We are inmediately crunching whatever it produces

Thanks. That is what I wanted to know.

All the additional people from WCG tasking the work rate over the rate of production.

Will you create an application for AMD GPUs?

I'm actually on it.

The client code is open-source and anyone willing can improve it. Several people from outside the research group already added very clever stuff (see posts by "[B@P] Daniel"), but so far no GPU version made it to stable. I'm now focusing on a new OpenCL-GPU (Nvidia and AMD GPU) implementation.

The current code cannot be ported 1:1, but the idea behind it can be ported. The switch from sequential to parallel will have 2 effects:

1. the raw number of calculations goes up by a factor of 1.5 - 2. That isn't so bad as most computers have way, way faster GPU than CPU.

2. the results will slightly change. I contacted the team about this. They seem to be willing to at least consider the option. The change in results implies the CPU and GPU version cannot crunch the same WU without major changes on the server side. But the scientific output will be comparable.

TN-Grid recovers links between gene expressions. Nobody knows for sure what the true result is. There is the uncertainty related to the measurement itself, statistics,... . But there are also test data sets generated by a human (ground truth data). Even for those datasets the algorithms don't hit the target perfectly. Getting the main parts right is good enough, for various liberal definitions of "main", "right" and "good enough".

Porting the code takes time, don't hold your breath. I'm not part of the team, i'm not related to the university, the scientific team may not like the result in the end and i do this on my own in my free time. It currently looks doable without major gotchas. It will be faster than the CPU version, but not by an exorbitant factor. Expect 25x or maybe 100x faster than a single CPU-core, not some magic 10.000x faster. Given that modern CPUs with 8 cores / 16 threads already run 16 WU in parallel, the impact will not be a total game changer. The current code is already good and highly optimized.

ETA wise think months, not days. And the results need to be verified, which will also take time.

If your new computational resources are sufficient, don't feel the need to "make work" to keep crunchers happy, I have several "silent" projects on my machines, every now and then, one of them pushes a couple of weeks work, then goes silent again. Remember, the crunchers are trying to help you, they should never be a burden.

The AMD grant is an important temporary boost to our computational resources that will hopefully help us to explore new scientific frontiers. The gene@home project doesn't have a deadline. Our algorithms are very "generic", they can easily be applied to other organisms, other datasets (differently built) of the same organism, not only in the biological field, they can be useful whenever you have a lot of variables, each one with different experimental values and you need to find causal relationships among them. The algorithm itself could also be improved, others may be added.
We will continue to move on, with your help.