# TGV Performance Numbers

**URL:** <https://pyfr.discourse.group/t/tgv-performance-numbers/407>\
**Category:** General\
**Created:** [3 June 2021 18:42 UTC](https://pyfr.discourse.group/t/tgv-performance-numbers/407 "2021-06-03T18:42:42Z")\
**Posts on this page:** 1\
**Showing post:** 12

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**Author:** ![WillT](https://avatars.discourse-cdn.com/v4/letter/w/bc79bd/32.png) [@WillT](https://pyfr.discourse.group/u/WillT)\
**Post date:** [9 June 2021 14:03 UTC](https://pyfr.discourse.group/t/tgv-performance-numbers/407/12 "2021-06-09T14:03:05Z")

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I reran the case, for slightly less time (`tend=1`), and also turned off the integrate and nan checker plugins.

Here are the results for an A100 at p=3, for a 40^3 element mesh.

| | Single | Double |
| --- | --- | --- |
| Runtime [s] | 216.256 | 355.794 |
| DoF | (40\times4)^3 | (40\times4)^3 |
| RHS | 4\times10^4 | 4\times10^4 |
| No. GPUs | 1 | 1 |
| s/DoF/RHS/GPU | 1.320\times10^{-9} | 2.172\times10^{-9} |

Converting these to Freddie’s units this gives:

- FP32 3.788GDoF/s
- FP64 2.302GDoF/s

So the A100, as expected compares favourably with the V100, but given that the bandwidth is ~50% higher on the A100 I would’ve expected more.

Now either the case is too small to keep the GPU busy or some kernel isn’t performing well on the new hardware. I have some more data on this but this is getting a little out of scope for this topic, so will start a new thread in due course to discuss this further, if people are interested.

The moral of the story here is that the _plugins can have significant overhead_.  
(We might want to think about moving the nan checker to device for GPU to avoid the PCIe bottleneck).

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_[View the full topic](https://pyfr.discourse.group/t/tgv-performance-numbers/407)._