gf 6800

It was a great card in it's time, but as of today it's almost 9 years old and it never supported any GPGPU stuff in hardware to begin with.

It was a great card in it's time, but as of today it's almost 9 years old and it never supported any GPGPU stuff in hardware to begin with.

Maybe he meant 680? That's current top-of-the-line.

However, it sounds like OP probably isn't using the thing right -- You can't just spawn a billion threads and expect it to work faster.

OP, you first have to have a reasonable understanding that your problem is suitable for OpenCL or other similar libraries. In most cases, you also can't just throw a best-in-class serial algorithm at a GPU and expect it to speed up. To get real gains you very often have to tailor-make a new algorithm that's suitable for parallel execution, and which can take advantage of the other resources that a modern GPU shares across groups of threads, but not equally with all threads. GPU is an entirely different world of performance expectations and trade-offs.

Its possible that CPU algorithms can be faster for some problems, but that the performance disparity gets worse and worse with the size of the algorithm tends to indicate that there's something fundamentally wrong with your approach.

OP, you first have to have a reasonable understanding that your problem is suitable for OpenCL or other similar libraries. In most cases, you also can't just throw a best-in-class serial algorithm at a GPU and expect it to speed up. To get real gains you very often have to tailor-make a new algorithm that's suitable for parallel execution, and which can take advantage of the other resources that a modern GPU shares across groups of threads, but not equally with all threads. GPU is an entirely different world of performance expectations and trade-offs.

Excellent point. OpenCL is geared for repeated, heavy, parallel calculations. I am almost certain that you are bus-bound. Sure, you can add two numbers in parallel on the GPU, but you have to put each one there from the CPU (kinda). So, the trivial addition kernel you have, I would never expect any performance improvement from--no matter how large your data is.

Try a more complicated calculation (e.g. for each x, calculate x! or x!! You'll get thousands or billions of multiply operations, depending on type (integers overflow, so huge factorials won't run indefinitely). This will outweigh your data transfer cost by a lot, and you'll definitely see performance gains).

It's not possible to explain it through just memory bandwidth.

You would be surprised.

It's a real problem. I don't understand what local and global groups are and how i should choose such values for best performance. I've tried to set local group into value different from zero but i've had an error.

You should query your device for CL_DEVICE_MAX_WORK_GROUP_SIZE using clGetDeviceInfo, and use the returned value as the local work group size. Also make sure your global work group size is a multiple of the local group size (which will probably end up being some power of two between 64 and 1024). For best performance you should let the OpenCL compiler work out the optimal work group size by analyzing the kernel, but this should be plenty enough for now.

But still, as said above, just adding two numbers in a kernel isn't enough work for the GPU to show any performance advantage. You'll spend most of your time transferring buffers across the PCI-e bus, and your GPU will even be constrained by its own memory bandwidth (which is saying something, as your GTX 680 has a ludicrous global memory bandwidth of 200GB/s)