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NVIDIA CUDA Toolkit v4.2 Release Notes for Windows, Linux, and Mac OS X
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CONTENTS
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-- Release Highlights
-- Documentation
-- List of Important Files
-- Supported NVIDIA hardware
-- Supported Operating Systems
---- Windows
---- Linux
---- Mac OS X
-- Installation Notes
-- New Features
-- Resolved Issues
-- Known Issues
-- Source Code for Open64 and CUDA-GDB
-- Revision History
-- More Information
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Release Highlights
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* Added support for GK10x Kepler GPUs.
* This release contains the following:
- NVIDIA CUDA Toolkit documentation
- NVIDIA OpenCL documentation
- NVIDIA CUDA compiler (nvcc) and supporting tools
- NVIDIA CUDA runtime libraries
- NVIDIA CUDA-GDB debugger
- NVIDIA CUDA-MEMCHECK
- NVIDIA Visual Profiler
- NVIDIA CUBLAS, CUFFT, CUSPARSE, CURAND, Thrust, and NPP libraries
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Documentation
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For a list of documents supplied with this release, please refer to the
/doc directory of your CUDA Toolkit installation.
NOTE: The NVML development package is not shipped with CUDA 4.2. For
changes related to nvidia-smi and NVML, please refer to the nvidia-smi man
page and the "Tesla Deployment Kit" package located on the developer site
http://developer.nvidia.com/tesla-deployment-kit; NVML documentation and the
SDK are included.
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List of Important Files
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bin/
nvcc CUDA C/C++ compiler
cuda-gdb CUDA Debugger
cuda-memcheck CUDA Memory Checker
nvvp NVIDIA Visual Profiler
(On Windows, nvvp is located in libnvvp/)
include/
cuda.h CUDA driver API header
cudaGL.h CUDA OpenGL interop header for driver API
cudaVDPAU.h CUDA VDPAU interop header for driver API
(Linux only)
cuda_gl_interop.h CUDA OpenGL interop header for toolkit API
(Linux only)
cuda_vdpau_interop.h CUDA VDPAU interop header for toolkit API
(Linux only)
cudaD3D9.h CUDA DirectX 9 interop header (Windows only)
cudaD3D10.h CUDA DirectX 10 interop header (Windows only)
cudaD3D11.h CUDA DirectX 11 interop header (Windows only)
cufft.h CUFFT API header
cublas_v2.h CUBLAS API header
cublas.h CUBLAS Legacy API header
cusparse_v2.h CUSPARSE API header
cusparse.h CUSPARSE Legacy API header
curand.h CURAND API header
curand_kernel.h CURAND device API header
thrust/* Thrust Headers
npp.h NPP API Header
nvcuvid.h CUDA Video Decoder header (Windows and Linux)
cuviddec.h CUDA Video Decoder header (Windows and Linux)
NVEncodeDataTypes.h CUDA Video Encoder (C-library or DirectShow)
(Windows only)
NVEncodeAPI.h CUDA Video Encoder (C-library) (Windows only)
INvTranscodeFilterGUIDs.h CUDA Video Encoder (DirectShow) (Windows only)
INVVESetting.h CUDA Video Encoder (DirectShow) (Windows only)
extras/
CUPTI CUDA Profiling APIs
Debugger CUDA Debugger APIs
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Windows lib files
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lib/
cuda.lib CUDA driver library
cudart.lib CUDA runtime library
cublas.lib CUDA BLAS library
cufft.lib CUDA FFT library
cusparse.lib CUDA Sparse Matrix library
curand.lib CUDA Random Number Generation library
npp.lib NVIDIA Performance Primitives library
nvcuvenc.lib CUDA Video Encoder library
nvcuvid.lib CUDA Video Decoder library
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Linux lib files
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lib/
libcuda.so CUDA driver library
libcudart.so CUDA runtime library
libcublas.so CUDA BLAS library
libcufft.so CUDA FFT library
libcusparse.so CUDA Sparse Matrix library
libcurand.so CUDA Random Number Generation library
libnpp.so NVIDIA Performance Primitives library
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Mac OS X lib files
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lib/
libcuda.dylib CUDA driver library
libcudart.dylib CUDA runtime library
libcublas.dylib CUDA BLAS library
libcufft.dylib CUDA FFT library
libcusparse.dylib CUDA Sparse Matrix library
libcurand.dylib CUDA Random Number Generation library
libnpp.dylib NVIDIA Performance Primitives library
libtlshook.dylib NVIDIA internal library
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Supported NVIDIA Hardware
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See http://www.nvidia.com/object/cuda_gpus.html.
