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LD => '$^X -MExtUtils::nvcc::Backend -eExtUtils::nvcc::Backend::linker -' ДЄnd now you understand why you would use the function that generates these options for you. This is equivalent to the following: use Inline C => DATA =>Д¬C => '$^X -MExtUtils::nvcc::Backend -eExtUtils::nvcc::Backend::compiler -', Those options are generated for you (in an alternating key => value list) by the function ExtUtils::nvcc::Inline: use ExtUtils::nvcc If you want to use CUDA in your Inline::C scripts, you simply need to add the proper configuration options. The functions you would use are, respectively, ExtUtils::nvcc::Inline, ExtUtils::nvcc::EUMM, and ExtUtils::nvcc::MB. It provides a few functions that generate the configuration keys necessary to get Inline::C, ExtUtils::MakeMaker, and Module::Build to use ExtUtils::nvcc::Backend to compile your CUDA code. This module functions for your day-to-day use of ExtUtils::nvcc (if there is such a thing as day-to-day use of a toolchain). I discuss that functionality in ExtUtils::nvcc::Backend.

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This means you can use nvcc to compile CUDA code for Perl.

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This module serves as the configuration front-end to a Perl module that knows how to translate arbitrary command-line arguments into nvcc-digestable command-line arguments. } ExtUtils::MakeMaker # In your Makefile.PL: Copy the contents back to the Perl scalar:Д¬udaMemcpy(data, data_d, data_bytes, cudaMemcpyDeviceToHost) Print a status indicator and execuate the kernel Unsigned int data_bytes = sizeof(float) * 100 Д¬udaMemcpy(data_d, data, data_bytes, cudaMemcpyHostToDevice) array (pointer), which I can easily cast as a float pointer: Inline::C knows how to massage a Perl scalar into a char data to and from the device is very, very slow (compared with all

NOTE: Do not make such a kernel a regular habit. indeed write CUDA kernels using Inline::C. It is just a proof of concept, to show that you can is a particularly good example of BAD programming - it should be global data associated with the location at threadIdx.x. This is a very simple CUDA kernel that triples the value of the # indicates that the segmentation fault occurrs after # Call the Perl-callable wrapper to the CUDA kernel:

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# Generate a series of 100 sequential values and pack them # The rest of this is just a working example Use Inline C => DATA => ExtUtils::nvcc::Inline I have included a fully working example for Inline::C, as well as other partial examples for ExtUtils::MakeMaker and Module::Build. This documentation explains the use of ExtUtils::nvcc version 0.03. ExtUtils::nvcc - CUDA compiler and linker wrapper for Perl's toolchain VERSION