Recently, I was able to successfully use RealSense R200 on my NVIDIA Jetson TX1 with librealsense. I had to replace some SSSE3 instructions in the code to get it to compile under ARM. I created a fork of librealsense on Github with all the changes I made. Check it out here: ttps://github.com/Maghoumi/librealsense
R200 works at 60 FPS with my Jetson TX1 flawlessly! 🙂
One popular BLAS implementation under Windows is AMD’s ACML.
CMake has some modules that can find certain required dependency libraries. As far as I know, under Windows the “FindBLAS” and “FindLapack” modules are unable to locate AMD’s ACML libraries. This is because ACML > v4.0 does not include the “mv” related packages anymore.
I looked around a little bit and I found a patched version of these files here and here. However, I couldn’t get them to work. If anyone was able to get them to work, comment below.
To my surprise, the CUDA library ArrayFire is now open source and licensed under BSD 3-Clause License which means that commercial use is permitted!
ArrayFire is a production oriented library which greatly reduces CUDA application development time. The repository is hosted on GitHub and is located here.
If you receive this error while compiling a CUDA program, it means that you have included a CUDA header file containing CUDA specific qualifiers (such as __device__) in a *.cpp file.
CUDA header files with such qualifiers should ONLY be included in *.cu files.
This happened to me when I had #inlcude <common_functions.h> in my *.cpp file. Note that having this in a header file that will be linked to a *.cpp file will also result in the same error.
Seeing as how often many programmers struggle with the same issue twice, I decided to start this blog. I will try to note the problems that I encountered during my coding here so that when I, or other programmers, encounter them again the solution is already available somewhere.
I will note the issues that required more than a simple Google search to solve.
Never get stuck on the same issue twice! 🙂