CUDA/Jetson TK1 (Tegra K1 SoC)

This article is an introduction to Tegra K1 SOC board (hereafter Jetson), which is equipped with Kepler Core GPU. Even though Jetson has already installed Linux for Tegra (L4T) and CUDA 6.0 ToolKit by default, this article shows how to install L4T and CUDA 6.0 ToolKit, from the beginning.


Hardware Specification of Jetson

The hardware specification of Jetson is listed below.

  • Kepler GPU with 192 CUDA cores
  • 4-Plus-1 quad-core ARM Cortex A15 CPU
  • 2GB DDR3L
  • 16GB 4.51 eMMC memory

Installing Process

Although LT4 (Linux for Tegra) is initially installed in Jetson, this article explains how to install L4T and CUDA 6.0 from the scratch. These processes are based up on the L4T Quick Start Guide. For the details please click the URL shown below.

Caution1: To apply this installation process, the directory under /home/ubuntu/NVIDIA-INSTALLER will be gone and will NOT be recovered even if you apply the recovery process.

Caution2: To install default L4T, please use the commands below

cd home/ubuntu/NVIDIA-INSTALLER
sudo ./

Then, reboot the system. However, should only be run once.

The host OS is Ubuntu 12.04 LTS (64bit) . This article assumes that the host OS is already installed and running on your PC. This article does not explain the details of how to install Ubuntu on a PC nor a virtual machine.

About CUDA 6.0 Toolkit for L4T Rel-19.2

To obtain CUDA6.0 Toolkit for L4T Rel-19.2, you need to register for CUDA Registered Developer Program. Even though you register (join) the program, you can not download the Toolkit immediately. It will take a few hours before you can download the Toolkit. For its details, please visit the URL shown below.

Installing L4T

Remove the AC adapter from Jetson, connect to the host PC via a microUSB cable.
Create a working directory on the host OS, and download Tegra_Linux_Sample-Root-Filesystem_R19.2.0_armhf.tbz2 and Tegra124_Linix_R19.2.0_armhf.tb2 from the URL listed below.

$ mkdir ~/Jetson
$ cd Jetson/
$ wget
$ wget

Applying the root privilege, expand Tegra124_Linux_armhf.tbz2. Eventually, a new directory Linux_for_Tegra is created. Under Linux_for_Tegra directory, there is rootfs directory. Please move to this rootfs directory.

$ sudo tar xpf Tegra124_Linux_R19.2.0_armhf.tbz2
$ cd Linux_for_Tegra/rootfs/

Move to the rootfs directory. Then, download and expand the other file, Tegra_Linux_Sample_Root_Filesystem_R19.2.0_armhf.tbz2, at the rootfs directory.

$ sudo tar xpf ../../Tegra_Linux_Sample-Root-Filesystem_R19.2.0_armhf.tbz2

Configuring USB3.0 port

As reinstalling L4T, the USB 3.0 port of Jetson is turned off. As the default setting, the USB port of Jetson is configured as USB 2.0. To be recognized as USB3.0, edit Linux_for_Tegra/Jetson-tk1.conf. For the details, please click the URL shown below.

This is how the file is edited.

$ vi jetson-tk1.conf

# USB 2.0 operation on USB2 port(J1C2 connector)/for use as root device use ODMDATA=0x6009C000;
# USB 3.0 operation on USB2 port(J1C2 connector) use ODMDATA=0x6209C000, requires firmware load from userspace or initial ramdisk
ODMDATA=0x6209C000;     <- Comment in this code
#ODMDATA=0x6009C000;    <- Comment out this code

Completing the modification of jetson-tk1.conf, execute is a script, which places binaries to rootfs.

$ cd ..
$ sudo ./ 
Using rootfs directory of: /home/beat/Jetson/Linux_for_Tegra/rootfs

... <snip>...

Extracting the firmwares and kernel modules to /home/beat/Jetson/Linux_for_Tegra/rootfs
Installing the board *.dtb files into /home/beat/Jetson/Linux_for_Tegra/rootfs/boot

While push down the button of FORCE RECOVERY on Jetson and push RESET button, Jetson is recognized by the host OS. Then, check the presence of Jetson on host PC by lsusb. If it has not been recognized, push RESET button again.

$ lsusb | grep -i nvidia
Bus 001 Device 002: ID 0955:7140 NVidia Corp.

Applying the root privilege, execute the command shown below. Kernel and rootfs are transfered. Because of NFS mount and transfer by tft, it will take at least 30 minutes to complete the transfer.

If not automatically execute reset when the transfer is completed, please push the RESET button and execute reset.

When reset is completed, the recovery of L4T is completed.

