Adding prediction timings to README.

PiperOrigin-RevId: 496937349
Change-Id: I9e9f447b3ce11d1b5a5c7433aeae7b03a3ed19e9

README.md

@@ -3,16 +3,15 @@
 # AlphaFold
 
 This package provides an implementation of the inference pipeline of AlphaFold
-v2.0. For simplicity, we refer to this model as AlphaFold throughout the rest
-of this document.
+v2.0. For simplicity, we refer to this model as AlphaFold throughout the rest of
+this document.
 
 We also provide:
 
 1.  An implementation of AlphaFold-Multimer. This represents a work in progress
     and AlphaFold-Multimer isn't expected to be as stable as our monomer
-    AlphaFold system.
-    [Read the guide](#updating-existing-installation)
-    for how to upgrade and update code.
+    AlphaFold system. [Read the guide](#updating-existing-installation) for how
+    to upgrade and update code.
 2.  The [technical note](docs/technical_note_v2.3.0.md) containing the models
     and inference procedure for an updated AlphaFold v2.3.0.
 3.  A [CASP15 baseline](docs/casp15_predictions.zip) set of predictions along
@@ -109,8 +108,8 @@ subdirectory in the AlphaFold repository directory.** If it is, the Docker build
 will be slow as the large databases will be copied during the image creation.
 
 We don't provide exactly the database versions used in CASP14 – see the
-[note on reproducibility](#note-on-casp14-reproducibility). Some of the databases are
-mirrored for speed, see [mirrored databases](#mirrored-databases).
+[note on reproducibility](#note-on-casp14-reproducibility). Some of the
+databases are mirrored for speed, see [mirrored databases](#mirrored-databases).
 
 :ledger: **Note: The total download size for the full databases is around 415 GB
 and the total size when unzipped is 2.62 TB. Please make sure you have a large
@@ -351,6 +350,38 @@ for a small drop in accuracy you may wish to run a single seed per model. This
 can be done via the `--num_multimer_predictions_per_model` flag, e.g. set it to
 `--num_multimer_predictions_per_model=1` to run a single seed per model.
 
+### AlphaFold prediction speed
+
+The table below reports prediction runtimes for proteins of various lengths. We
+only measure unrelaxed structure prediction while excluding runtimes from MSA
+and template search. When running `docker/run_docker.py` with
+`--benchmark=true`, this runtime is stored in `timings.json`. All runtimes are
+from a single A100 NVIDIA GPU. Prediction speed on A100 for smaller structures
+can be improved by increasing `global_config.subbatch_size` in
+`alphafold/model/config.py`.
+
+No. residues | Prediction time (s)
+-----------: | ------------------:
+100          | 4.9
+200          | 7.7
+300          | 13
+400          | 18
+500          | 29
+600          | 36
+700          | 53
+800          | 60
+900          | 91
+1,000        | 96
+1,100        | 140
+1,500        | 280
+2,000        | 450
+2,500        | 969
+3,000        | 1,240
+3,500        | 2,465
+4,000        | 5,660
+4,500        | 12,475
+5,000        | 18,824
+
 ### Examples
 
 Below are examples on how to use AlphaFold in different scenarios.
@@ -602,7 +633,6 @@ If you use the code or data in this package, please cite:
 
 In addition, if you use the AlphaFold-Multimer mode, please cite:
 
-
 ```bibtex
 @article {AlphaFold-Multimer2021,
   author       = {Evans, Richard and O{\textquoteright}Neill, Michael and Pritzel, Alexander and Antropova, Natasha and Senior, Andrew and Green, Tim and {\v{Z}}{\'\i}dek, Augustin and Bates, Russ and Blackwell, Sam and Yim, Jason and Ronneberger, Olaf and Bodenstein, Sebastian and Zielinski, Michal and Bridgland, Alex and Potapenko, Anna and Cowie, Andrew and Tunyasuvunakool, Kathryn and Jain, Rishub and Clancy, Ellen and Kohli, Pushmeet and Jumper, John and Hassabis, Demis},