ArrayFire 3.5.1 Compiled, using GCC 4.9.2 and CUDA – Success!

A project which has fixated me for several days has been, to get the API which is named “ArrayFire” custom-compiled, specifically version 3.5.1 of that API, to enable CUDA support. This poses a special problem because when we use the Debian / Stretch repositories to install the CUDA Run-Time, we are limited to installing version 8.0.44. This run-time is too old, to be compatible with the standard GCC / CPP / C++ compiler-set available under the same distribution of Linux. Therefore, it can be hard for users and Debian Maintainers alike to build projects that use CUDA, and which are compatible with -Stretch.

Why use ArrayFire? Because writing kernels for parallel computing on the GPU is hard. ArrayFire is supposed to make the idea more-accessible to people like me, who have no specific training in writing highly parallel code.

When we install ArrayFire from the repositories, OpenCL support is provided, but not CUDA support.

I’ve hatched a plan by which I can install an alternative compiler set on the computer I name ‘Phosphene’, that being GCC / CPP / C++ version 4.9.2, and with which I can switch my compilers to that version, so that maybe, I can compile projects in the future, that do use CUDA, and that are sophisticated enough to require a full compiler-suite to build.

What I’ve found was that contrarily to how it went the previous time, this time I’ve scored a success. :-)  Not only did the project compile without errors, but then, a specific Demo that ships with the source-code version, that uses the ability of CUDA to pass graphics to the OpenGL rendering of the GPU, also ran without errors…


So now I can be sure that the tool-chain which I’ve installed, is up to the task of compiling highly-complex CUDA projects.

(Update 5/03/2019, 7h30 : )

Continue reading ArrayFire 3.5.1 Compiled, using GCC 4.9.2 and CUDA – Success!

Another way to assess quickly, how many computing cores our GPU has.

This posting is on a familiar topic.

On certain Windows computers, there was a popular GUI-based tool named “CPU-Z”, which would give the user fast info about the capabilities of his CPU. Well that application inspired many others, on different platforms, among them, “CUDA-Z”, available for Linux.

If the user has CUDA installed, then he can Download this tool from SourceForge, which is available under Linux as an executable, which can just be put into any directory and run from there. This type of statically-linked executable has come to be known as ‘an app-image’ in the Linux world, but in this case the filename ends with ‘.run’. Below is what mine shows me… Its permission-bits need to be changed to ‘a+x’ after downloading:



I find almost all the information accurate, the only exception being the “Runtime Dll Version”. BTW, Linux computers don’t generally have DLL Files. But I expect that this version-number stems from some internal limitation of the app, as I already know that my Run-Time Version is 8.0.44 .



Finding Out How Many GPU Cores We Have Under Linux, Revisited!

In this earlier posting, I tried to describe in a roundabout way, what the shader cores of a GPU – the Graphics Processing Unit – actually do.

And in this earlier posting, I tried to encourage even Linux-users to find out approximately how many GPU cores they have, given a correct install of the open standard OpenCL – for actual GPU computing – using the command-line tool ‘clinfo’. But that use of ‘clinfo’ left much to be desired, including the fact that sometimes, OpenCL will only assign a maximum number of cores belonging to each core group, that’s a power of 2, even if there may be a non-power-of-two number of cores.

Well, if we have the full set of nVidia drivers installed, nVidia CUDA – which is a competitor to OpenCL, as well as having the nVidia Settings GUI installed, it turns out that there is a much-more accurate answer:


But, this method has as drawback, that it’s only available to us, when we have both nVidia hardware, and the proprietary drivers installed. This could lead some people to the false impression, that maybe, only nVidia graphics cards have real GPUs?



Finding Out, How Many GPU Cores we have, Under Linux

One question which I see written about often on the Web, is how to find out certain stats about our GPU, under Linux. Under Windows, we had GUI-based programs such as ‘GPU-Z’, etc., but under Linux, the information can be just a bit harder to find.

I think that one tool which helps, is to have ‘OpenCL’ installed, as well as the command-line utility ‘clinfo’, which exists as one out of several packages, and as an actual, resulting command-name.

If we’re serious about programming our GPU, then having a GUI won’t help us much. We’d need to get dirty with code in that case, and then to have text-based solutions is suitable. But, if we’re just spectators in this sport, then two stats we may nevertheless want to know are:

  1. How many GPU-Core-Groups do we have – since GPU-Cores are organized as Groups, and
  2. How many actual Shader-Cores do we have in each Group?

Interestingly, the grouping of shader-cores, also represents how many vector-processors such GPU-computing tools as OpenCL see. And so, on the computer which I name ‘Klystron’, which is running Debian / Jessie, when typing in these commands as user, I get the following results:


dirk@Klystron:~$ clinfo | grep units
  Max compute units:                             4
  Max compute units:                             6
dirk@Klystron:~$ clinfo | grep multiple
  Kernel Preferred work group size multiple:     1
  Kernel Preferred work group size multiple:     64


This needs some explaining. On ‘Klystron’, I have the proprietary, AMD packages for OpenCL installed, since that computer has both an AMD CPU and a Radeon GPU. And this means that the OpenCL version will be able to carry out computing on both. And so I have the stats for both.

In this case, the second entries reveal that I have 6×64 cores on the GPU.

Continue reading Finding Out, How Many GPU Cores we have, Under Linux