Please review the footnotes associated with the table. The following table lists the supported Linux distributions. The CUDA development environment relies on tight integration with the host development environment, including the host compiler and C runtime libraries, and is therefore only supported on distribution versions that have been qualified for this CUDA Toolkit release. To use NVIDIA CUDA on your system, you will need the following installed:Ī supported version of Linux with a gcc compiler and toolchain This guide will show you how to install and check the correct operation of the CUDA development tools. The on-chip shared memory allows parallel tasks running on these cores to share data without sending it over the system memory bus. These cores have shared resources including a register file and a shared memory. This configuration also allows simultaneous computation on the CPU and GPU without contention for memory resources.ĬUDA-capable GPUs have hundreds of cores that can collectively run thousands of computing threads. The CPU and GPU are treated as separate devices that have their own memory spaces. As such, CUDA can be incrementally applied to existing applications. Serial portions of applications are run on the CPU, and parallel portions are offloaded to the GPU. Support heterogeneous computation where applications use both the CPU and GPU. With CUDA C/C++, programmers can focus on the task of parallelization of the algorithms rather than spending time on their implementation. Provide a small set of extensions to standard programming languages, like C, that enable a straightforward implementation of parallel algorithms. It enables dramatic increases in computing performance by harnessing the power of the graphics processing unit (GPU).ĬUDA was developed with several design goals in mind: Introduction ĬUDA ® is a parallel computing platform and programming model invented by NVIDIA ®. It’s most useful when you are constructing long and complicated commands – especially in scripts when the details depend on data that changes or responses provided when the scripts are run.The installation instructions for the CUDA Toolkit on Linux. It evaluates the commands and arguments in the variable and then executes them. The eval command can be used to run simple or very complex commands that are saved as variables. All I needed to do was answer a few questions about what I knew and didn’t know. The result in the example shown was the single word “cheat” pulled from the nearly half a million lines in the words file. My script uses eval to run the $cmd variable as a command and passes the output to the column command which makes the output more useful when there are dozens or hundreds of matching words. $ cmd=”grep ^ch.t$ /usr/share/dict/words | grep -v ‘q’ | grep -v ‘w’ | grep -v ‘r’ | grep -v ‘y’ | grep -v ‘u’ | grep -v ‘i’ | grep -v ‘o’ | grep -v ‘p’ | grep -v ‘s’ | grep -v ‘d’ | grep -v ‘f’ | grep -v ‘g’ | grep -v ‘j’ | grep -v ‘k’ | grep -v ‘l’ | grep -v ‘z’ | grep -v ‘x’ | grep -v ‘v’ | grep -v ‘b’ | grep -v ‘n’ | grep -v ‘m’ | grep a | grep e | grep -v. If you turn it into a variable as shown below, it will work the same. In fact, if you run that command against your words file, you should get the same response. $ grep ^ch.t$ /usr/share/dict/words | eval grep -v ‘q’ | grep -v ‘w’ | grep -v ‘r’ | grep -v ‘y’ | grep -v ‘u’ | grep -v ‘i’ | grep -v ‘o’ | grep -v ‘p’ | grep -v ‘s’ | grep -v ‘d’ | grep -v ‘f’ | grep -v ‘g’ | grep -v ‘j’ | grep -v ‘k’ | grep -v ‘l’ | grep -v ‘z’ | grep -v ‘x’ | grep -v ‘v’ | grep -v ‘b’ | grep -v ‘n’ | grep -v ‘m’ | grep a | grep e | grep -v. Here’s an example of what such a command might look like with all of its grep (match) and its grep -v (ignore) commands and the match that it found: Step-by-step, the script built a long string of grep commands to look through the words file to find only the words that fit my specifications. I also knew that a number of letters were not included in the words that I needed to find. When you’re building a complex command in pieces, however-especially in scripts that need to collect the necessary data when they are run-using eval to run the command can make running the command a lot easier.Īs an example, while working on a recent script, I needed to find words that were five letters long, had specific letters in known positions and other specific letters in uncertain positions.
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