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Supported Operating Systems for Windows, Linux, and Mac OS X
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Windows
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* Supported Operating Systems (32-bit and 64-bit)
OS
--
Windows Server 2008
Windows XP
Windows Vista
Windows 7
* Supported Compilers
Platform Compiler IDE
-------- -------- ---
Windows MSVC8(14.00) VS 2005
Windows MSVC9(15.00) VS 2008
Windows MSVC2010(16.00) VS 2010
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Linux
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The CUDA development environment relies on tight integration with the host
development environment, including the host compiler and C runtime libraries,
and is therefore only supported on distro versions that have been qualified
for this CUDA Toolkit release.
* Supported Distros
Distro 32 64 Kernel GCC GLIBC
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Fedora14 X X 2.6.35.6-45 4.5.1 2.12.90
ICC Compiler 11.1 X X
OpenSUSE-11.2 X X 2.6.31.5-0.1 4.4.1 2.10.1
RHEL-5.>=5 X X 2.6.18-238.el5 4.1.2 2.5
(5.5, 5.6, 5.7)
RHEL-6.X X 2.6.32- 4.4.5 2.12
(6.0, 6.1) 131.0.15.el6
SLES 11.1 X X 2.6.32.12-0.7-pae 4.3-62.198 2.11.1-0.17.4
Ubuntu-10.04 X X 2.6.35-23-generic 4.4.5 2.12.1
Ubuntu-11.04 X X 2.6.38-8-generic 4.5.2 2.13
* Distros No Longer Supported
Distro 32 64 Kernel GCC GLIBC
------ -- -- ------ --- -----
Fedora13 X X 2.6.33.3-85 4.4.4 2.12
RHEL-4.8 X 2.6.9-89.ELsmpl 3.4.6 2.3.4
Ubuntu-10.10 X X 2.6.35-23-generic 4.4.5 2.12.1
NOTE: 32-bit versions of RHEL 4.8 and RHEL 6.0 have not been tested with
this release and are therefore not supported in this CUDA Toolkit release.
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Mac OS X
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Platform 32 64 GCC
-------- -- -- ---
Mac OS X 10.7 X X 4.2.1 (build 5646)
Mac OS X 10.6 X X 4.2.1 (build 5646)
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Installation Notes
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Windows
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Silent Installation:
- To install, use msiexec.exe from the shell and pass these arguments:
msiexec.exe /i cudatoolkit.msi /qn
- To uninstall, use /x instead of /i.
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Linux
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* In order to run CUDA applications, the CUDA module must be loaded and the
entries in /dev created. This may be achieved by initializing X Windows, or
by creating a script to load the kernel module and create the entries. An
example script (to be run at boot time):
#!/bin/bash
/sbin/modprobe nvidia
if [ "$?" -eq 0 ]; then
# Count the number of NVIDIA controllers found.
N3D=`/sbin/lspci | grep -i NVIDIA | grep "3D controller" | wc -l`
NVGA=`/sbin/lspci | grep -i NVIDIA | grep "VGA compatible controller" | wc -l`
N=`expr $N3D + $NVGA - 1`
for i in `seq 0 $N`; do
mknod -m 666 /dev/nvidia$i c 195 $i;
done
mknod -m 666 /dev/nvidiactl c 195 255
else
exit 1
fi
* On some Linux releases, due to a GRUB bug in the handling of upper memory
and a default vmalloc too small on 32-bit systems, it may be necessary to
pass this information to the bootloader:
vmalloc=256MB, uppermem=524288
Example of grub conf:
title Red Hat Desktop (2.6.9-42.ELsmp)
root (hd0,0)
uppermem 524288
kernel /vmlinuz-2.6.9-42.ELsmp ro root=LABEL=/1 rhgb quiet vmalloc=256MB
pci=nommconf
initrd /initrd-2.6.9-42.ELsmp.img
* Pinned memory in CUDA is only supported on Linux kernel versions >= 2.6.18.
