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Summary: In current implementation, D2D memcpy is first to copy data back to host and then copy from host to device. This is very efficient if the device supports D2D memcpy, like CUDA. In this patch, D2D memcpy will first try to use native supported driver API. If it fails, fall back to original way. It is worth noting that D2D memcpy in this scenerio contains two ideas: - Same devices: this is the D2D memcpy in the CUDA context. - Different devices: this is the PeerToPeer memcpy in the CUDA context. My implementation merges this two parts. It chooses the best API according to the source device and destination device. Reviewers: jdoerfert, AndreyChurbanov, grokos Reviewed By: jdoerfert Subscribers: yaxunl, guansong, sstefan1, openmp-commits Tags: #openmp Differential Revision: https://reviews.llvm.org/D80649 |
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| deviceRTLs | ||
| include | ||
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| src | ||
| test | ||
| CMakeLists.txt | ||
| README.txt | ||
README for the LLVM* OpenMP* Offloading Runtime Library (libomptarget)
======================================================================
How to Build the LLVM* OpenMP* Offloading Runtime Library (libomptarget)
========================================================================
In-tree build:
$ cd where-you-want-to-live
Check out openmp (libomptarget lives under ./libomptarget) into llvm/projects
$ cd where-you-want-to-build
$ mkdir build && cd build
$ cmake path/to/llvm -DCMAKE_C_COMPILER=<C compiler> -DCMAKE_CXX_COMPILER=<C++ compiler>
$ make omptarget
Out-of-tree build:
$ cd where-you-want-to-live
Check out openmp (libomptarget lives under ./libomptarget)
$ cd where-you-want-to-live/openmp/libomptarget
$ mkdir build && cd build
$ cmake path/to/openmp -DCMAKE_C_COMPILER=<C compiler> -DCMAKE_CXX_COMPILER=<C++ compiler>
$ make
For details about building, please look at README.rst in the parent directory.
Architectures Supported
=======================
The current library has been only tested in Linux operating system and the
following host architectures:
* Intel(R) 64 architecture
* IBM(R) Power architecture (big endian)
* IBM(R) Power architecture (little endian)
* ARM(R) AArch64 architecture (little endian)
The currently supported offloading device architectures are:
* Intel(R) 64 architecture (generic 64-bit plugin - mostly for testing purposes)
* IBM(R) Power architecture (big endian) (generic 64-bit plugin - mostly for testing purposes)
* IBM(R) Power architecture (little endian) (generic 64-bit plugin - mostly for testing purposes)
* ARM(R) AArch64 architecture (little endian) (generic 64-bit plugin - mostly for testing purposes)
* CUDA(R) enabled 64-bit NVIDIA(R) GPU architectures
Supported RTL Build Configurations
==================================
Supported Architectures: Intel(R) 64, IBM(R) Power 7 and Power 8
---------------------------
| gcc | clang |
--------------|------------|------------|
| Linux* OS | Yes(1) | Yes(2) |
-----------------------------------------
(1) gcc version 4.8.2 or later is supported.
(2) clang version 3.7 or later is supported.
Front-end Compilers that work with this RTL
===========================================
The following compilers are known to do compatible code generation for
this RTL:
- clang (from https://github.com/clang-ykt )
- clang (development branch at http://clang.llvm.org - several features still
under development)
-----------------------------------------------------------------------
Notices
=======
This library and related compiler support is still under development, so the
employed interface is likely to change in the future.
*Other names and brands may be claimed as the property of others.