$ sudo ./ -S 8GiB jetson-tk1 mmcblk0p1

copying dtbfile(/home/beat/Jetson/Linux_for_Tegra/kernel/dtb/tegra124-pm375.dtb) to tegra124-pm375.dtb... done.
Making system.img...
         populating rootfs from /home/beat/Jetson/Linux_for_Tegra/rootfs... done.
         Sync'ing... done.
System.img built successfully.
copying bctfile(/home/beat/Jetson/Linux_for_Tegra/bootloader/ardbeg/BCT/PM375_Hynix_2GB_H5TC4G63AFR_RDA_924MHz.cfg) to bct.cfg... done.
copying cfgfile(/home/beat/Jetson/Linux_for_Tegra/bootloader/ardbeg/cfg/gnu_linux_fastboot_emmc_full.cfg) to flash.cfg... done.
creating gpt(ppt.img)...
*** GPT Parameters ***
device size -------------- 15766388736
bootpart size ------------ 8388608
userpart size ------------ 15758000128
Erase Block Size --------- 2097152
sector size -------------- 4096
Partition Config file ---- flash.cfg
Visible partition flag --- GP1
Primary GPT output ------- PPT->ppt.img
Secondary GPT output ----- GPT->gpt.img
Target device name ------- none


Create, format and download  took 4315 Secs
Time taken for flashing 4317 Secs
*** The target ardbeg has been flashed successfully. ***
Reset the board to boot from internal eMMC.

Checking and Setting up L4T on Jetson

Checking and setting up L4T, which is re-installed on Jetson.~

User Name: Ubuntu
Password  : Ubuntu

Checking Kernel Version of L4T on Jetson

Kernel version of L4T on Jetson is listed as it is followed.

$ uname -a
Linux tegra-ubuntu 3.10.24-g6a2d13a #1 SMP PREEMPT Fri Apr 18 15:56:45 PDT 2014 armv7l armv7l armv7l GNU/Linux

Checking USB 3.0 port of Jetson

To check enabling USB 3.0, apply lsusb command.

$ lsusb
Bus 002 Device 001: ID 1d6b:0003 Linux Foundation 3.0 root hub
Bus 001 Device 001: ID 1d6b:0002 Linux Foundation 2.0 root hub

Back up

Create a back-up of libgix,so. When the update of xserver overwrite, this is used for brunging back to the previous settings. For more details, please look at the PDF, which the URL points out below.

$ cd /usr/lib/modules/extensions/
$ sudo cp __libglx.so_orig

Adding extra repository

To obtain the code for development, easily, an extra repository is added. The URL below provides more information on adding an extra repository.

$ sudo apt-add-repository universe
$ sudo apt-get update
$ sudo apt-get upgrade

Caution: Sometimes, the currently executing process is suspended, as applying sudo apt-get upgrade. If this happens, reset, first. Execute sudo dpkg –configure -a. Then, re-execute sudo apt-get update, and sudo apt-get upgrade.

Installing CUDA6.0 ToolKit

Caution: Assuming that you have already downloaded cuda-repo-14t-r19.2_6.0-42_armhf.deb.

Before reinstalling CUDA6.0 Toolkit, reset the clock by the command of ntpdate. This is for avoiding warnings of time conflicts when cuda-repo-14t-r19.2_6.0-42_armhf.deb is installed.

$ sudo ntpdate
$ date

Install cuda-repo-14t-r19.2_6.0-42_armhf.deb, and update repository. Then, CUDA 6.0 Toolkit is installed.

$ sudo dpkg -i cuda-repo-l4t-r19.2_6.0-42_armhf.deb
$ sudo apt-get update
$ sudo apt-get install cuda-toolkit-6-0

Default user, Ubuntu, is added to the video group.

$ sudo usermod -a -G video ubuntu

Then, set the environment variable as adding the two lines of code to the end of .bashrc.

export PATH=/usr/local/cuda-6.0/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda-6.0/lib:$LD_LIBRARY_PATH

Installing and Executing CUDA Samples

Applying the commands below, install and build CUDA Samples.

After its build process is completed, move to the directory of ~/NVIDIA_CUDA-6.0_Samples/bin/armv7l/linux/release/guneabihf. Then, execute deviceQuery, which is listed at the section of Running the Binaries of NVIDIA CUDA Getting Started Guide for Linux.

$ ~/
$ cd NVIDIA_CUDA-6.0_Samples/
$ make
$ cd bin/armv7l/linux/release/gnueabihf/
$ ./deviceQuery
./deviceQuery Starting...

CUDA Device Query (Runtime API) version (CUDART static linking)

Detected 1 CUDA Capable device(s)

Device 0: "GK20A"
  CUDA Driver Version / Runtime Version          6.0 / 6.0
  CUDA Capability Major/Minor version number:    3.2
  Total amount of global memory:                 1746 MBytes (1831051264 bytes)
  ( 1) Multiprocessors, (192) CUDA Cores/MP:     192 CUDA Cores
  GPU Clock rate:                                852 MHz (0.85 GHz)
   Memory Clock rate:                             924 Mhz
   Memory Bus Width:                              64-bit
   L2 Cache Size:                                 131072 bytes
  M aximum Texture Dimension Size (x,y,z)         1D=(65536), 2D=(65536, 65536), 3D=(4096, 4096, 4096)
  Maximum Layered 1D Texture Size, (num) layers  1D=(16384), 2048 layers
  Maximum Layered 2D Texture Size, (num) layers  2D=(16384, 16384), 2048 layers
  Total amount of constant memory:               65536 bytes
  Total amount of shared memory per block:       49152 bytes
  Total number of registers available per block: 32768
  Warp size:                                     32
  Maximum number of threads per multiprocessor:  2048
  Maximum number of threads per block:           1024
  Max dimension size of a thread block (x,y,z): (1024, 1024, 64)
  Max dimension size of a grid size    (x,y,z): (2147483647, 65535, 65535)
  Maximum memory pitch:                          2147483647 bytes
  Texture alignment:                             512 bytes
  Concurrent copy and kernel execution:          Yes with 1 copy engine(s)
  Run time limit on kernels:                     No
  Integrated GPU sharing Host Memory:            Yes
  Support host page-locked memory mapping:       Yes
  Alignment requirement for Surfaces:            Yes
  Device has ECC support:                        Disabled
  Device supports Unified Addressing (UVA):      Yes
  Device PCI Bus ID / PCI location ID:           0 / 0
  Compute Mode:
      < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >

deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 6.0, CUDA Runtime Version = 6.0, NumDevs = 1, Device0 = GK20A
Result = PASS

Compare and Comparison between Tesla K20c and Jetson TK1 (Tegra K1)

Update CUDA of the machine, which is introduced at the article of CUDA5/CentOS6.4 to CUDA6, compare and comparison of executing results of the CUDA_Samples between them.

  • Results of deviceQuery
    The major differences are defined in bold.

    Device 0: "Tesla K20c"Device 0: "GK20A"
    CUDA Driver Version / Runtime Version6.0 / 6.06.0 / 6.0
    CUDA Capability Major/Minor version number3.53.2
    Total amount of global memory4800 MBytes (5032706048 bytes)1746 MBytes (1831051264 bytes)
    Multiprocessors, (192) CUDA Cores/MP2496 CUDA Cores 注1192 CUDA Cores
    GPU Clock rate706 MHz (0.71 GHz)852 MHz (0.85 GHz)
    Memory Clock rate2600 Mhz924 Mhz
    Memory Bus Width320-bit64-bit
    L2 Cache Size1310720 bytes131072 bytes
    Maximum Texture Dimension Size (x,y,z)1D=(65536), 2D=(65536, 65536), 3D=(4096, 4096, 4096)1D=(65536), 2D=(65536, 65536), 3D=(4096, 4096, 4096)
    Maximum Layered 1D Texture Size, (num) layers1D=(16384), 2048 layers1D=(16384), 2048 layers
    Maximum Layered 2D Texture Size, (num) layers2D=(16384, 16384), 2048 layers2D=(16384, 16384), 2048 layers
    Total amount of constant memory65536 bytes65536 bytes
    Total amount of shared memory per block49152 bytes49152 bytes
    Total number of registers available per block6553632768
    Warp size3232
    Maximum number of threads per multiprocessor20482048
    Maximum number of threads per block10241024
    Max dimension size of a thread block (x,y,z)(1024, 1024, 64)(1024, 1024, 64)
    Max dimension size of a grid size (x,y,z)(2147483647, 65535, 65535)(2147483647, 65535, 65535)
    Maximum memory pitch2147483647 bytes2147483647 bytes
    Texture alignment512 bytes512 bytes
    Concurrent copy and kernel executionYes with 2 copy engine(s)Yes with 1 copy engine(s)
    Run time limit on kernelsNoNo
    Integrated GPU sharing Host MemoryNoYes
    Support host page-locked memory mappingYesYes
    Alignment requirement for SurfacesYesYes
    Device has ECC supportEnabledDisabled
    Device supports Unified Addressing (UVA)YesYes
    Device PCI Bus ID / PCI location ID1 / 00 / 0

    Caution: Tesla K20c does have 13 units of MP.

  • matrixMul (Matrix Multiplication)
    The results are the averages of outcomes of 10 experiments.

    GPU Device 0: "Tesla K20c" with compute capability 3.5GPU Device 0: "GK20A" with compute capability 3.2
    MatrixA(320,320), MatrixB(640,320)Performance= 243.46 GFlop/s ,Time= 0.538 msecPerformance= 11.49 Gflop/s ,Time= 11.404 msec
    Size = 131,072,000 Ops
    WorkgroupSize = 1024 threads / block
  • bandwidthTest
    The results are the averages of outcomes of 10 experiments.

    Device 0: Tesla K20cDevice 0: GK20A
    Quick Mode
    Host to Device Bandwidth, 1 Device(s) PINNED Memory Transfers
    Transfer Size (Bytes)Bandwidth(MB/s)Bandwidth(MB/s)
    Device to Host Bandwidth, 1 Device(s) PINNED Memory Transfers
    Transfer Size (Bytes)Bandwidth(MB/s)Bandwidth(MB/s)
    335544326550.15466 注2
    Device to Device Bandwidth, 1 Device(s) PINNED Memory Transfers
    Transfer Size (Bytes)Bandwidth(MB/s)Bandwidth(MB/s)
    33554432147382.768360.8 注3

    Caution: Outcomes may differ depending on mode.
    Caution: There are large discrepancies in outcomes of Device-to-Device experiments.

Revision History

  • 2014/12/22 This article is initially published

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Last-modified: 2014-12-22 (Mon) 12:40:37 (2466d)