Host side memory allocations pinned for CUDA using cudaHostRegister() API can
be passed to 3rd party drivers. Pinned memory allocations returned from
cudaHostAlloc() and cudaMallocHost() can also be passed to 3rd party drivers
and starting with 4.1, CUDA_NIC_INTEROP is no longer needed on these APIs;
thus this flag is now deprecated.
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New Features
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Support for GK10x Kepler GPUs.
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Resolved Issues
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* In the routines cusparse<T>csr2hyb and cusparse<T>dense2hyb, upon the
occurrence of an error (typically a device memory allocation problem), the
handle to the hybrid format descriptor (cusparseHybMat_t) was wrongly
destroyed using cusparseDestroyHybMat. A subsequent call to
cusparseDestroyHybMat by the user would then result in an error. This issue
has been fixed in the 4.2 toolkit and now the user can and should call
cusparseDestroyHybMat to clean up, either after an error or when the matrix
is no longer needed.
* CUDA-MEMCHECK now explicitly reports calls to assert() inside a CUDA kernel.
* The version of Thrust included with the CUDA toolkit has been upgraded from
1.5.1 to 1.5.2.
* Rotate primitives falsely used to enforce that the source image's pitch
(nSrcStep) was large enough to accommodate the destination ROI's size. This
bug was fixed and the restriction no longer exists.
* Starting with CUDA Toolkit 4.0, cublasDestroy did not properly free all of
the GPU resources, leading to a GPU memory leak of about 256 KB per CUBLAS
handle. This could also lead to GPU memory fragmentation when the unreleased
resources were scattered over the GPU memory. This issue has been resolved
in the 4.2 Toolkit.
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Known Issues
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Windows
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* In the NPP library, the nppiGraphcut_32s8u() and nppiGraphcut8_32s8u()
primitives may fail with an error while running on a GPU that supports the
sm1.0 architecture, especially on systems with a 64-bit operating system.
* Individual kernels are limited to a 2-second runtime by Windows Vista.
Kernels that run for longer than 2 seconds will trigger the Timeout
Detection and Recovery (TDR) mechanism. For more information, see
http://www.microsoft.com/whdc/device/display/wddm_timeout.mspx.
* The maximum size of a single memory allocation created by cudaMalloc or
cuMemAlloc on WDDM devices is limited to:
MIN( (System Memory Size in MB - 512 MB) / 2, PAGING_BUFFER_SEGMENT_SIZE )
For Vista, PAGING_BUFFER_SEGMENT_SIZE is approximately 2GB.
* (Windows and Linux) Individual GPU program launches are limited to a run
time of less than 5 seconds on a GPU with a display attached. Exceeding this
time limit usually causes a launch failure reported through the CUDA driver
or the CUDA runtime. GPUs without a display attached are not subject to the
5 second runtime restriction. For this reason it is recommended that CUDA
be run on a GPU that is NOT attached to a display and does not have the
Windows desktop extended onto it. In this case, the system must contain at
least one NVIDIA GPU that serves as the primary graphics adapter.
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Linux & Mac
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* In the NPP library, the nppiGraphcut_32s8u() and nppiGraphcut8_32s8u()
primitives may fail with an error while running on a GPU that supports the
sm1.0 architecture, especially on systems with a 64-bit operating system.
* The Linux kernel provides a mode where it allows user processes to
overcommit system memory. (Refer to kernel documentation for /proc/sys/vm/
for details). If this mode is enabled (the default on many distros) the
kernel may have to kill processes in order to free up pages for allocation
requests. The CUDA driver process, especially for CUDA applications that
allocate lots of zero-copy memory with cuMemHostAlloc or cudaMallocHost,
is particularly vulnerable to being killed in this way. Since there is no
way for the CUDA SW stack to report an OOM error to the user before the
process disappears, users, especially on 32-bit Linux, are encouraged to
disable memory overcommit in their kernel to avoid this problem.
Please refer to documentation on vm.overcommit_memory and
vm.overcommit_ratio for more information.
* When compiling with GCC, special care must be taken for structs that contain
64-bit integers. This is because GCC aligns long longs to a 4 byte boundary
by default, while NVCC aligns long longs to an 8 byte boundary by default.
