OpenCL: Compute, Cryptography, And Bandwidth
Shader Performance: FP32 vs. FP64
Let’s start with an OpenCL benchmark, which should push the theoretical ceiling of 32- and 64-bit precision compute performance.
Although this benchmark (along with the cryptography test) is synthetic, its still illustrates Nvidia’s half-hearted support of OpenCL.
Yes, Nvidia offers its proprietary CUDA API, and there are plenty of applications that support it. Increasingly, though, ISVs don't want to support two compute languages, and OpenCL is gaining traction as a result. Even long-time bastions of CUDA support, like Adobe, are adopting OpenCL.
Folding It Up: Folding@Home
Let’s run the Folding@Home benchmark on this card, even though few people would use a $4000 workstation board for folding or Bitcoin mining.
In the memory bandwidth test, Nvidia’s sub-par OpenCL implementation almost catches AMD's latest. But switching over to DirectX allows GK110's Kepler architecture to beat the FirePro W9100 by 50%.
As we move on to application benchmarks, keep these synthetic benchmarks in mind. They help decipher the performance results of real-world benchmarks, which are subject to influence from other platform subsystems.
At least for now, we have to question whether Nvidia's lackluster support for OpenCL and emphasis on CUDA is the best strategy. Only time will tell.