PROJECT #17604 RESEARCH FOR CANCER
FOLDING PERFORMANCE PROFILE

PROJECT SUMMARY

This project is an attempt at implementing umbrella sampling in Folding@home.

Umbrella sampling is a way of "pulling" the protein to new configurations by attaching a spring to specific atoms to move into a certain configuration. Identifying druggable states or exploring conformational state space relevant to disease is an existing challenge.

The embarassingly parallel nature of Folding@home allows us to massively scale up our exploration.

However, the underlying methods still rely on luck to a large extent – we must discover the states in work units as the dataset grows in size and more work units are run.

This can be an incredibly inefficient and slow process.

To help speed up state discovery and exploration, we can place 'springs' at regularly spaced intervals in our configuration space, and pull any independent simulation to one of these springs.

This "spring pulled simulation" is called Umbrella sampling (because the shape of the space explored around the spring looks like an parabola/umbrella).

With FAH, we can run multiple umbrellas at once, pulling each individual RUN to a unique point in conformational space independently of other RUNs.

In doing so we are able to massively scale up our sampling and discovery of unique states in a protein's conformational landscape. This project is identical in calculation to 16497, exploring conformations of MET kinase, involved in non-small-cell lung carcinoma, but acting as a test bed..

PROJECT INFO

Manager(s): Sukrit Singh

Institution: Memorial Sloan-Kettering Cancer-Center

Project URL: http://sukritsingh.github.io/

PROJECT WORK UNIT SUMMARY

Atoms: 59,897

Core: OPENMM_22

Status: Public

PROJECT FOLDING PPD AVERAGES BY GPU

PPDDB data as of Thursday, 01 December 2022 06:13:27

Rank
Project
Model Name
Folding@Home Identifier
Make
Brand
GPU
Model
PPD
Average
Points WU
Average
WUs Day
Average
WU Time
Average
1 GeForce RTX 3080 Ti
GA102 [GeForce RTX 3080 Ti]
Nvidia GA102 4,575,937 295,932 15.46 2 hrs 33 mins
2 GeForce RTX 3080 Lite Hash Rate
GA102 [GeForce RTX 3080 Lite Hash Rate]
Nvidia GA102 4,368,945 285,082 15.33 2 hrs 34 mins
3 GeForce RTX 3090
GA102 [GeForce RTX 3090]
Nvidia GA102 4,114,919 286,098 14.38 2 hrs 40 mins
4 GeForce RTX 3090 Ti
GA102 [GeForce RTX 3090 Ti]
Nvidia GA102 3,817,751 284,139 13.44 2 hrs 47 mins
5 GeForce RTX 3070 Ti
GA104 [GeForce RTX 3070 Ti]
Nvidia GA104 3,657,410 277,678 13.17 2 hrs 49 mins
6 GeForce RTX 2080 Ti
TU102 [GeForce RTX 2080 Ti] M 13448
Nvidia TU102 3,589,676 272,186 13.19 2 hrs 49 mins
7 GeForce RTX 2080 Ti Rev. A
TU102 [GeForce RTX 2080 Ti Rev. A] M 13448
Nvidia TU102 3,344,915 269,869 12.39 2 hrs 56 mins
8 GeForce RTX 3070 Lite Hash Rate
GA104 [GeForce RTX 3070 Lite Hash Rate]
Nvidia GA104 3,282,713 269,472 12.18 2 hrs 58 mins
9 GeForce RTX 2080 Rev. A
TU104 [GeForce RTX 2080 Rev. A] 10068
Nvidia TU104 3,187,731 262,933 12.12 2 hrs 59 mins
10 GeForce RTX 3070
GA104 [GeForce RTX 3070]
Nvidia GA104 3,041,036 261,837 11.61 2 hrs 4 mins
11 GeForce RTX 2080 Super
