[メモ] NVIDIA GPU Computing Software Development Kit CUDA SDK 4.0 Release Notes

メモとして公開

コンパイル時 (nvcc) エラーがあるという方へ。（ドキュメント類は目を通しましょう）

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NVIDIA GPU Computing Software Development Kit
CUDA SDK 4.0 Release Notes
R270 Driver

Windows Vista, Windows XP, and Windows 7 (32/64-bit)
Windows Server 2003, 2003 R2, 2008, 2008 R2
Linux OS (32/64-bit)
Mac OSX (10.5.x Leopard 32/64-bit, 10.6.x SnowLeopard 32/64-bit)

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Please, also refer to the release notes of version 4.0 Release of the CUDA Toolkit,
installed by the CUDA Toolkit installer.

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TABLE OF CONTENTS
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I. (a) Windows Installation Instructions
I. (b) Linux Installation Instructions
I. (c) Mac OSX Installation Instructions

II. (a) Creating Your Own CUDA Projects for Windows
II. (b) Creating Your Own CUDA Projects for Linux
II. (c) Creating Your Own CUDA Projects for Mac OSX

III.(a) Known Issues on CUDA for Windows
III.(b) Known Issues on CUDA for Linux
III.(c) Known Issues on CUDA for Mac OSX

IV. Frequently Asked Questions
V. Addendum
VI. Change Log
VII. OS Platforms and Compilers Supported
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I. (a) Windows Installation Instructions
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0. CUDA 4.0 Release Toolkit requires at least version R270 of the Windows Vista or
Windows XP NVIDIA Display Driver. See the NVIDIA CUDA Toolkit 4.0 Release
Notes for more detailed information.

Please make sure to read the Driver Installation Hints Document before you
install the driver:
http://www.nvidia.com/object/driver_installation_hints.html

1. Uninstall any previous versions of the NVIDIA CUDA Toolkit and NVIDIA GPU Computing SDK.
You can uninstall the NVIDIA CUDA Toolkit through the Start menu:
Start menu->All Programs->NVIDIA Corporation->CUDA Toolkit->Uninstall CUDA

You can uninstall the NVIDIA GPU Computing SDK through the Start menu:
Start menu->All Programs->NVIDIA Corporation
->NVIDIA GPU Computing SDK->Uninstall NVIDIA GPU Computing SDK

2. Install version 4.0 Release of the NVIDIA CUDA Toolkit by running
cudatoolkit_4.0_Win_[32|64].exe corresponding to your operating
system.

3. Install version 4.0 Release of the NVIDIA GPU Computing SDK by running
gpucomputingsdk_4.0_Win_[32|64].exe corresponding to your operating
system.

4. Build the 32-bit and/or 64-bit, release or debug
configurations of the SDK project examples using the provided
*_vs2005.sln solution files for Microsoft Visual Studio 2005 or
*_vs2008.sln solution files for Microsoft Visual Studio 2008 or
*_vs2010.sln solution files for Microsoft Visual Studio 2010
You can:
– either use the solution files located in each of the example
directories in “NVIDIA GPU Computing SDK 4.0Csrc”,
– or use the global solution files located under “NVIDIA GPU Computing SDK 4.0Csrc”

“release_vs2005.sln”
“release_vs2008.sln”
“release_vs2010.sln”

Notes:

– The simpleD3D9 example requires to have a Direct3D SDK installed and the
VC++ directory paths (located in Tools->Options…) properly setup.

– Most samples link to a utility library called “cutil” whose source code
is in “NVIDIA GPU Computing SDK 4.0Ccommon”. The release versions of
these samples link to cutil[32|64].lib and dynamically load
cutil[32|64].dll. The debug versions of these samples link
to cutil[32D|64D].lib and dynamically load cutil[32D|64D].dll.
In order to build the 32-bit and/or 64-bit, release and/or debug configurations
of the cutil library, use the solution files located in
“NVIDIA GPU Computing SDK 4.0Ccommon”. The output of the compilation goes to
“NVIDIA GPU Computing SDK 4.0Ccommonlib”:
– cutil[32|64].lib and cutil[32D|64D].lib are the release and debug
import libraries,
– cutil[32|64].dll and cutil[32D|64D].dll are the release and debug
dynamic-link libraries, which get also copied to
“NVIDIA GPU Computing SDK 4.0Cbinwin[32|64][release]” and
“NVIDIA GPU Computing SDK 4.0Cbinwin[32|64][debug]”
Respectively;

5. Run the examples from the release or debug directories
located in “NVIDIA GPU Computing SDK 4.0Cbinwin[32|64][release|debug]”.

Notes:

– The release and debug configurations require a CUDA-capable GPU to run
properly (see Appendix A.1 of the CUDA Programming Guide for a complete
list of CUDA-capable GPUs).

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I. (b) Linux Installation Instructions
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[Part 1/2]

Note: The default installation folder <SDK_INSTALL_PATH> is “~/NVIDIA_GPU_Computing_SDK”

For more detailed instructions, see section II below.

0. Install the NVIDIA Linux display driver by executing the file

a. For 32-bit linux distributions use:
cudadriver_4.0_linux_32_270.xx.run

b. For 64-bit linux distributions use:
cudadriver_4.0_linux_64_270.xx.run

For information on installing NVIDIA Linux display drivers, please refer to
the NVIDIA Accelerated Linux Driver Set README and Installation Guide:
http://us.download.nvidia.com/XFree86/Linux-x86/1.0-9755/README/index.html

1. Install version 4.0 Release of the NVIDIA Toolkit by executing the file
cudatoolkit_4.0_linux_*.run where * corresponds to your Linux distribution

Add the CUDA binaries and lib path to your PATH and LD_LIBRARY_PATH
environment variables.

2. Install version 4.0 Release of the NVIDIA GPU Computing SDK by executing the file
gpucomputingsdk_4.0_linux.run

The installer will prompt you to enter an installation path for the SDK or
accept the default. We will refer to the path you choose as
SDK_INSTALL_PATH.

