PROJECT #17622 RESEARCH FOR CANCER
FOLDING PERFORMANCE PROFILE

PROJECT SUMMARY

The previous FEC work unit test was completed successfully so now we're running larger "full size" WUs that are more scientifically useful for studying protein mutations with Free Energy Calculations.

As before, the approach and technology are similar to the Moonshot, but these set of projects instead are studying protein mutations and are much larger (since proteins are also much larger) In this case we are trying out multiple mutations and their impact upon a protein-protein of a dimerization complex (RIPK2), the most complex case.

PROJECT INFO

Manager(s): Sukrit Singh

Institution: Memorial Sloan-Kettering Cancer-Center

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

PROJECT WORK UNIT SUMMARY

Atoms: 329,840

Core: OPENMM_22

Status: Public

PROJECT FOLDING PPD AVERAGES BY GPU

PPDDB data as of Sunday, 02 October 2022 12:16:32

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 3090
GA102 [GeForce RTX 3090]
Nvidia GA102 7,408,345 1,901,972 3.90 6 hrs 10 mins
2 GeForce RTX 3070 Lite Hash Rate
GA104 [GeForce RTX 3070 Lite Hash Rate]
Nvidia GA104 3,517,530 1,484,236 2.37 10 hrs 8 mins
3 GeForce RTX 2080 Ti Rev. A
TU102 [GeForce RTX 2080 Ti Rev. A] M 13448
Nvidia TU102 3,017,087 1,570,741 1.92 12 hrs 30 mins
4 GeForce GTX 1080 Ti
GP102 [GeForce GTX 1080 Ti] 11380
Nvidia GP102 1,755,507 1,299,604 1.35 18 hrs 46 mins
5 GeForce GTX 980
GM204 [GeForce GTX 980] 4612
Nvidia GM204 832,076 919,692 0.90 27 hrs 32 mins

PROJECT FOLDING PPD AVERAGES BY CPU BETA

PPDDB data as of Sunday, 02 October 2022 12:16:32

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