Standards for Manufactured Home Installation The serves as the basis for developing the manufacturers’ installation instructions as required by 24 CFR Part 3285. The Model Manufactured Home Installation Standards include methods for performing specific operations or assembly of a manufactured home that will not take the home out of compliance with the Manufactured Home Construction and Safety Standards (24 CFR Part 3280). States that choose to operate an installation program for manufactured homes in lieu of the federal program must implement installation standards that provide protection to its residents that equals or exceeds the protection provided by these Model Installation Standards. In states that do not choose to operate their own installation program for manufactured homes, these Model Installation Standards serve as the minimum standards for manufactured home installations.
CUDA was developed with several design goals in mind:. Provide a small set of extensions to standard programming languages, like C, that enable a straightforward implementation of parallel algorithms. With CUDA C/C++, programmers can focus on the task of parallelization of the algorithms rather than spending time on their implementation. Support heterogeneous computation where applications use both the CPU and GPU.
Serial portions of applications are run on the CPU, and parallel portions are offloaded to the GPU. As such, CUDA can be incrementally applied to existing applications. The CPU and GPU are treated as separate devices that have their own memory spaces. This configuration also allows simultaneous computation on the CPU and GPU without contention for memory resources. CUDA-capable GPUs have hundreds of cores that can collectively run thousands of computing threads. These cores have shared resources including a register file and a shared memory.
The on-chip shared memory allows parallel tasks running on these cores to share data without sending it over the system memory bus. This guide will show you how to install and check the correct operation of the CUDA development tools. The NVIDIA CUDA Toolkit is available at no cost from the main page. The installer is available in two formats:. Network Installer: A minimal installer which later downloads packages required for installation. Only the packages selected during the selection phase of the installer are downloaded. This installer is useful for users who want to minimize download time.
Full Installer: An installer which contains all the components of the CUDA Toolkit and does not require any further download. This installer is useful for systems which lack network access. Both installers install the driver and tools needed to create, build and run a CUDA application as well as libraries, header files, CUDA samples source code, and other resources. The download can be verified by comparing the with that of the downloaded file. If either of the checksums differ, the downloaded file is corrupt and needs to be downloaded again.
Choose which packages you wish to install. The packages are:. CUDA Driver: This will install /Library/Frameworks/CUDA.framework and the UNIX-compatibility stub /usr/local/cuda/lib/libcuda.dylib that refers to it.
CUDA Toolkit: The CUDA Toolkit supplements the CUDA Driver with compilers and additional libraries and header files that are installed into /Developer/NVIDIA/CUDA- 10.0 by default. Symlinks are created in /usr/local/cuda/ pointing to their respective files in /Developer/NVIDIA/CUDA- 10.0/. Previous installations of the toolkit will be moved to /Developer/NVIDIA/CUDA-#.# to better support side-by-side installations. CUDA Samples (read-only): A read-only copy of the CUDA Samples is installed in /Developer/NVIDIA/CUDA- 10.0/samples.
Previous installations of the samples will be moved to /Developer/NVIDIA/CUDA-#.#/samples to better support side-by-side installations. A command-line interface is also available:.accept-eula: Signals that the user accepts the terms and conditions of the CUDA- 10.0 EULA.silent: No user-input will be required during the installation. Requires -accept-eula to be used.no-window: No windows will be created during the installation. Useful for installing in environments without a display, such as via ssh.
Implies -silent. Requires -accept-eula to be used.install-package=: Specifies a package to install. Can be used multiple times. Options are 'cuda-toolkit', 'cuda-samples', and 'cuda-driver'.log-file=: Specify a file to log the installation to.
Default is /var/log/cudainstaller.log. Mac Uninstall Script Locations Package Location CUDA Driver /usr/local/bin/uninstallcudadrv.pl CUDA Toolkit /Developer/NVIDIA/CUDA- 10.0/bin/uninstallcuda 10.0.pl CUDA Samples /Developer/NVIDIA/CUDA- 10.0/bin/uninstallcuda 10.0.pl All packages which share an uninstall script will be uninstalled unless the -manifest= flag is used. Uninstall manifest files are located in the same directory as the uninstall script, and have filenames matching.uninstallmanifestdonotdelete.txt. The NVIDIA CUDA Toolkit includes CUDA sample programs in source form. To fully verify that the compiler works properly, a couple of samples should be built. After switching to the directory where the samples were installed, type: make -C 0Simple/vectorAdd make -C 0Simple/vectorAddDrv make -C 1Utilities/deviceQuery make -C 1Utilities/bandwidthTest The builds should produce no error message.
The resulting binaries will appear under /bin/x8664/darwin/release. To go further and build all the CUDA samples, simply type make from the samples root directory. Note that the measurements for your CUDA-capable device description will vary from system to system.
The important point is that you obtain measurements, and that the second-to-last line (in ) confirms that all necessary tests passed. Should the tests not pass, make sure you have a CUDA-capable NVIDIA GPU on your system and make sure it is properly installed. If you run into difficulties with the link step (such as libraries not being found), consult the Release Notes found in the doc folder in the CUDA Samples directory. To see a graphical representation of what CUDA can do, run the particles executable. Now that you have CUDA-capable hardware and the NVIDIA CUDA Toolkit installed, you can examine and enjoy the numerous included programs. To begin using CUDA to accelerate the performance of your own applications, consult the. A number of helpful development tools are included in the CUDA Toolkit to assist you as you develop your CUDA programs, such as NVIDIA ® Nsight™ Eclipse Edition, NVIDIA Visual Profiler, cuda-gdb, and cuda-memcheck.
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