3. Build the SDK project examples.

cd <SDK_INSTALL_PATH>/C
make

Note Adding the following in make will build for specific targets

make x86_64=1 (for 64-bit targets)
make i386=1 (for 32-bit targets)

4. Run the examples (32-bit or 64-bit Linux)

cd <SDK_INSTALL_PATH>/C/bin/linux/release
matrixmul

(or any of the other executables in that directory)

See the next section for more details on installing, building, and running
SDK samples.

[Part 2/2]
Note: The default installation folder <SDK_INSTALL_PATH> is “~/NVIDIA_GPU_Computing_SDK”

This package consists of a “.run” file. This is a self-extracting archive that
decompresses its contents to a temporary folder and then installs the contents
The archive is:

gpucomputingsdk_4.0_linux.run: NVIDIA GPU Computing SDK Installer

In addition, a NVIDIA Linux Display driver is needed to run CUDA code on an
NVIDIA GPU. CUDA 4.0 Release requires version 270 or newer version of the linux
NVIDIA Display Driver. Please see the NVIDIA CUDA Toolkit 4.0 Release notes
for more details.

For information on installing NVIDIA Linux display drivers, please refer to
the NVIDIA Accelerated Linux Driver Set README and Installation Guide:
http://us.download.nvidia.com/XFree86/Linux-x86/1.0-9755/README/index.html

1. Install version 4.0 Release of the NVIDIA CUDA Toolkit by executing the file
cudatoolkit_4.0_linux_*.run where * corresponds to your Linux distribution

To install, run the cudatoolkit_4.0_linux_*.run script. You will be prompted
for the path to where you want to put the CUDA files. In the following we will
call this path <CUDA_INSTALL_PATH>. It is recommended that you run the
installer as root and use the default install path (/usr/local).

Make sure that you add the location of the CUDA binaries (such as nvcc) to
your PATH environment variable and the location of the CUDA libraries
(such as libcuda.so) to your LD_LIBRARY_PATH environment variable.

In the bash shell, one way to do this is to add the following lines to the
file ~/.bash_profile from your home directory.

a. For 32-bit operating systems use the following paths
PATH=$PATH:<CUDA_INSTALL_PATH>/bin
LD_LIBRARY_PATH=$LD_LIBRARY_PATH:<CUDA_INSTALL_PATH>/lib

b. For 64-bit operating systems use the following paths
PATH=$PATH:<CUDA_INSTALL_PATH>/bin
LD_LIBRARY_PATH=$LD_LIBRARY_PATH:<CUDA_INSTALL_PATH>/lib64

Then to export the environment variables add this to the profile configuration
export PATH
export LD_LIBRARY_PATH

2. Install the NVIDIA GPU Computing SDK by executing the file gpucomputingsdk_4.0_linux.run

To install, run the gpucomputingsdk_4.0_linux.run script. You will
be prompted for the path to where you want to put the CUDA SDK. You can
regard the CUDA SDK as user code (it is a set of examples), and therefore
the default installation is in the current user’s home directory
(~/NVIDIA_GPU_Computing_SDK). You must either accept the default or specify a path
to which the user has write permissions.

We will refer to the path you choose as SDK_INSTALL_PATH below.

3. Build the SDK project examples.
a. cd <SDK_INSTALL_PATH>/C
b. Build:
– release configuration by typing “make”.
– debug configuration by typing “make dbg=1”.
– x86_64=1 configuration by typing “make x86_64=1”
– i386=1 configuration by typing “make i386=1”

Running make at the top level first builds libcutil, a utility library used
by the SDK examples (libcutil is simply for convenience — it is not a part
of CUDA and is not required for your own CUDA programs). Make then builds
each of the projects in the SDK.

NOTES:
– The release and debug configurations require a CUDA-capable GPU to run
properly (see Appendix A.1 of the CUDA Programming Guide for a complete
list of CUDA-capable GPUs).

– To build just libcutil, type “make” (or “make dbg=1”) in the “common”
subdirectory:

cd <SDK_INSTALL_PATH>/C/common
make

4. Run the examples from the release or debug
directories located in

<SDK_INSTALL_PATH>/C/bin/linux/[release|debug].

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I. (c) Mac OSX Installation Instructions
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[Part 1/2]

For more detailed instructions, see part 2 of this section below.

Note: The default installation folder <SDK_INSTALL_PATH> is “/Developer/GPU Computing”

Note: For SnowLeopard, if you need to boot up in 32-bit kernel. During Power-On, hit keys
‘3’ and ‘2’ immediately after the startp sound, and the OS will startup in a 32-bit
kernel mode. To boot up with a 64-bit kernel, during Power-On, hit keys ‘6’ and ‘4’.

Please install the packages in this order.

0. Install version 4.0 Release of the NVIDIA Toolkit package by executing the file
cudatoolkit_4.0_macos.pkg

This package will work MAC OSX running 32/64-bit.
CUDA applications built in 32/64-bit (CUDA Driver API) are supported.
CUDA applications built as 32/64 bit (CUDA Runtime API) are supported.
(10.5.x Leopard and 10.6 SnowLeopard)

1. A. Install the NVIDIA Driver Package (Mac OSX Leopard)

i. Do you have a Quadro 4000 for Mac and/or recently updated to the Mac OSX 10.6.6?
If so, please first install the release 256 driver for Mac. You can download the
package from here:

http://www.nvidia.com/object/quadro-macosx-256.01.00f03-driver.html

ii. For NVIDIA GeForce GPU or Quadro GPUs, install this package:
cudadriver_4.0_macos.pkg

2. Install version 4.0 Release of the NVIDIA GPU Computing SDK by executing the file
gpucomputingsdk_4.0_macos.pkg

3. Build the SDK project examples

cd <SDK_INSTALL_PATH>
make

Note: Adding the following in make will build for specific target.

