Let the competition on large memory GPUs begin! Some algorithms and continuous batch processes will have the joy of the extra memory. For example when inverting a large matrix or doing huge simulations, you need as much memory as possible. or to avoid memory-bank conflicts by duplicating data-objects (possible only when the data is in

I’d like to share two images. The following image is being shared for quite some time, to show the technical capabilities of CUDA. I replaced “CUDA source” by “OpenCL source” and worked from there. Result: I know it is not optimised for a certain architecture, but neither is the CUDA-source for the two extra targets.

OpenCL on ARM is hot, but it just is getting started. Currently it takes some time to find needed information about the processors concerning For OpenCL-discussions the best place is the Khronos OpenCL board. So where can you go when you want to ask questions specifically on ARM-based GPUS like MALI, PowerVR, Adreno and Vivante?

Guest-blogger Jaap van de Loosdrecht wants to share his thesis with you. He leads the Centre of Expertise in Computer Vision department at NHL University of applied sciences and is the owner of his own company, and still managed to study and write a MSc-thesis. The thesis is interesting because it extensively compares OpenCL with

In the below list are the members of the OpenCL workgroup as of November 2013. We can expect small changes each year, but this is close to the actual state. I need the rest of Q4 to finalise all the info – any help is appreciated. This list has also been compiled in 2010, and

Something that creates extra value for Open CL is the flexibility with which it runs on an important variety of hardware. A famous strategy is running the code on CPUs to find data-races and debug the code more easily. Another is to develop on GPUs and port to FPGAs to reduce the development-cycles. But there’s