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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
72
~ 80
Tegra K1 CPU 4+1/192
CUDA core
~ 180
TITAN 2688 CUDA
core
~ 4500
TITAN Z 5760 CUDA
core
~ 8000
1.2 DEVELOPMENT OF WIFI
Protocol Theoretical
Speed (M)
Frequency (G) Release Date
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
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;
Tegra has limited GFLOPS, when the
applications exceed Tegra’s
computational ability GFLOPS, what
should we do?
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
2.3.1.1 Tigre Android
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.
2.3 .3
TCP/IP
Socket
Program
2.3.2 CUDA C/C++,
CUDA Fortran,
MATLAB Parallel
Computing Toolbox,
other commercial
libraries, existing
software, 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
Geforce
Communication
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
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;
After the setup of the development environment, applications on Tegra-Wifi-Geforce can be developed by:
3 Enhancement of Tegra's
Computational Performance by
GeForce
MOBILE HEALTH MARKET WORTH $20.7 BILLION BY 2018
1000000 KJ
Healthcare apps for the physicians;
Healthcare apps for the public;
Currently, medical image in healthcare apps are currently in medical image
viewer;
ITK on the iOS
3.1 THE EXAMPLE ON TEGRA-WIFI-
GEFORCE: ULTRASOUND SIMULATION
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;
Signal Simulation Image Reconstruction
Settings
Communication 1 Communication 2
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;
Signal Simulation can Reach Large GFLOPS
Number of Channels C
One scan line
EVALUATION OF COMPUTATIONAL REQUIREMENTS
EVALUATION OF COMPUTATIONAL
REQUIREMENTS
Beamforming
TGC
Filtering
Envelop Detection
Log Compression
Scan Convention
FFT IFFT
Image Domain Frequency Domain
IFFT FFT
Image reconstruction needs
small GFLOPS, and easy to
program. In Tegra K1, CUDA,
cuFFT and cuBLAS are
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;
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;
3.4 FUTURE RESEARCH
4 DISCUSSION
Dicoogle Mobile
Open-source Medical Image for Mobile Device
Droid Dicom Viewer ITK on the iOS
MIRC Viewer KiwiViewer
Endrov
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?
Please visit my poster:
THANKS !