4. Run the examples:

cd <SDK_INSTALL_PATH>/C/bin/darwin/release
./matrixMul

(or any of the other executables in that directory)

[Part 2/2]

See the next section for more details on installing, building, and running
SDK samples.

Note: The default installation folder <SDK_INSTALL_PATH> is “/Developer/GPU Computing”

1. Build the SDK project examples.
a. Go to <SDK_INSTALL_PATH> (“cd <SDK_INSTALL_PATH>”)
b. Build:
– release configuration by typing “make”.
– debug configuration by typing “make dbg=1”.
– x86_64=1 configuration by typing “make x86_64=1”
– i386=1 configuration by typing “make i386=1”

Note: x86_64 is not currently working for Leopoard or SnowLeopard

Running make at the top level first builds libcutil, a utility library used
by the SDK examples (libcutil is simply for convenience — it is not a part
of CUDA and is not required for your own CUDA programs). Make then builds
each of the projects in the SDK.

NOTES:
– The release and debug configurations require a CUDA-capable GPU to run
properly (see Appendix A.1 of the CUDA Programming Guide for a complete
list of CUDA-capable GPUs).
– To build just libcutil, type “make” (or “make dbg=1”) in the “common”
subdirectory:

cd <SDK_INSTALL_PATH>/C/common
make

4. Run the examples from the release or debug
directories located in

<SDK_INSTALL_PATH>/C/bin/darwin/[release|debug].

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II. (a) Creating Your Own CUDA Projects for Windows
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Creating a new CUDA Program using the NVIDIA GPU Computing SDK infrastructure is easy.
We have provided a “template” and “template_runtime” project that you can copy and modify
to suit your needs. Just follow these steps:

1. Copy the content of “NVIDIA GPU Computing SDK 4.0Csrctemplate” or
“NVIDIA GPU Computing SDK 4.0Csrctemplate_runtime” to a directory of your own
“NVIDIA GPU Computing SDK 4.0Csrcmyproject”

2. Edit the filenames of the project to suit your needs.

3. Edit the *.sln, *.vcproj and source files. Just search and replace all
occurrences of “template” or “template_runtime” with “myproject”.

4. Build the 32-bit and/or 64-bit, release or debug configurations using:

“myproject_vs2005.sln”
“myproject_vs2008.sln”
“myproject_vs2010.sln”

5. Run myproject.exe from the release or debug
directories located in
“NVIDIA GPU Computing SDK 4.0Cbinwin[32|64][release|debug]”.

6. Now modify the code to perform the computation you require. See the CUDA
Programming Guide for details of programming in CUDA.

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II. (b) Creating Your Own CUDA Projects for Linux
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Note: The default installation folder <SDK_INSTALL_PATH> is “~/NVIDIA_GPU_Computing_SDK”

Creating a new CUDA Program using the NVIDIA GPU Computing SDK infrastructure is easy.
We have provided a “template” or “template_runtime” project that you can copy and modify to suit your
needs. Just follow these steps:

1. Copy the template or template_runtime project

cd <SDK_INSTALL_PATH>/C/src
cp -r template <myproject>

or using template_runtime)

cd <SDK_INSTALL_PATH>/C/src
cp -r template_runtime <myproject>

2. Edit the filenames of the project to suit your needs

mv template.cu myproject.cu
mv template_kernel.cu myproject_kernel.cu
mv template_gold.cpp myproject_gold.cpp

or (using template_runtime)

mv main.cu myproject.cu

3. Edit the Makefile and source files. Just search and replace all occurrences
of “template” or “template_runtime” with “myproject”.

4. Build the project

make

You can build a debug version with “make dbg=1”.

5. Run the program

../../C/bin/linux/release/myproject

6. Now modify the code to perform the computation you require. See the
CUDA Programming Guide for details of programming in CUDA.

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II. (c) Creating Your Own CUDA Projects for Mac OSX
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Note: The default installation folder <SDK_INSTALL_PATH> is “/Developer/GPU Computing”

Creating a new CUDA Program using the NVIDIA GPU Computing SDK infrastructure is easy.
We have provided a “template” project that you can copy and modify to suit your
needs. Just follow these steps:

1. Copy the template project

cd <SDK_INSTALL_PATH>/C/src
cp -r template <myproject>

2. Edit the filenames of the project to suit your needs

mv template.cu myproject.cu
mv template_kernel.cu myproject_kernel.cu
mv template_gold.cpp myproject_gold.cpp

3. Edit the Makefile and source files. Just search and replace all occurrences
of “template” with “myproject”.

4. Build the project

make

You can build a debug version with “make dbg=1”.
5. Run the program

../../C/bin/darwin/release/myproject

(It should print “PASSED”)

6. Now modify the code to perform the computation you require. See the
CUDA Programming Guide for details of programming in CUDA.

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III. (a) Known Issues on CUDA SDK for Windows
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Note: Please see the CUDA Toolkit release notes for additional issues.

1. In code sample alignedTypes, the following aligned type does not provide
maximum throughput because of a compiler bug:
typedef struct __align__(16) {
unsigned int r, g, b;
} RGB32;
The workaround is to use the following type instead:
typedef struct __align__(16) {
unsigned int r, g, b, a;
} RGBA32;
as illustrated in the sample.

2. By default the CUDA 4.0 SDK will be installed to “ProgramDataNVIDIA CorporationNVIDIA GPU Computing SDK 4.0”,
so it will not have conflicts with Vista with UAC.

By default, UAC is enabled for Vista. If UAC is disabled, the user is free to install the
SDK in other folders.

Before the CUDA 2.1, the SDK installations path would be under:
“Program FilesNVIDIA CorporationNVIDIA CUDA SDK”.

Starting with CUDA 2.1, the new default installation folder is:
“Application DataNVIDIA CorporationNVIDIA CUDA SDK” residing under “All Users” or “Current”.

For NVIDIA GPU Computing 4.0 Release, the SDK installations path would be under:
“ProgramDataNVIDIA CorporationNVIDIA GPU Computing SDK 4.0”.

