced, and an example of medical image is discussed; 2 SETUP DEVELOPMENT ENVIRONMENT FOR TEGRA-WIFI- GEFORCE Setup of Development Environment for Tegra Tablet; TestingWifi Device; Setup of Development for Geforce Desktop; Development of the Communication Model; 2.1 SETUP OF DEVELOPMENT ENVIRONMENT FOR TEGRA TABLET Install Visual Studio on the development computer; Setup Android Debug Bridge (ADB) on Tegra tablet, connect Tegra tablet and the development computer by USB cable, bluetooth or Wifi; Enable developer mode for Android on Tegra tablet; Install the latest version of Tegra Android Development Pack; If everything works fine, you will see: 2.2 TESTING WIFI SPEED Test the real speed of Wifi speed by tools, unrelated to Internet speed; Iperf is a tool to measure maximum TCP bandwidth, delay jitter, datagram loss, etc; Maximum TCP bandwidth is important parameter to develop the application for Tegra-Wifi-Geforce; TCP bandwidth can be read from Iperf output: 2.3 PROGRAMMING TEGRA-WIFI- GEFORCE 2.3 .3 2.3.2 CUDA C/C++, TCP/IP CUDA Fortran, Socket MATLAB Parallel Program Computing Toolbox, other commercial libraries, existing 2.3.1.1 Tigre Android software, etc. Development Pack, Android Application (Java), Android Application with Native Code (Java, C++), Android Native Application (C++) 2.3.1.2 Graphic Programming: OpenGL ES, OpenCV, etc. 2.4 COMMUNICATION Tegra and Geforce run different code, not SIMD; Tegra and Geforce cooperate and communicate for the application; Tegra tablet and Geforce desktop are different computers, and heterogeneous computing with OpenCL may be not an option; Tegra tablet and Geforce desktop communicate only by Wifi; Any library? 2.4 COMMUNICATION Tegra Communication Geforce Communication Tegra Communication Geforce Blocked Point-to-Point Communication between Geforce and Tegra 2.4 COMMUNICATION Work perfect between Tegra and Geforce; Advantage: no conflict for too much data or no data; Advantage: easy to program for communication; Disadvantage: low efficiency; Disadvantage: single Geforce and single Tegra; Better solution? Blocked Point-to-Point Communication between Geforce and Tegra 2.5 APPLICATIONS FOR TEGRA-WIFI- GEFORCE After the setup of the development environment, applications on Tegra-Wifi-Geforce can be developed by: Evaluation of computational requirements; Evaluation of communication requirements; Evaluation the programming difficulty: Geforce is much easier programming than Tegra; Decide the Separation Point of the applications into Tegra tablet and Geforce desktop; 3 Enhancement of Tegra's Computational Performance by GeForce Healthcare apps for the physicians; Healthcare apps for the public; Currently, medical image in healthcare apps are currently in medical image viewer; 1000000 KJ ITK on the iOS MOBILE HEALTH MARKET WORTH $20.7 BILLION BY 2018 3.1 THE EXAMPLE ON TEGRA-WIFI- GEFORCE: ULTRASOUND SIMULATION Settings Communication 1 Communication 2 Signal Simulation Image Reconstruction Ultrasound simulation consists of two parts: signal simulation and image reconstruction; By the settings, ultrasound signals are simulated from solving physical equations, not from real medical equipment; Based on the simulated signals, ultrasound images are reconstructed; 3.2 SIMULATION OF SIGNAL In the simulation equation, both variable t and p are discretized, and each solution of the two variables results in the matrix for the simulated signals: the sensor data. EVALUATION OF COMPUTATIONAL REQUIREMENTS Generally, the ultrasound signals are calculated by numerical methods; Ultrasound signal simulation is computationally intensive: by finite difference method or finite element method, at every time step T, a tri-diagonal matrix is solved with the discretization size P of the variable p; EVALUATION OF COMPUTATIONAL REQUIREMENTS Number of Channels C One scan line Signal Simulation can Reach Large GFLOPS EVALUATION OF COMPUTATIONAL REQUIREMENTS Image Domain Frequency Domain Beamforming TGC FFT Filtering IFFT FFT IFFT Envelop Detection Image reconstruction needs Log Compression small GFLOPS, and easy to program. In Tegra K1, CUDA, cuFFT and cuBLAS are Scan Convention available. Signal processing? EVALUATION OF COMMUNICATION REQUIREMENTS AND PROGRAMMING DIFFICULTY Communication 1: small; Communication 2: for simulating an image, fps × scan resolution × channels × precision ( float or double ), generally several mb per second; Image data is transferred in scan resolution, not in image resolution. After the scan data is received, the image resolution is obtained by interpolation. Image reconstruction is much easier programming than signal simulation; the Separation Point: the simulated ultrasound signal 3.3 ULTRASOUND SIMULATION The setting of parameters is transferred to Geforce through Wifi (communication 1); The ultrasound signals are simulated in Geforce; The simulated signals are transferred to Tegra tablet through Wifi (communication 2); The image is reconstructed in Tegra tablet; 3.4 FUTURE RESEARCH By CUDA and libraries, ultrasound simulation on Tegra K1; Better communication mode for Tegra- Wifi-Geforce; Real-time 3D+time ultrasound simulation: faster signal simulation and faster image reconstruction; 4 DISCUSSION Dicoogle Mobile Droid Dicom Viewer ITK on the iOS Endrov MIRC Viewer KiwiViewer Open-source Medical Image for Mobile Device MORE MEDICAL IMAGE Drishti is volume exploration and presentation tool: http://sf.anu.edu.au/Vizlab/drishti/; ITK-SNAP is a software application used to segment structures in 3D medical images: http://www.itksnap.org/; VTK is an open-source, freely available software system for 3D computer graphics, image processing and visualization: http://www.vtk.org/; Voreen volume rendering engine: http://www.voreen.org/; InVesalius is Open source software for reconstruction of CT and MRI: http://www.cti.gov.br/invesalius/; GIMIAS is a workflow-oriented environment focused on biomedical image computing and simulation: www.gimias.net; Open-source Medical Image Software EVEN MORE MEDICAL IMAGE? 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