NVIDIA’s GeForce GF100 Under the Microscope

Compute Performance on the GF100

We have already talked over and over again about the inherent efficiency that comes with the incorporation of several new technologies into the GF100 but its strength in GPU Compute applications stems from its HPC roots. When it comes to processing large parallel data sets nothing distinguishes the GF100 more than its ability to run concurrent kernels.


The GigaThread hardware thread scheduler is the piece of the puzzle that allows for concurrent kernel execution. In a serial kernel execution scenario, each kernel has to wait for the one before it to finish before it can begin. However, with concurrent kernels the GPU can be utilized in a more efficient way since different kernels of the same application context can operate at the same time. Basically, the use of concurrent kernels means that multiple work loads can be implemented at the same time which not only frees up resources but also allows for quicker processing of things like in-game physics and AI.

NVIDIA has stated that their PhysX 3.0 update will take advantage of concurrent kernels.


This all boils down to a significant increase in compute performance over the previous generation of cards. We also have to remember that this will not only benefit game features but will have a significant impact upon Folding@home GPU performance as well.