Starting with NVIDIA GPU Computing 4.0 Release, the new default installation folder is:
“ProgramDataNVIDIA CorporationNVIDIA GPU Computing SDK 4.0” residing under “All Users” or “Current”.

3. There are number of samples that are not pre-build with the CUDA SDK. Why are these samples not pre-built?
cudaOpenMP, simpleMPI, VFlockingD3D10, ExcelCUDA2007, ExcelCUDA2010

The samples may depend on other header and library packages to be installed on the development machine.
The CUDA SDK does not distribute these, hence these are not pre-built.

4. The following Direct3D samples are not officially supported on Telsa GPU:

cudaDecodeD3D9, fluidsD3D9,
simpleD3D9, simpleD3D9Texture,
simpleD3D10, simpleD3D10Texture,
simpleD3D11Texture, vFlockingD3D10

These samples will not run and report that a Direct3D device is not available.

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III. (b) Known Issues on CUDA SDK for Linux
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Note: Please see the CUDA Toolkit release notes for additional issues.

1. The SDK samples that make use of OpenGL fail to build or link. This is because many of the default
installations for many Linux distributions do not include the necessary OpenGL, GLUT, GLU, GLEW,
X11, Xi, Xlib, or Xmi headers or libraries. Here are some general and specific solutions:

(a) Redhat 4 Linux Distributions
“ld: cannot find -lglut”. On some linux installations, building the simpleGL example
show the following linking error like the following.

/usr/bin/ld: cannot find -lglut

Typically this is because the SDK makefiles look for libglut.so and not for
variants of it (like libglut.so.3). To confirm this is the problem, simply
run the following command.

ls /usr/lib | grep glut

ls /usr/lib64 | grep glut

You should see the following (or similar) output.

lrwxrwxrwx 1 root root 16 Jan 9 14:06 libglut.so.3 -> libglut.so.3.8.0
-rwxr-xr-x 1 root root 164584 Aug 14 2004 libglut.so.3.8.0

If you have libglut.so.3 in /usr/lib and/or /usr/lib64, simply run the following command
as root.

ln -s /usr/lib/libglut.so.3 /usr/lib/libglut.so
ln -s /usr/lib64/libglut.so.3 /usr/lib64/libglut.so

If you do NOT have libglut.so.3 then you can check whether the glut package
is installed on your RHEL system with the following command.

rpm -qa | grep glut

You should see “freeglut-2.2.2-14” or similar in the output. If not, you
or your system administrator should install the package “freeglut-2.2.2-14”.
Refer to the Red Hat and/or rpm documentation for instructions.

If you have libglut.so.3 but you do not have write access to /usr/lib, you
can also fix the problem by creating the soft link in a directory to which
you have write permissions and then add that directory to the library
search path (-L) in the Makefile.

(b) Some Linux distributions (i.e. Redhat or Fedora) do not include the GLU library.
You can find the latest packages download this file from this website. Please
make sure you match the correct Linux distribution.

http://fr.rpmfind.net/linux/rpm2html/search.php?query=libGLU.so.1&submit=Search+…

(c) (SLED11) SUSE Linux Enterprise Edition 11 is missing:
“libGLU”, “libX11” “libXi”, “libXm”

This particular version of SUSE Linux 11 does not have the proper symbolic links for the following libraries:

i. libGLU

ls /usr/lib | grep GLU
ls /usr/lib64 | grep GLU

libGLU.so.1
libGLU.so.1.3.0370300

To create the proper symbolic links (32-bit and 64-bit OS)

ln -s /usr/lib/libGLU.so.1 /usr/lib/libGLU.so
ln -s /usr/lib64/libGLU.so.1 /usr/lib64/libGLU.so

ii. libX11

ls /usr/lib | grep X11
ls /usr/lib64 | grep X11

libX11.so.6
libX11.so.6.2.0

To create the proper symbolic links (32-bit and 64-bit OS)

ln -s /usr/lib/libX11.so.6 /usr/lib/libX11.so
ln -s /usr/lib64/libX11.so.6 /usr/lib64/libX11.so

iii. libXi

ls /usr/lib | grep Xi
ls /usr/lib64 | grep Xi

libXi.so.6
libXi.so.6.0.0

To create the proper symbolic links (32-bit and 64-bit OS)

ln -s /usr/lib/libXi.so.6 /usr/lib/libXi.so
ln -s /usr/lib64/libXi.so.6 /usr/lib64/libXi.so

iv. libXm

ls /usr/lib | grep Xm
ls /usr/lib64 | grep Xm

libXm.so.6
libXm.so.6.0.0

To create the proper symbolic links (32-bit and 64-bit OS)

ln -s /usr/lib/libXm.so.6 /usr/lib/libXm.so
ln -s /usr/lib64/libXm.so.6 /usr/lib64/libXm.so

(d) Ubuntu Linux unable to build these SDK samples that use OpenGL

The default Ubuntu distribution is missing many libraries

i. What is missing are the GLUT, Xi, Xmu, GL, and X11 headers. To add these headers and
libraries to your distribution, type the following in at the command line.

sudo apt-get install freeglut3-dev build-essential libx11-dev libxmu-dev libxi-dev libgl1-mesa-glx libglu1-mesa libglu1-mesa-dev

ii. Note, by installing Mesa, you may see linking errors against libGL. This can be solved below:

cd /usr/lib/
sudo rm libGL.so
sudo ln -s libGL.so.1 libGL.so

2. In code sample alignedTypes, the following aligned type does not provide
maximum throughput because of a compiler bug:
typedef struct __align__(16) {
unsigned int r, g, b;
} RGB32;

The workaround is to use the following type instead:
typedef struct __align__(16) {
unsigned int r, g, b, a;
} RGBA32;
as illustrated in the sample.

