1. Kepler Compatibility
1.1. About this Document
This application note, Kepler Compatibility Guide for CUDA Applications, is intended to help developers ensure that their NVIDIA® CUDA™ applications will run effectively on GPUs based on the NVIDIA® Kepler Architecture. This document provides guidance to developers who are already familiar with programming in CUDA C/C++ and want to make sure that their software applications are compatible with Kepler.
1.2. Application Compatibility on Kepler
The NVIDIA CUDA C compiler, nvcc, can be used to generate both architecture-specific cubin files and forward-compatible PTX versions of each kernel. Each cubin file targets a specific compute-capability version and is forward-compatible only with GPU architectures of the same major version number. For example, cubin files that target compute capability 2.0 are supported on all compute-capability 2.x (Fermi) devices but are not supported on compute-capability 3.x (Kepler) devices. For this reason, to ensure forward compatibility with GPU architectures introduced after the application has been released, it is recommended that all applications support launching PTX versions of their kernels.1
Applications that already include PTX versions of their kernels should work as-is on Kepler-based GPUs. Applications that only support specific GPU architectures via cubin files, however, will need to be updated to provide Kepler-compatible PTX or cubins.
1.3. Verifying Kepler Compatibility for Existing Applications
The first step is to check that Kepler-compatible device code (at least PTX) is compiled in to the application. The following sections show how to accomplish this for applications built with different CUDA Toolkit versions.
1.3.1. Applications Using CUDA Toolkit 4.1 or Earlier
- Download and install the latest driver from http://www.nvidia.com/drivers.
- Set the environment variable CUDA_FORCE_PTX_JIT=1.
- Create an empty temporary directory on your system.
- Set the environment variable CUDA_CACHE_PATH to be the path to this empty directory.
- Launch your application.
When starting a CUDA application for the first time with the above environment flag, the CUDA driver will JIT-compile the PTX for each CUDA kernel that is used into native cubin code. The generated cubin for the target GPU architecture is cached on disk by the CUDA driver.
If you set the environment variables above and then launch your program and it works properly, and if the directory you specified with the CUDA_CACHE_PATH environment variable is now populated with cache files, then you have successfully verified Kepler compatibility. Note that it is not necessary to inspect the contents of the cache files themselves; just check that the previously empty cache directory is now non-empty.
Be sure to unset these two environment variables when you are done testing if you do not normally use them. The temporary cache directory you created is safe to delete.
1.3.2. Applications Using CUDA Toolkit 5.0
CUDA applications built using CUDA Toolkit 5.0 are compatible with Kepler as long as they are built to include kernels in either Kepler-native cubin format (see Building Applications with Kepler Support) or PTX format (see Applications Using CUDA Toolkit 4.1 or Earlier) or both.
1.4. Building Applications with Kepler Support
When a CUDA application launches a kernel, the CUDA Runtime determines the compute capability of each GPU in the system and uses this information to automatically find the best matching cubin or PTX version of the kernel that is available. If a cubin file supporting the architecture of the target GPU is available, it is used; otherwise, the CUDA Runtime will load the PTX and JIT-compile that PTX to the GPU's native cubin format before launching it. If neither is available, then the kernel launch will fail.
The method used to build your application with either native cubin or at least PTX support for Kepler depend on the version of the CUDA Toolkit used.
- It saves the end user the time it takes to PTX JIT a kernel that has been compiled as PTX. (However, since the CUDA driver will cache the cubin generated as a result of the PTX JIT, this is mostly a one-time cost for a given user.)
- PTX JIT-compiled kernels often cannot take advantage of architectural features of newer GPUs, meaning that native-compiled code may be faster or of greater accuracy.
1.4.1. Applications Using CUDA Toolkit 4.1 or Earlier
The compilers included in CUDA Toolkit 4.1 or earlier generate cubin files native to earlier NVIDIA architectures such as Fermi, but they cannot generate cubin files native to the Kepler architecture. To allow support for Kepler and future architectures when using version 4.1 or earlier of the CUDA Toolkit, the compiler must generate a PTX version of each kernel.
Below are compiler settings that could be used to build mykernel.cu to run on Fermi and earlier devices natively and on Kepler devices via PTX JIT.
Note that compute_XX refers to a PTX version and sm_XX refers to a cubin version. The arch= clause of the -gencode= command-line option to nvcc specifies the front-end compilation target and must always be a PTX version. The code= clause specifies the back-end compilation target and can either be cubin or PTX or both. Only the back-end target version(s) specified by the code= clause will be retained in the resulting binary; at least one must be PTX to provide Kepler compatibility.
Windows
nvcc.exe -ccbin "C:\vs2008\VC\bin" -Xcompiler "/EHsc /W3 /nologo /O2 /Zi /MT" -gencode=arch=compute_10,code=sm_10 -gencode=arch=compute_20,code=sm_20 -gencode=arch=compute_20,code=compute_20 --compile -o "Release\mykernel.cu.obj" "mykernel.cu"
Mac/Linux
/usr/local/cuda/bin/nvcc -gencode=arch=compute_10,code=sm_10 -gencode=arch=compute_20,code=sm_20 -gencode=arch=compute_20,code=compute_20 -O2 -o mykernel.o -c mykernel.cu
Alternatively, you may be familiar with the simplified nvcc command-line option -arch=sm_XX, which is a shorthand equivalent to the following more explicit -gencode= command-line options used above. -arch=sm_XX expands to the following:
-gencode=arch=compute_XX,code=sm_XX -gencode=arch=compute_XX,code=compute_XX
However, while the -arch=sm_XX command-line option does result in inclusion of a PTX back-end target by default, it can only specify a single target cubin architecture at a time, and it is not possible to use multiple -arch= options on the same nvcc command line, which is why the examples above use -gencode= explicitly.
1.4.2. Applications Using CUDA Toolkit 5.0
With version 5.0 of the CUDA Toolkit, nvcc can generate cubin files native to the Kepler architecture (compute capability 3.x). When using CUDA Toolkit 5.0, to ensure that nvcc will generate cubin files for all released GPU architectures as well as a PTX version for forward compatibility with future GPU architectures, specify the appropriate -gencode= parameters on the nvcc command line as shown in the examples below.
Windows
nvcc.exe -ccbin "C:\vs2008\VC\bin" -Xcompiler "/EHsc /W3 /nologo /O2 /Zi /MT" -gencode=arch=compute_10,code=sm_10 -gencode=arch=compute_20,code=sm_20 -gencode=arch=compute_30,code=sm_30 -gencode=arch=compute_35,code=sm_35 -gencode=arch=compute_35,code=compute_35 --compile -o "Release\mykernel.cu.obj" "mykernel.cu"
Mac/Linux
/usr/local/cuda/bin/nvcc -gencode=arch=compute_10,code=sm_10 -gencode=arch=compute_20,code=sm_20 -gencode=arch=compute_30,code=sm_30 -gencode=arch=compute_35,code=sm_35 -gencode=arch=compute_35,code=compute_35 -O2 -o mykernel.o -c mykernel.cu
Note that compute_XX refers to a PTX version and sm_XX refers to a cubin version. The arch= clause of the -gencode= command-line option to nvcc specifies the front-end compilation target and must always be a PTX version. The code= clause specifies the back-end compilation target and can either be cubin or PTX or both. Only the back-end target version(s) specified by the code= clause will be retained in the resulting binary; at least one should be PTX to provide compatibility with future architectures.
A. Revision History
Version 1.0
- Initial public release.
Version 1.1
- Added sm_35.
- Simplified by removing discussion of CUDA Driver API, which is infrequently used
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