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Update documentation for CUDA

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Gael Guennebaud 2017-07-17 11:05:26 +02:00
parent 3182bdbae6
commit a74b9ba7cd
2 changed files with 7 additions and 7 deletions
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2
doc/PreprocessorDirectives.dox
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@ -120,6 +120,8 @@ run time. However, these assertions do cost time and can thus be turned off.
- \b \c EIGEN_STACK_ALLOCATION_LIMIT - defines the maximum bytes for a buffer to be allocated on the stack. For internal
temporary buffers, dynamic memory allocation is employed as a fall back. For fixed-size matrices or arrays, exceeding
this threshold raises a compile time assertion. Use 0 to set no limit. Default is 128 KB.
- \b \c EIGEN_NO_CUDA - disables CUDA support when defined. Might be useful in .cu files for which Eigen is used on the host only,
and never called from device code.
- \c EIGEN_DONT_ALIGN - Deprecated, it is a synonym for \c EIGEN_MAX_ALIGN_BYTES=0. It disables alignment completely. %Eigen will not try to align its objects and does not expect that any objects passed to it are aligned. This will turn off vectorization if \b EIGEN_UNALIGNED_VECTORIZE=1. Not defined by default.

12
doc/UsingNVCC.dox
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@ -3,18 +3,16 @@ namespace Eigen {
/** \page TopicCUDA Using Eigen in CUDA kernels
\b Disclaimer: this page is about an \b experimental feature in %Eigen.
Staring from CUDA 5.0, the CUDA compiler, \c nvcc, is able to properly parse %Eigen's code (almost).
A few adaptations of the %Eigen's code already allows to use some parts of %Eigen in your own CUDA kernels.
To this end you need the devel branch of %Eigen, CUDA 5.0 or greater with GCC.
Staring from CUDA 5.5 and Eigen 3.3, it is possible to use Eigen's matrices, vectors, and arrays for fixed size within CUDA kernels. This is especially useful when working on numerous but small problems. By default, when Eigen's headers are included within a .cu file compiled by nvcc most Eigen's functions and methods are prefixed by the \c __device__ \c __host__ keywords making them callable from both host and device code.
This support can be disabled by defining \c EIGEN_NO_CUDA before including any Eigen's header.
This might be usefull to disable some warnings when a .cu file makes use of Eigen on the host side only.
However, in both cases, host's SIMD vectorization has to be disabled in .cu files.
It is thus \b strongly \b recommended to properly move all costly host computation from your .cu files to regular .cpp files.
Known issues:
- \c nvcc with MS Visual Studio does not work (patch welcome)
- \c nvcc with \c clang does not work (patch welcome)
- \c nvcc 5.5 with gcc-4.7 (or greater) has issues with the standard \c \<limits\> header file. To workaround this, you can add the following before including any other files:
\code
// workaround issue between gcc >= 4.7 and cuda 5.5