3. Unable to build simpleMPI sample on Linux Distros
“simpleMPI.cpp:35:17: error: mpi.h: No such file or directory”

The linux system is missing the libraries and headers for MPI.

a. For OpenSuSE or RedHat distributions
– Search http://www.rpmfind.net for “openmpi-devel” for your specific distribution

For Ubuntu or Debian distributions, using “apt-get”
– sudo apt-get install build-essential openmpi-bin openmpi-dev

b. For 32-bit linux distributions

ln -s /usr/lib/mpi/gcc/openmpi/lib/libmpi_cxx.so.0 /usr/lib/libmpi_cxx.so
ln -s /usr/lib/mpi/gcc/openmpi/lib/libmpi.so.0 /usr/lib/libmpi.so
ln -s /usr/lib/mpi/gcc/openmpi/lib/libopen-rte.so.0 /usr/lib/libopen-rte.so
ln -s /usr/lib/mpi/gcc/openmpi/lib/libopen-pal.so.0 /usr/lib/libopen-pal.so

c. For 64-bit linux distributions

ln -s /usr/lib64/mpi/gcc/openmpi/lib64/libmpi_cxx.so.0 /usr/lib64/libmpi_cxx.so
ln -s /usr/lib64/mpi/gcc/openmpi/lib64/libmpi.so.0 /usr/lib64/libmpi.so
ln -s /usr/lib64/mpi/gcc/openmpi/lib64/libopen-rte.so.0 /usr/lib64/libopen-rte.so
ln -s /usr/lib64/mpi/gcc/openmpi/lib64/libopen-pal.so.0 /usr/lib64/libopen-pal.so

4. Fedora 13 or 14 will have linking error when building samples:
MonteCarloMultiGPU, simpleMultiGPU, threadMigration.

The following error is seen:

make -C src/threadMigration/
make[1]: Entering directory `/root/sdk/C/src/threadMigration’
/usr/bin/ld: obj/i386/release/threadMigration.cpp.o: undefined reference to symbol ‘pthread_create@@GLIBC_2.1’
/usr/bin/ld: note: ‘pthread_create@@GLIBC_2.1′ is defined in DSO /lib/libpthread.so.0 so try adding it to the linker command line
/lib/libpthread.so.0: could not read symbols: Invalid operation
collect2: ld returned 1 exit status
make[1]: *** [../../bin/linux/release/threadMigration] Error 1
make[1]: Leaving directory `/root/sdk/C/src/threadMigration’
make: *** [src/threadMigration/Makefile.ph_build] Error 2

For these linux distributions: Fedora 13 or 14, symbolic links are missing from the following
libraries:
a. libpthread

To create the proper symbolic links (32-bit OS and 64-bit OS) type this:

ln -s /usr/lib/libpthread.so.0 /usr/lib/libpthread.so
ln -s /usr/lib64/libpthread.so.0 /usr/lib64/libpthread.so

——————————————————————————–
III. (c) Known Issues on CUDA SDK for Mac OSX
——————————————————————————–

Note: In addition, please look at the CUDA Toolkit 4.0 Release notes for additional issues.

– Note on CUDA Mac 10.5.x (Leopard) or 10.6.x (Snow Leopard).
CUDA applications built with the CUDA driver API can run as either 32/64-bit applications.
CUDA applications using CUDA Runtime APIs can only be built on 32-bit applications.

– CUDA 3.1 Beta and newer now supports 10.6.3 (Snow Leopard) 64-bit Runtime API.

——————————————————————————–
IV. Frequently Asked Questions
——————————————————————————–

The Official CUDA FAQ is available online on the NVIDIA CUDA Forums:
http://forums.nvidia.com/index.php?showtopic=84440

Note: Please also see the CUDA Toolkit release notes for additional Frequently

Asked Questions.

——————————————————————————–
V. Addendum
——————————————————————————–

Important note about the use of ?volatile? in warp-synchronous code:
The optimized code in the samples ?reduction?, ?radixSort?, and ?scan? uses a technique known
as “warp-synchronous” programming, which relies on the fact that within a warp of threads
running on a CUDA GPU, all threads execute instructions synchronously. The code uses this to
avoid __syncthreads() when threads within a warp are sharing data via __shared__ memory.
It is important to note that for this to work correctly without race conditions on all GPUs, the
shared memory used in these warp-synchronous expressions must be declared “volatile”.
If it is not declared volatile, then in the absence of __syncthreads(), the compiler is free to
delay stores to __shared__ memory and keep the data in registers (an optimization technique),
which will result in incorrect execution. So please heed the use of “volatile” in these samples
and use it in the same way in any code you derive from them.

——————————————————————————–
VI. Change Log
——————————————————————————–

Note: The Change Logs apply to all OS platforms unless explicitly repoted

Release 4.0 Final (R270 Release Driver Update)
[All OSs]
* nBody SDK sample can now support multiple GPUs through new CUDA 4.0’s new API for multi-GPU support.
* Added 2 new documents to the Documentation:
“CUDA_SDK_New_Features_Guide.pdf”
“Getting_Started_With_CUDA_SDK_Samples.pdf”
* Updated CUDA and CUDALibraries individual SDK sample solution files (*.sln) for VS2005, VS2008, and VS2010.
Now the individual solutions include project dependencies with SDK helper libraries. If a SDK sample
depend on linking with shrUtils or cutil, the individual SDK samples can now be built without using
Release_vs20??.sln to properly build its dependencies.

[Windows OS]
* Visual Studio 2010 projects for CUDA C, CUDALibraries, and OpenCL are now included with the SDK.

Release 4.0 RC2 (R270 Release Driver Update)
[All OSs]
* Added simpleP2P SDK sample to illustrate how to use P2P and UVA. This requires a Tesla GPU with SM 2.0
(Tesla C2050/C2070). For Windows Vista/Win7, it must be running 64-bit and two or more GPUs must be
running the TCC driver. With linux, the standard driver will work on a Tesla GPU with SM 2.0 capabilities.
* CUDA SDK samples that use the Driver API now use the new cuLaunchKernel API.

