u/Inevitable-Sky-7238

I am currently doing a research project which involves benchmarking Nvidia HPC-level GPUs. I am running into an issue while trying to profile a V100 using HPCG, since it uses an older binary (not the newest release). When trying to profile the Symmetric Gauss Seidel (SYMGS) kernels, I get this strange result:

Gauss-Seidel forwards + backwards sweep kernels

These 15 kernels pretty clearly represent the SYMGS forwards and backwards sweeps. This loop repeats 4 times, and then I get this:

SYMGS4 kernels

This SYMGS4 kernel repeats 48 times. It does this between loop instances of the 4 loops from the previous screenshot. I would have thought that the large majority for SYMGS would have been done in the forward and backward sweep kernels, but clearly there is work being done here, since SYMGS4 takes up around 41% of the duration of the entire HPCG application.

If anyone here is familiar with how SYMGS might have been configured in CUDA to yield this type of result, please let me know what I'm missing here. For those interested, I got this version of Nvidia-HPCG from hpcg-benchmark.org/software/view.html%3Fid=280.html

I am currently doing a research project which involves comparing performance of Nvidia HPC class GPUs, and I have found that referencing the die-area investment of these GPUs would be useful for this analysis. The floorplan analyses I have found for GV100, GA100 and GH100 so far only include speculative summaries of die-area investment, so if anyone knows of any credible resources for this information I would be very appreciative.