Performance wise, it depends on the task, sometimes one or the other is faster. But it should still demonstrate the point.
#Ati tool nvidia software#
Granted, it is a bit exaggerated, and there are software packages that ATI(AMD) writes that make the code less miserable to work with. In both cases there’s a short kernel (on top) which is executed on a GPU, and the rest of the code initializes the system, copies data to & from the GPU, and launches the kernel: Two programs doing the same exact task, one using CUDA run-time API, the other using vanilla OpenCL. Here’s a picture that I made back when I was learning OpenCL. The code that you write for the GPU is very similar, but OpenCL requires a lot of extra work on the CPU side that is avoided with CUDA. What are the prices? Which is easier to use, gDEBugger or Parallel NSight? (If I remember correctly, NVIDIA needs 2 video cards for debugging, while ATI needs only 1) Which one has better performance? is there no solution to write the same code for both NVIDIA and ATI Cards?ĬUDA is easier to use than OpenCL, especially for a beginner. But here we don’t have tools to run the same code on ATI and NVIDIA Video Cards right? If so, please tell me which one is better solution and why. Is it easy to write big projects in OpenCL? I guessed if I wanted to write a big code without bugs, I should write in VS. If I remember correctly, OpenCL is for both - ATI and NVIDIA, but CUDA is Nvidia’s product. Nvidia has CUDA and Parallel NSight plugin in VS, ATI has OpenCL and gDEBugger plugin in VS. As I want to start parallel programming on GPUs (to be more precise, I want to use GPUs in Computational Finance), I’d like to know what advantages and disadvantages both sides have. I found out that GPGPU can be done on NVIDIA and ATI Video Cards. I became interested and took a look in Google. I have recently heard about parallel programming using GPUs. I’m new in the forum so if I opened topic in wrong place, sorry.