[Windows OS]
* 49 of the CUDA C SDK samples include VS2010 projects for some of the projects. All of the remaining
samples will support Visual Studio 2010

Release 4.0 RC1 (R270 Release Driver Update)
[All OS]
* Changes to lineOfSight, marchingCubes, radixSort, particles, smokeParticles to use Thrust library (now included as part of the CUDA 4.0 Toolkit)
The SDK sample radixSort has been renamed to radixSortThrust with this change to use Thrust.
CUDPP libraries, headers, have been removed from the SDK. Thrust headers are now part of the CUDA Toolkit and are used by these SDK samples.
* Updated MonteCarloMultiGPU SDK sample to illlustrate the new CUDA 4.0 method for Multi-GPU programming. The sample has the ability to launch kernels on
multiple GPUs through a single CPU thread.
* Updated Simple Multi-GPU SDK sample to illustrate how to the new CUDA 4.0 method for CUDA context management and multi-threaded access to CUDA contexts.
* Updated matrixMul, simpleCUBLAS, batchCUBLAS SDK Sample to illustrate how to use the new CUDA 4.0 CUBLAS API interface.
* Added template_runtime, simple project that shows how to develop a CUDA project without the use of helper libraries or functions (i.e. cutil, shrUtil).
* Added conjugateGradientPrecond (sample showing how to implement a preconditioned conjugate gradient solver with CUBLAS and CUSPARSE).
* Added newdelete (sample that demonstrates GPU device memory alloc/free using C++).
* Added NPP SDK samples: boxFilterNPP, freeImageInteropNPP, histEqualizationNPP, imageSegmentationNPP
* Updated simpleSTreams SDK sample that demonstrates CUDA 4.0 capability of supporting generic pinning of system memory
* Added simpleLayeredTexture (sample that demonstrates how to use a new CUDA 4.0 feature to support texture arrays in CUDA).
* Updated ExcelCUDA2007 and ExcelCUDA2010 projects so they will build out of the box. ExcelCUDA2007 includes the necessary header/library files to build.
ExcelCUDA2010 requires the Microsoft Excel 2010 SDK which can be downloaded from the Microsoft developer website.

[Windows]
* Updated cudaEncode (H.264 Encode Sample) to support true 64-bit support of Device Pointers (CUdevicePtr)
* Updated cudaDecodeD3D9 and cudaDecodeGL samples to support 64-bit support of Device Pointers (CUdevicePtr)

Release 3.2 Release (R260 Release Driver Update)
[All OS]
* Added randomFog, uses CURAND to generate random values for Fog
* Added simpleSurfaceWrite, demonstrates how to write a CUDA kernel that handles Surface Writes

[Windows]
* The cudaDecodeGL handles multi-GPU configurations and can enumerate which one is a WDDM and TCC
device. Support to explicitly specify a GPU for decoding has also been added.
* The cudaEncode sample has been wrapped into a VideoEncoder class for ease of use. GPU Device
Memory Input support has also been added, as well as support for other YUV formats for input
(YUY2, UYVY, YV12, NV12, IYUV).
* Fixes cudaDecodeGL and cudaDecodeD3D9 sample so the 64-bit builds work with visualization (using 32-bit builds/device pointers)
Added support for better handling of multiple-GPU devices. Developers can now specify which GPU to run the decoding on.

Release 3.2 Beta (R260 Beta Driver Update)
[Windows]
* CUDA SDK Visual Studio projects have been revised to use environment variables $(CUDA_PATH) to reference the
CUDA Toolkit Installation path, includes, and compiler. The CUDA Tookit also installs NvCudaDriverAPI.rules
and NvCudaRuntimeApi.rules to refer to rules files which are versioned: NvCudaDriverApi.v3.2.rules and
NvCudaRuntimeApi.v.3.2.rules. To help with development migration from CUDA 3.1 to CUDA 3.2, the environment
paths CUDA_BIN_PATH, CUDA_INC_PATH, and CUDA_LIB_PATH are defined and set by the CUDA 3.2 toolkit.

Please refer to the Getting_Started_Windows.pdf on windows and the CUDA toolkit release notes for
more details in the CUDA Toolkit release notes.

* Added cudaEncode sample which demonstrates CUDA GPU accelerated video encoding of YUV to H.264 surface
* Added SLI D3D10 Texture sample that demonstrates improved performance for multi-GPU configurations.
* Added Interval Computing sample (illustrates how to use Recursion on Fermi Architecture)
* Added VFlocking (not-pre built) sample demonstrating a CUDA simulation of bird flocking behavior.

[All OS]
* Added MonteCarloCURAND samples for estimation of MonteCarlo Simulation with the NIVIDA CURAND libraries
MonteCarloCURANDEstimatePiInlineP
MonteCarloCURANDEstimatePiInlineQ
MonteCarloCURANDEstimatePiP
MonteCarloCURANDEstimatePiQ
MonteCarloCURANDSingleAsianOptionP
* Add bilateralFiltering sample
* Add conjugateGradient solver on the GPU using CUBLAS and CUSPARSE libraries
* Add simplePrintf (shows how to call cuprintf through device code)
* Updated DeviceQuery/DeviceQueryDrv samples to support determine if ECC/TCC is enabled for Tesla devices

* Added FunctionPointers (version of SobelFilter) sample, requires GPU based on Fermi architecture
* Updated BicubicTexture sample to include Catmull Rom

Release 3.1 Final (R256 Driver Update)
* Add support for 64-bit CUDA runtime for Mac OSX 10.6.3
* Added FunctionPointers (version of SobelFilter) sample, requires GPU based on Fermi architecture
* Updated BicubicTexture sample to include Catmull Rom
* Added excelCUDA – demonstrates how create an Excel plugin that can run CUDA kernels

