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Update README.md
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@Intron7
Intron7 authored Jul 15, 2022
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Expand Up @@ -60,23 +60,23 @@ To show the capability of these functions, I created two example notebooks evalu

Here are some benchmarks. I ran the notebook on the CPU with as many cores as were available where possible.

|Step |CPU (Ryzen 5950x, 32 Cores, 64GB RAM)|GPU (RTX 3090)|CPU (AMD Eypc Rome, 60 Cores, 1TB RAM)| GPU (Quadro RTX 6000)
|------------------------------|---------------------------|--------------|----------|--------------|
|whole Notebook | 494 s | 51 s | 820 s | 82 s |
|Preprocessing | 90 s | 23 s | 120 s | 35 s |
|Clustering and Visulatization | 379 s | 24 s | 640 s | 38 s |
|Normalize_total | 273 ms | > 1ms | 423 ms | 1 ms |
|Regress_out | 81 s | 18 s | 105 s | 26.1 s |
|Scale | 701 ms | 154 ms | 1.1 s | 177 ms |
|PCA | 19.9 s | 767 ms | 22.8 s | 1.2 s |
|Neighbors | 14.1 s | 6.9 s | 31.6 s | 12.8 s |
|UMAP | 31 s | 5 s | 72 s | 5.4 s |
|Louvain | 8.6 s | 152 ms | 27.1 s | 240 ms |
|Leiden | 13.7 s | 171 ms | 35.9 s | 305 ms |
|TSNE | 215 s | 1.9 s | 336 s | 2.3 s |
|Logistic_Regression | 66 s | 3.7 s | 94 s | 8 s |
|Diffusion Map | 612 ms | 358 ms | 1 s | 1.9 s |
|Step |CPU (Ryzen 5950x, 32 Cores, 64GB RAM)|GPU (RTX 3090)|CPU (AMD Eypc Rome, 30 Cores, 500GB RAM)| GPU (Quadro RTX 6000)|GPU (A100 80GB)|
|------------------------------|---------------------------|--------------|----------|--------------|----------------|
|whole Notebook | 728 s | 43 s | 917 s | 67 s | 57 s |
|Preprocessing | 75 s | 21 s | 40 s | 34 s | 30 s |
|Clustering and Visulatization | 423 s | 18 s | 524 s | 27 s | 21 s |
|Normalize_total | 252 ms | > 1ms | 425 ms | 1 ms | 1 ms |
|Highly Variable Genes | 3.2 s | 2.6 s | 4.1 s | 2.7 s | 3.7 s |
|Regress_out | 63 s | 14 s | 24 s | 23 s | 15 s |
|Scale | 1.3 s | 299 ms | 2 s | 2 s | 359 ms |
|PCA | 26 s | 1.8 s | 23 s | 3.6 s | 2.6 s |
|Neighbors | 10 s | 5 s | 16.8 s | 8.1 s | 6 s |
|UMAP | 30 s | 659 ms | 66 s | 1 s | 783 ms |
|Louvain | 16 s | 121 ms | 20 s | 214 ms | 201 ms |
|Leiden | 11 s | 102 ms | 20 s | 175 ms | 152 ms |
|TSNE | 240 s | 1.4 s | 319 s | 1.8 s | 1.4 s |
|Logistic_Regression | 74 s | 4 s | 45 s | 5 s | 3.4 s |
|Diffusion Map | 715 ms | 259 ms | 747 ms | 431 ms | 826 ms |
|Force Atlas 2 | 207 s | 236 ms | 300 s | 298 ms | 353 ms |


It seems like Turing based GPUs are a lot slower running the eigenvector calculations on sparse matrixes needed for Diffusion Maps than Ampere based ones.
I also observed that the first GPU run in a new enviroment is slower than the runs after that (with a restarted kernel) (RTX 6000).
I also observed that the first GPU run in a new enviroment is slower than the runs after that (with a restarted kernel) (RTX 6000).

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