Description

Welcome to GPUGRID

GPUGRID is a volunteer computing project that harnesses the power of GPUs (Graphics Processing Units) to conduct cutting-edge scientific research. By utilizing the computational power of GPUs, we can perform complex calculations and simulations at a much faster rate than traditional CPU-based systems.

Important: We have just moved to a new server. The website does not look the same as before. We are in the process of updating it

Our Goals

GPUGRID aims to advance scientific knowledge in various fields, including:

  • Drug Discovery: Helping to identify new drugs for diseases like cancer, HIV, and neurodegenerative disorders.
  • Protein Folding: Understanding the folding processes of proteins to uncover mechanisms of diseases and design better therapeutics.
  • Molecular Dynamics: Simulating the behavior of biomolecules to study their functions and interactions.

How You Can Help

By joining GPUGRID, you can contribute your GPU's computational power to assist in our scientific research. Here's how you can get involved:

  • Install BOINC: Download and install the BOINC software from here.
  • Join GPUGRID: Attach to the GPUGRID project through the BOINC manager.
  • Contribute: Your GPU will automatically start processing tasks and contributing to our research.

What is GPUGRID?

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News

JOIN GPUGRID discord
We mainly use discord these days:

https://discord.gg/dCMkcafPpX
10 Apr 2025, 13:47:05 UTC · Discuss

Moved to New Server
Hello GPUGRID Community,

We have recently moved to a new server! This transition will enhance the capabilities of the project.

We are currently redeploying applications and addressing some bugs that have emerged. Your patience and support during this process are greatly appreciated.

For quicker responses from the team, please check out our Discord channel: https://gpugrid.net/gpugrid/forum_thread.php?id=5435 . We also continue to monitor this forum, so both avenues of communication remain open.

Thank you for your continued contributions to GPUGRID!

Best regards,
The GPUGRID Team
26 Mar 2025, 9:25:29 UTC · Discuss

In-silico Binding Assay (ISBA/ACEMD3)
This is a message Adrià posted in our discord channel. Since he doesn't have an account in the GPUGRID forum I open the thread for him.
If you want to be more up to date to the news related to this project and others please join our discord, we are usually more active there:

https://discord.gg/dCMkcafPpX

Hello GPUGRID!

Here Adrià. I'll be recovering the ACEMD3 application again, and sending new jobs of standard MD simulations
(We've been testing it these past weeks to make sure it worked well for both Windows and Linux)

The main goal of these new batch of simulations will be to validate further our capacity to predict the binding mode of ligands using simulations and adaptive sampling methods. Those of you that have been around for some time here might already be familiar with these simulations, such as the Benzamidin-Trypsin system (https://www.pnas.org/doi/abs/10.1073/pnas.1103547108) or the Dopamine D3 receptor with an antagonist ligand (https://www.nature.com/articles/s41598-018-19345-7#Ack1), which we were able to simulate thanks to GPUGRID and all your effort!

Now, we are revisiting this method, which we call in-silico binding assay (ISBA). During drug discovery campaings, it's common that you know of ligands that bind to your target, but you don't know their binding mode, the exact conformation and structure that both the ligand and the protein have when bound. Knowing the binding mode is critical for further development of the molecule into a potent and usable drug.

The most precise way of discovering the binding mode is with crystallization. However, that can take too much time or be directly impossible, depending on the protein. Therefore, we want to optimize and refine ISBA for binding mode prediction, so it can be usable during drug discovery projects. To summarize a bit our objectives, we want to predict binding modes for larger molecules than Benzamidin, with the same precision, but with less simulation time that was needed for the D3 receptor system.

To do so, we'll be using the latest version of adaptive sampling that we developed, AdaptiveBandit (https://pubs.acs.org/doi/abs/10.1021/acs.jctc.0c00205). The objective of these new simulations I'll be sending will be to benchmark AdaptiveBandit in an ISBA scenario, improve the algorithm if required and fine-tune its hyperparameters.

Let me know if there's any issue with the simulations. I'll be sending 100ns trajectories for the most part, divided in two steps.

2 May 2024, 9:28:34 UTC · Discuss

Discord channel for GPUGRID
Hi,
I have created a discord channel for GPUGRID. It kind of duplicates this in some aspects but maybe people are more used to Discord.

It is an additional channel that may help to build up the community.

JOIN:
https://discord.gg/abpWXawZ7v

GDF
13 Feb 2024, 11:29:01 UTC · Discuss

PYSCFbeta: Quantum chemistry calculations on GPU
Hello GPUGRID!

We are deploying a new app "PYSCFbeta: Quantum chemistry calculations on GPU". It is currently in testing/beta stage. It is only on Linux at the moment.

The app performs quantum chemistry calculations. At the moment we are using it specifically for Density Functional Theory calculations: http://en.wikipedia.org/wiki/Density_functional_theory

These types of calculations allow us to accurately compute specific properties of small molecules.


The current test work units have a runtime of the order 1hr (very much dependent on the GPU speed and size of molecule). Each work unit currently contains 1 molecule with ~10 configurations.

The app will not work on GPUs with compute capability less than 6.0. It should not be sending them to these cards but I think at the moment this functionality is not working properly.

The work-units require a lot of GPU memory. It works best if the work-unit is the only thing running on the GPU. If other programs are using significant GPU memory the work-unit might fail.

Looking forward to hearing feedback from you.

Steve
12 Jan 2024, 13:03:21 UTC · Discuss

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