Thus, when using GCC to compile a file that has a struct/union, users must
give the -malign-double option to GCC. When using NVCC, this option is
automatically passed to GCC.
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Mac
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To save power, some Apple products automatically power-down the CUDA-capable
GPU in the system. If the operating system has powered down the CUDA-capable
GPU, CUDA fails to run and the system returns an error that no device was
found. In order to ensure that your CUDA-capable GPU is not powered down by
the operating system do the following:
1. Go to "System Preferences"
2. Open the "Energy Saver" section
3. Un-check the "Automatic graphics switching" check box in the upper left
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Visual Profiler & Command Line Profiler
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* Visual Profiler fails to generate events or counter information. There are
several reasons why Visual Profiler may fail to gather counter information:
a. If more than one tool is trying to access the GPU. To fix this issue
please make sure only one tool is using the GPU at any given point. Tools
include the CUDA command line profiler, Parallel NSight Analysis Tools and
Graphics Tools, and applications that use either CUPTI or PerfKit API
(NVPM) to read counter values.
b. If more than one application is using the GPU at the same time when Visual
Profiler is profiling a CUDA application. To fix this issue please close
all applications and just run the one with Visual Profiler. Interacting
with the active desktop should be avoided while the application is
generating counter information. Please note that Visual Profiler gathers
counters for only one context if the application is using multiple
contexts within the same application.
* Enabling "{gld|gst} instructions {8|16|32|64|128}bit" counters can cause
GPU kernels to run longer than the driver's watchdog timeout limit. In these
cases the driver will terminate the GPU kernel resulting in an application
error and profiling data will not be available. Please disable driver
watchdog timeout before profiling such long running CUDA kernels.
- On Linux, setting the X Config option 'Interactive' to false is recommended.
- For Windows, detailed information on disabling the Windows TDR is available
at: http://msdn.microsoft.com/en-us/windows/hardware/gg487368.aspx#E2.
* On Windows Vista/Win7 profiling an application which makes more than 32K
CUDA kernel launch, memory copy, or memory set API calls without a
synchronization call can result in an application hang. To work around
this issue add synchronization calls like cudaDeviceSynchronize()
or cudaStreamSynchronize().
* Enabling counters on GPUs with compute capability (SM type) 1.x can result
in occasional hangs. Please disable counters on such runs.
* The "warp serialize" counter for GPUs with compute capability 1.x is known
to give incorrect and high values for some cases.
* Prof triggers are not supported on GPUs with compute capability (SM type) 1.0.
* Profiler data gets flushed to a file only at synchronization calls like
cudaDeviceSynchronize() and cudaStreamSynchronize() or when the profiler
buffer gets full. If an app terminates without these sync calls then
profiler data may be lost.
* Counters gld_incoherent and gst_incoherent always return zero on GPUs
with compute capability (SM type) 1.3. A value of zero doesn't mean that
all load/stores are 100% coalesced.
* Use Visual Profiler version 4.1 onwards with driver version 285
(or later).Due to compatibility issues with profile counters, Visual
Profiler 4.0 (or earlier) must not be used with driver version 285 (or later).
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Source Code for Open64 and CUDA-GDB
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* The Open64 and CUDA-GDB source files are controlled under terms of the
GPL license. Current and previously released versions are located at:
ftp://download.nvidia.com/CUDAOpen64
* Linux users:
- Please refer to the "Release Notes" and "Known Issues" sections in the
CUDA-GDB User Manual (cuda-gdb.pdf).
- Please refer to cuda-memcheck.pdf for notes on supported error detection
and known issues.
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Revision History
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-- 04/2012 - Version 4.2
-- 01/2012 - Version 4.1 Production
-- 11/2011 - Version 4.1 RC2
-- 10/2011 - Version 4.1 RC1
-- 09/2011 - Version 4.1 EA (Information in ReadMe.txt)
-- 05/2011 - Version 4.0
-- 04/2011 - Version 4.0 RC2 [Errata]
-- 02/2011 - Version 4.0 RC
-- 11/2010 - Version 3.2
-- 10/2010 - Version 3.2 RC2
-- 09/2010 - Version 3.2 RC
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More Information
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* For more information and help with CUDA, please visit:
http://www.nvidia.com/cuda
* Please refer to the LLVM Release License text in EULA.txt for details on
LLVM licensing.
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