Release 3.0 Final
* Replaced 3dfd sample with FDTD3d (Finite Difference sample has been updated)
* Added support for Fermi Architecture (Compute 2.0 profile) to the SDK samples
* Updated Graphics/CUDA samples to use the new unified graphics interop
* Several samples with Device Emulation have been removed. Device Emulation is
deprecated for CUDA 3.0, and will be removed with CUDA 3.1.
* Added new samples:
concurrentKernels (Fermi feature) – how to run more than 1 CUDA kernel simultaneously
simpleMultiCopy (Fermi feature) – exercises simulteaneous copy, compute, readback.
– GeForce has 1 copy engine, and Quadro/Tesla have 2 copy engines
simpleD3D11Texture – demonstrates Direct3D11 and CUDA interop
* Bug Fixes

Release 3.0 (R195 Beta 1 Release Update)
* Minor updates to the CUDA SDK samples
* Support for shrUtils (shared utilities, useful for logging information)

Release 2.3 (R190 Release Update)
* New SDK Samples
– vectorAdd, vectorAddDrv – Two samples (Runtime and Driver API) which
implements element by element vector addition. These simple samples do
not use the CUTIL library.

[Linux SDK]
* Support for Cross Compilation
– Adding the following during Make will build for specific targets
x86_64=1 (for 64-bit targets)
i386=1 (for 32-bit targets)
– Note on Linux, the 190.18 or newer driver installs the necessary libcuda.so for both 32-bit and 64-bit targets.

Release 3.0 Beta 1
* New Samples
– 3DFD – 3D Finite Difference sample demonstrates 3DFD stencil computation on a regular grid.
– matrixMulDynlinkJIT – Matrix Multiply sample that uses PTXJIT (inlined) and also supports dynamic linking to nvcuda.dll
– bitonicSort has been renamed to sortingNetworks
– histogram64 and histogram256 have been combined to a single project histogram
* Removed
– scanLargeArray removed from the SDK, consolidated to scan

Release 3.0 Beta 1 Beta
* Added PTXJIT
– New SDK sample that illustrates how to use cuModuleLoadDataEx
– Loads a PTX source file from memory instead of file.
* Windows SDK only
– Changing the name NVIDIA CUDA SDK -> NVIDIA GPU Computing SDK
– Added cudaDecodeD3D9 and cudaDecodeGL
– NVCUVID now support for OpenGL interop, these two new SDK samples
illustrates how to use NVCUVID to decode MPEG-2, VC-1, or H.264 content.
– The samples show how to decode video and pass to D3D9 or OpenGL,
however these samples do not display the decoded video frames.

Release 2.2.1
[Windows SDK]
* Updated Cuda.Rules files (no long uses -m32) to generate CUBIN/PTX output,
* Cuda.rules option to generate PTX and to inline CUDA source with PTX
generated assembly
[Mac and Linux SDK]
* Updated common.mk file to removed -m32 when generating CUBIN output
* Support for PTX output has been added to common.mk

[All SDK Packages]
* CUDA Driver API samples: simpleTextureDrv, matrixMulDrv, and threadMigration
have been updated to reflect changes:
– Previously when compiling these CUDA SDK samples, gcc would generate a
compilation error when building on a 64-bit Linux OS if the 32-bit glibc
compatibility libraries were not previously installed. This SDK release
addresses this problem. The CUDA Driver API samples have been modified
and solve this problem by casting device pointers correctly before
being passed to CUDA kernels.
– When setting parameters for CUDA kernel functions, the address offset
calculation is now properly aligned so that CUDA code and applications
will be compatible on 32-bit and 64-bit Linux platforms.
– The new CUDA Driver API samples by default build CUDA kernels with the
output as PTX instead of CUBIN. The CUDA Driver API samples now use
PTXJIT to load the CUDA kernels and launch them.

* Added sample pitchLinearTexture that shows how to texture from pitch linear
memory

Release 2.2 Final
[Windows and Linux SDK]
* Supports CUDA Event Blocking Stream Synchronization (CU_CTX_BLOCKING_SYNC) on Linux and Windows

[All SDK Packages]
* Added Mandelbrot (Julia Set), deviceQueryDrv, radixSort, SobolQRNG, threadFenceReduction
* New CUDA 2.2 capabilities:
– supports zero-memory copy (GT200, MCP79)
* simpleZeroCopy SDK sample
– supports OS allocated pinned memory (write combined memory). Test this by:
> bandwidthTest -memory=PINNED -wc

Release 2.1
[Windows SDK]
* Projects that depend on paramGL now build the paramGL source files instead of
statically linking with paramGL*.lib.

[All SDK Packages]
* CUDA samples that use OpenGL interop now call cudaGLSetGLDevice after the GL context is created.
This ensures that OpenGL/CUDA interop gets the best possible performance possible.
* Bug fixes

Release 2.1 Beta
[Windows SDK]
* Now supports Visual Studio 2008 projects, all samples also include VS2008
* Removed Visual Studio 2003.NET projects
* Added Visual Studio CUDA.rules to support *.cu files. Most projects now use this
rule with VS2005 and VS2008 projects.
* Default CUDA SDK installation folder is under “All Users” or “Current User” in a sub-folder
“Application DataNVIDIA CorporationNVIDIA GPU Computing SDK”. See section “III. Known issues” for
more details.

[Mac and Linux SDK]
* For CUDA samples that use the Driver API, you must install the Linux 32-bit
compatibility (glibc) binaries on Linux 64-bit Platforms. See Known issues in about
section IV on how to do this.

