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If the CUDA toolkit is installed in a non-default location on your system, two considerations must be made.įirstly, do not add the toolkit to your standard environment variables ( PATH, LD_LIBRARY_PATH), as to do so will create conflicts with OpenCL headers. Users can specify lower values,īut some features may not be supported. Operating systems with any GPU compatible with SM 50 or above.
INTEL C COMPILER STANDARD WINDOWS
The CUDA backend should work on Windows or Linux
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NVIDIA CUDA Installation Guide for WindowsĬurrently, the only combination tested is Ubuntu 18.04 with CUDA 10.2 usingĪ Titan RTX GPU (SM 71). Linux (WSL) is not needed to build and run the CUDA backend.Įnabling this flag requires an installation of The CUDA backend has experimental Windows support, windows subsystem for Windows DPC++ toolchain, but add the -cuda flag to configure.py. To enable support for CUDA devices, follow the instructions for the Linux or There is experimental support for DPC++ for CUDA devices. py Build DPC++ toolchain with support for NVIDIA CUDA ¶ libcxx - library python % DPCPP_HOME %\ llvm\ buildbot\ compile. Python % DPCPP_HOME %\ llvm\ buildbot\ configure. There is experimental support for building and linking DPC++ runtime with Build DPC++ toolchain with libc++ library ¶ This allows you, for example, toĬonfigure several different builds and then build just one of them which is Placed in non-default directory using -o flag, you must also specify this flagĪnd the same path in compile.py options. Please note that no data about flags is being shared between configure.py andĬompile.py scripts, which means that if you configured your build to be cmake-gen -> Set build system type (e.g. ‘–enable-esimd-cpu-emulation’ -> enable ESIMD CPU emulation (see ESIMD CPU emulation) hip-platform -> select the platform used by the hip backend, AMD or NVIDIA (see HIP AMD or see HIP NVIDIA) cuda -> use the cuda backend (see Nvidia CUDA) no-werror -> Don’t treat warnings as errors when compiling llvm
INTEL C COMPILER STANDARD DOWNLOAD
system-ocl -> Don’t download OpenCL headers and library via CMake but use the system ones You can use the following flags with configure.py (full list of availableįlags can be found by launching the script with -help): Python %DPCPP_HOME%\llvm\buildbot\compile.py Python %DPCPP_HOME%\llvm\buildbot\configure.py It might be useful to create an environment variable Throughout this document DPCPP_HOME denotes the path to the local directoryĬreated as DPC++ workspace. Windows: Visual Studio version 15.7 preview 4 or later. Linux: GCC version 7.1.0 or later (including libstdc++). Using the DPC++ toolchain on CUDA platforms Run Khronos* SYCL* conformance test suite (optional) Obtain prerequisites for ahead of time (AOT) compilation Wa_cq_url: "/content/Table of contents ¶īuild DPC++ toolchain with libc++ libraryīuild DPC++ toolchain with support for NVIDIA CUDAīuild DPC++ toolchain with support for HIP AMDīuild DPC++ toolchain with support for HIP NVIDIAīuild DPC++ toolchain with support for ESIMD CPU Emulation
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