Check out the samples in the NVIDIA or AMD SDKs, they should point you in the right direction. Matrix transpose would use local memory for example. Using your squaring kernel, you could stage the data in an intermediate buffer. Remember to pass in the additional parameter. __kernel square( __global float *input, __global float *output, __local … Read more
Well, all libraries you’ve stated are simple wrappers for opencl native libraries. They pose relatively small amount of additional abstractions and are very close to general opencl functions. So if you are familiar with opencl in general you will get familiar with these libraries in no time. I think the ‘OpenCL.NET’ implementation is complete, it … Read more
Apple holds the trademark on OpenCL. Google competes with Apple. Perhaps it’s really that simple. We’ve done work on OpenCL with Android (see here) and are happy to see it moving forward thanks to the work of Intel, Imagination, and other chip makers. Google will turn around soon enough.
In general you can choose global_work_size as big as you want, while local_work_size is constraint by the underlying device/hardware, so all query results will tell you the possible dimensions for local_work_size instead of the global_work_size. the only constraint for the global_work_size is that it must be a multiple of the local_work_size (for each dimension). The … Read more
As you have stated, barriers may only synchronize threads in the same workgroup. There is no way to synchronize different workgroups in a kernel. Now to answer your question, the specification was not clear to me either, but it seems to me that section 6.11.9 contains the answer: CLK_LOCAL_MEM_FENCE – The barrier function will either … Read more
I suspect there is some issue with OpenCL in latest NVidia drivers on some video cards. Here and here are some reports about those. Try to repeat test on GPU from another family.
In contrast to the other OpenGL shader types, compute shaders are not directly related to computer graphics and provide a much more direct abstraction of the underlying hardware, similar to CUDA and OpenCL. It provides customizable work group size, shared memory, intra-group synchronization and all those things known and loved from CUDA and OpenCL. The … Read more
Edit 1 See Mikael Rousson’s answer – Amazon is now the way forwards as you can “rent” computational power from them. Edit 2 I’ve created a series of guides on how to set up Amazon EC2 Instances for Deep Learning with theano. It’s a lot more convenient than running on a personal machine. Edit 3 … Read more
OpenCL is created specifically for computing. When you do scientific computing using OpenGL you always have to think about how to map your computing problem to the graphics context (i.e. talk in terms of textures and geometric primitives like triangles etc.) in order to get your computation going. In OpenCL you just formulate you computation … Read more
AFAIK, JavaCL / OpenCL4Java is the only OpenCL binding that is available on all platforms right now (including MacOS X, FreeBSD, Linux, Windows, Solaris, all in Intel 32, 64 bits and ppc variants, thanks to its use of JNA). It has demos that actually run fine from Java Web Start at least on Mac and … Read more