![]() The next step is to install the latest Nvidia graphics driver for the graphics cards being installed on the development machine. ![]() In this case we have a need to install the required build toolchain that includes GNU GCC-C++ compiler, MAKE/CMAKE utilities and development libraries, the using of which is necessary for compiling the LLVM/Clang project.ĭownloading And Installing Nvidia Accelerated Graphics Driver Sudo apt install -y build-essential libqt5 python libelf-dev libffi-dev openssl pkg-config ninja-build git* To do that we must use the following command from the Linux bash-console with root administrative privileges elevated: Also, it is not necessary to update/downgrade the Ubuntu 18.04.4 kernel, compiling it from sources, since the latest version 5.4.0 of the Linux x86_64 kernel is already fully supported.Īfter we’ve installed the Ubuntu Desktop 18.04.4 on the physical local development machine, our goal is to install the prerequisite packages first. Unfortunately, we cannot use the current LLVM/Clang-10.0.0 distribution release in the Microsoft Windows environment. The approach discussed in this blog works only for the local development machine with Ubuntu Desktop 18.04.4 Bionic Beaver x86_64 installed. Please, notice that the following approach being discussed will not work in case when using virtual machines with the Hyper-V, VMware, VirtualBox or Qemu generic virtual graphics card installed. To setup the development environment, all that we need is a Intel® Core™, Intel® Xeon® CPU 3.6 Ghz CPUs – based local development machine with the one more multiple Nvidia GeForce GPUs installed and SLI x2/ SLI x3 enabled. This feature was deprecated in the official pre-releases of Intel oneAPI Toolkit, but it might become useful while evaluating the performance of the C++17 OpenCL/SYCL-code delivered by running it on the various of acceleration targets with different hardware architecture. In this blog, I will discuss how to use the Intel-LLVM/Clang compiler staging distribution ( ) to build and run the OpenCL/SYCL code on the Nvidia GPUs acceleration targets. In my previous blog ( ) I have thoroughly discussed how to build OpenMP 4.5 parallel code the Intel-LLVM/Clang compiler, supporting the Nvidia GPGPUs CUDA-NVPTX64 capabilities.
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