[All SDK Packages]
* Added CUDA smokeParticles (volumetric particle shadows samples)
* Note: added cutil_inline.h for CUDA functions as an alternative to using the
cutil.h macro definitions

Release 2.0 Beta2
[Windows SDK]
* 2 new code samples:
cudaVideoDecode and simpleVoteIntrinsics

Release 2.0 Beta
[All SDK Packages]
* Updated to the 2.0 CUDA Toolkit
* CUT_DEVICE_INIT macro modified to take command line arguments. All samples now
support specifying the CUDA device to run on from the command line (?-device=n?).
* deviceQuery sample: Updated to query number of multiprocessors and overlap
flag.
* multiGPU sample: Renamed to simpleMultiGPU.
* reduction, MonteCarlo, and binomialOptions samples: updated with optional
double precision support for upcoming hardware.
* simpleAtomics sample: Renamed to simpleAtomicIntrinsics.
* 7 new code samples:
dct8x8, quasirandomGenerator, recursiveGaussian, simpleD3D9Texture,
simpleTexture3D, threadMigration, and volumeRender

[Windows SDK]
* simpleD3D sample: Renamed to simpleD3D9 and updated to the new Direct3D
interoperability API.
* fluidsD3D sample: Renamed to fluidsD3D9 and updated to the new Direct3D
interoperability API.

Release 1.1
* Updated to the 1.1 CUDA Toolkit
* Removed isInteropSupported() from cutil: graphics interoperability now works
on multi-GPU systems
* MonteCarlo sample: Improved performance. Previously it was very fast for
large numbers of paths and options, now it is also very fast for
small- and medium-sized runs.
* Transpose sample: updated kernel to use 2D shared memory array for clarity,
and optimized bank conflicts.
* 15 new code samples:
asyncAPI, cudaOpenMP, eigenvalues, fastWalshTransform, histogram256,
lineOfSight, Mandelbrot, marchingCubes, MonteCarloMultiGPU, nbody, oceanFFT,
particles, reduction, simpleAtomics, and simpleStreams

Release 1.0
* Added support for CUDA on the MAC
* Updated to the 1.0 CUDA Toolkit.
* Added 4 new code samples: convolutionTexture, convolutionFFT2D,
histogram64, and SobelFilter.
* All graphics interop samples now call the cutil library function
isInteropSupported(), which returns false on machines with multiple CUDA GPUs,
currently (see above).
* When compiling in DEBUG mode, CU_SAFE_CALL() now calls cuCtxSynchronize() and
CUDA_SAFE_CALL() and CUDA_CHECK_ERROR() now call cudaThreadSynchronize() in
order to return meaningful errors. This means that performance might suffer in
DEBUG mode.

Release 0.9
* Updated to version 0.9 of the CUDA Toolkit.
* Added 6 new code samples: MersenneTwister, MonteCarlo, imageDenoising,
simpleTemplates, deviceQuery, alignedTypes, and convolutionSeparable.
* Removed 3 old code samples:
– vectorLoads and loadUByte replaced by alignedTypes;
– convolution replaced by convolutionSeparable.

Release 0.8.1 beta
* Standardized project and file naming conventions. Several project names
changed as a result.
* cppIntegration output now matches the other samples (“Test PASSED”).
* Modified transpose16 sample to transpose arbitrary matrices efficiently, and
renamed it to transpose.
* Added 11 new code samples: bandwidthTest, binomialOptions, BlackScholes,
boxFilter, convolution, dxtc, fluidsGL, multiGPU, postProcessGL,
simpleTextureDrv, and vectorLoads.

Release 0.8 beta
* First public release.

——————————————————————————–
VII. OS Platforms and Compilers Supported
——————————————————————————–

[Windows Platforms]
OS Platform Support to CUDA 2.2
* Vista 32 & 64bit, WinXP 32 & 64-bit
o Visual Studio 8 (2005)
o Visual Studio 9 (2008)

OS Platform Support added to CUDA 3.0 Release
* Windows 7 32 & 64
* Windows Server 2008 and 2008 R2

Compiler Support added to CUDA 4.0 Release
o Visual Studio 10 (2010)

[Mac Platforms]
OS Platform Support added to CUDA 2.2
* MacOS X Leopard 10.5.6+ (32-bit)
o (llvm-)gcc 4.2 Apple

OS Platform Support added to CUDA 3.0 Beta 1
* MacOS X SnowLeopard 10.6 (32-bit)

OS Platform Support added to CUDA 3.0 Release
* MacOS X SnowLeopard 10.6.x
32/64-bit for CUDA Driver API
32-bit for CUDA Runtime API

OS Platform Support added to CUDA 3.1 Beta
* MacOS X SnowLeopard 10.6.3
32/64-bit for CUDA Driver API
32/64-bit for CUDA Runtime API

OS Platform Support added to CUDA 3.2
* MacOS X SnowLeopard 10.6.4
* MacOS X SnowLeopard 10.6.5

OS Platform Support added to CUDA 4.0
* MacOS X SnowLepard 10.6.6
* MacOS X SnowLepard 10.6.7

[Linux Platforms]
OS Platform Support added to CUDA 3.0
* Linux Distributions 32 & 64:
RHEL-4.x (4.8),
RHEL-5.x (5.3),
SLED-11
Fedora10,
Ubuntu 9.04,
OpenSUSE 11.1
o gcc 3.4, gcc 4

OS Platform Support added to CUDA 3.1
* Additional Platform Support Linux 32 & 64:
Fedora 12,
OpenSUSE-11.2,
Ubuntu 9.10
RHEL-5.4
* Platforms no longer supported
Fedora 10,
OpenSUSE-11.1,
Ubuntu 9.04

OS Platform Support added to CUDA 3.2
* Additional Platform Support Linux 32 & 64:
Fedora 13,
Ubuntu 10.04,
RHEL-5.5,
SLED-11SP1,
ICC (64-bit linux only?)
* Platforms no longer supported
Fedora 12,
Ubuntu 9.10
RHEL-5.4,
SLED11

OS Platform Support added to CUDA 4.0
* Additional Platform Support Linux 32 & 64:
SLES11-SP,
Fedora 14,
Ubuntu 10.10,
OpenSUSE-11.3
RHEL-6.0 (64-bit only),
ICC (64-bit linux only?)
* Platforms no longer supported
RHEL-4.8,
Ubuntu 10.04,
Fedora 13,
OpenSUSE-11.2,
SLED11-SP1

以上