CUDA_Test | CUDA/SIMD/AssemblyLanguage/OpenMP 's usage | GPU library

by fengbingchun C++ Version: Current License: No License

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kandi X-RAY | CUDA_Test Summary

CUDA_Test is a C++ library typically used in Hardware, GPU applications. CUDA_Test has no vulnerabilities and it has low support. However CUDA_Test has 2160 bugs. You can download it from GitHub.

CUDA/SIMD/AssemblyLanguage/OpenMP's usage

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CUDA_Test has a low active ecosystem.

It has 77 star(s) with 30 fork(s). There are 10 watchers for this library.

It had no major release in the last 6 months.

There are 0 open issues and 2 have been closed. On average issues are closed in 196 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of CUDA_Test is current.

Quality

CUDA_Test has 2160 bugs (0 blocker, 0 critical, 1152 major, 1008 minor) and 2378 code smells.

Security

CUDA_Test has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

CUDA_Test code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

CUDA_Test does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

CUDA_Test releases are not available. You will need to build from source code and install.

It has 29028 lines of code, 0 functions and 83 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

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Community Discussions

Trending Discussions on CUDA_Test

Pytorch custom CUDA extension build fails for torch 1.6.0 or higher

Why is addition without overflow set CC.CF to 1?

CUDA compiler is unable to compile a simple test program

QUESTION

Pytorch custom CUDA extension build fails for torch 1.6.0 or higher

Asked 2021-May-10 at 13:55

I have a custom CUDA extension for pytorch (https://pytorch.org/tutorials/advanced/cpp_extension.html), which used to work fine with pytorch1.4, CUDA10.1, and Titan Xp GPUs. However, recently we changed our system to new A40 GPUs and CUDA11.1. When I try to build my custom pytorch extension using CUDA11.1, pytorch 1.8.1, gcc 9.3.0, and Ubuntu 20.04 I get the following errors:

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ANSWER

Answered 2021-May-10 at 13:55

I found the issue. The Intel MKL module wasn't loaded properly and caused the error. After fixing this the compilation worked just fine also with CUDA 11.1 and pytorch 1.8.1!

Source https://stackoverflow.com/questions/67386709

QUESTION

Why is addition without overflow set CC.CF to 1?

Asked 2021-Feb-27 at 23:59

I have the next code

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ANSWER

Answered 2021-Feb-27 at 23:59

I cannot find information anywhere in the PTX documentation on how what PTX calls the CC.CF flag is actually generated. Looking at the generated machine code (SASS) I see that subtraction is implemented via addition, and the use of an extend flag CC.X.

Based on some quick experiments, this .X flag always seems to be the normal carry-out from the adder. Since a-b = a+~b+1, on a subtraction .X will be set if a >= b. It represents the carry-out from the adder which is the one's complement of an x86-style borrow on subtracts, which is set when a < b.

In other words, the extended arithmetic instructions of the GPU appear to use the same convention that is used by the ARM and PowerPC architectures for their extended arithmetic instructions. The Wikipedia article on the carry flag covers the two design alternatives for handling of the flag during subtraction.

In the code in the question, add.cc.u32 clears CC.CF, which signals to the subsequent subc.u32 that a borrow has occured, causing it to compute a+~b.

You may wish to file an enhancement request with NVIDIA to clarify the PTX documentation regarding details of CC.CF generation and handling.

Source https://stackoverflow.com/questions/66403252

QUESTION

CUDA compiler is unable to compile a simple test program

Asked 2020-Aug-03 at 06:20

I am trying to get NVIDIA's CUDA setup and installed on my PC which has an NVIDIA GEFORCE RTX 2080 SUPER graphics card. After hours of trying different things and lots of research I have gotten CUDA to work using the Command Prompt, though trying to use CUDA in CLion will not work.

Using

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ANSWER

Answered 2020-Aug-03 at 06:20

I was able to get a simple "Hello World" compiling in CLion by making sure your PATH is updated to include

C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v11.0/bin

My CMakeLists.txt looks like this

Source https://stackoverflow.com/questions/63061116

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