TU104 [GeForce RTX 2080 SUPER]
Nvidia TU104 3,023,379 260,854 11.59 2 hrs 4 mins
12 GeForce RTX 3080
GA102 [GeForce RTX 3080]
Nvidia GA102 2,917,529 255,462 11.42 2 hrs 6 mins
13 GeForce RTX 3060 Ti Lite Hash Rate
GA104 [GeForce RTX 3060 Ti Lite Hash Rate]
Nvidia GA104 2,687,927 239,680 11.21 2 hrs 8 mins
14 GeForce RTX 3080 Mobile / Max-Q 8GB/16GB
GA104M [GeForce RTX 3080 Mobile / Max-Q 8GB/16GB]
Nvidia GA104M 2,538,248 247,981 10.24 2 hrs 21 mins
15 Tesla P100 16GB
GP100GL [Tesla P100 16GB] 9340
Nvidia GP100GL 1,995,937 228,227 8.75 3 hrs 45 mins
16 GeForce RTX 2060
TU104 [GeForce RTX 2060]
Nvidia TU104 1,967,842 221,795 8.87 3 hrs 42 mins
17 GeForce RTX 2070 Rev. A
TU106 [GeForce RTX 2070 Rev. A]
Nvidia TU106 1,946,004 226,650 8.59 3 hrs 48 mins
18 GeForce RTX 3060
GA104 [GeForce RTX 3060]
Nvidia GA104 1,894,310 225,075 8.42 3 hrs 51 mins
19 GeForce GTX 1080 Ti
GP102 [GeForce GTX 1080 Ti] 11380
Nvidia GP102 1,863,995 222,433 8.38 3 hrs 52 mins
20 GeForce RTX 2060 Super
TU106 [GeForce RTX 2060 SUPER]
Nvidia TU106 1,846,460 222,616 8.29 3 hrs 54 mins
21 GeForce RTX 3060 Lite Hash Rate
GA106 [GeForce RTX 3060 Lite Hash Rate]
Nvidia GA106 1,484,044 204,930 7.24 3 hrs 19 mins
22 GeForce GTX 1660 SUPER
TU116 [GeForce GTX 1660 SUPER]
Nvidia TU116 1,267,217 195,168 6.49 4 hrs 42 mins
23 GeForce GTX 1080
GP104 [GeForce GTX 1080] 8873
Nvidia GP104 1,244,377 195,049 6.38 4 hrs 46 mins
24 GeForce GTX 1070 Ti
GP104 [GeForce GTX 1070 Ti] 8186
Nvidia GP104 1,219,206 195,927 6.22 4 hrs 51 mins
25 Quadro RTX 4000
TU104GL [Quadro RTX 4000]
Nvidia TU104GL 1,186,194 191,663 6.19 4 hrs 53 mins
26 GeForce RTX 3060 Ti
GA104 [GeForce RTX 3060 Ti]
Nvidia GA104 1,103,841 188,492 5.86 4 hrs 6 mins
27 GeForce GTX 1070
GP104 [GeForce GTX 1070] 6463
Nvidia GP104 1,000,849 182,858 5.47 4 hrs 23 mins
28 Tesla M40
GM200GL [Tesla M40] 6844
Nvidia GM200GL 828,729 170,052 4.87 5 hrs 55 mins
29 GeForce GTX 1060 6GB
GP106 [GeForce GTX 1060 6GB] 4372
Nvidia GP106 713,177 161,802 4.41 5 hrs 27 mins
30 GeForce GTX 1660
TU116 [GeForce GTX 1660]
Nvidia TU116 682,918 158,940 4.30 6 hrs 35 mins
31 GeForce GTX 980
GM204 [GeForce GTX 980] 4612
Nvidia GM204 611,049 153,261 3.99 6 hrs 1 mins
32 GeForce GTX 1060 3GB
GP106 [GeForce GTX 1060 3GB] 3935
Nvidia GP106 526,509 144,620 3.64 7 hrs 36 mins
33 GeForce RTX 3060 Mobile / Max-Q
GA106M [GeForce RTX 3060 Mobile / Max-Q]
Nvidia GA106M 466,687 68,863 6.78 4 hrs 32 mins
34 P106-090
GP106 [P106-090]
Nvidia GP106 317,692 123,593 2.57 9 hrs 20 mins
35 Quadro M4000
GM204GL [Quadro M4000]
Nvidia GM204GL 302,035 121,967 2.48 10 hrs 41 mins
36 GeForce GTX 950
GM206 [GeForce GTX 950] 1572
Nvidia GM206 217,537 109,035 2.00 12 hrs 2 mins
37 Quadro P1000
GP107GL [Quadro P1000]
Nvidia GP107GL 182,001 100,942 1.80 13 hrs 19 mins
38 GeForce GTX 970
GM204 [GeForce GTX 970] 3494
Nvidia GM204 173,115 109,619 1.58 15 hrs 12 mins
39 GeForce GT 1030
GP108 [GeForce GT 1030]
Nvidia GP108 41,778 52,740 0.79 30 hrs 18 mins

PROJECT FOLDING PPD AVERAGES BY CPU BETA

PPDDB data as of Thursday, 01 December 2022 06:13:27

Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make