Comment by pvnick | Hacker News Reader

Comment by pvnick | Hacker News Reader

Among other things, this has lots of applications for molecular dynamics (computational chemistry simulations) [1]. Before you had to transfer data over to the GPU, which if you're dealing with small data sets and only computationally limited is no big deal. But when you get bigger data sets that becomes a problem. Integrating the GPU and the CPU means they both have access to the same memory, which makes parallelization a lot easier. If, as someone else here said, AMD is partnering with Oracle to abstract the HSA architecture with something more high-level like java [2], then you don't need to go learn CUDA or Mantle or whatever GPU language gets cooked up just for using that hardware.

I'm personally hoping that not only will we get to see more effective medicines in less time, maybe some chemistry research professors will get to go home sooner to spend time with their kids.

[1] http://www.ks.uiuc.edu/Research/gpu/

[2] http://semiaccurate.com/2013/11/11/amd-charts-path-java-gpu/

As to your last point, I suspect there's a sort of Jevons' Paradox for scientific research (and creative problem solving fields in general).

Which is to say that when you're seeing progress more quickly, you'll spend more hours working on the problem, not less.

Determining whether this is good or bad is left as an exercise to the reader :)

Doesn't that kind of imply we're screwed when it comes to sustainability?

In general yes, unless we manufacture 100% of our energy sustainably.

Yes, with regards to the demand side; no, with regards to the supply side.

No, that would be the 2nd law of thermodynamics which guarantees we're fucked when it comes to sustainability.

HSA will also be compatible with LLVM, so you get all the languages supported by that, too.

Can you give examples of medicines developed thanks to computational chemistry simulations?

I'm rather ignorant about this area of research, but it always seemed to me kinda fruitless? Sorta like high throughput screening - a ton of resources and computation is dedicated because it sounds like a good idea... but ultimately there is very little to show for it all.

Way too little signal and way too much noise.

http://en.wikipedia.org/wiki/Drug_design#Rational_drug_disco...

Scroll down to Examples.

Why not just link there? https://en.wikipedia.org/wiki/Drug_design#Examples

I'm not giving you what you want, but here's a visual example of using a computational microscope to understand how drug actually works and finds its binding site:

http://www.youtube.com/watch?v=PGqCeSjNuTY&t=50m27s

If you can't see a difference between that and say, X-Ray crystallography then well..

I would be interested in seeing publications on this - the last time I checked, I think the algorithms and software were being treated as in-house intellectual property by pfizer, novartis, and co

Actually, the "algorithms" are usually fairly straight-forward and well documented integrators for Newtonian dynamics, usually some form of Verlet Integration. The most popular and efficient Molecular Dynamics packages are open source, some GPL (e.g., NAMD, Gromacs). The technology is nowhere near in silico drug design, but we're converging on useful tools to measure important quantities like free energies of binding. Pharmaceutical companies have some interest in this technology, but at this point, don't direct too much effort into atomistic simulations.

ah thanks. My only experience with this was going to a lecture back in school when they brought in someone from Pfizer's in-house modeling team, but the presentation was a bit light on references/citations...

There are lots, and a quick Google search would have cured your unnecessary scepticism. It would be more surprising if this method didn't work than that it does.

Imatinib[1] is a good, and pretty old example:

Imatinib was developed by rational drug design. After the Philadelphia chromosome mutation and hyperactive bcr-abl protein were discovered, the investigators screened chemical libraries to find a drug that would inhibit that protein. With high-throughput screening, they identified 2-phenylaminopyrimidine. This lead compound was then tested and modified by the introduction of methyl and benzamide groups to give it enhanced binding properties, resulting in imatinib

[1] https://en.wikipedia.org/wiki/Imatinib#History

Your example is about experimental HTS, not computational simulations which the parent requested.

Mantle's is a graphics, not a compute API. For compute AMD uses OpenCL and HSAIL.

I'd be surprised if Mantle did not have a way to issue compute shaders.

I've worked with CUDA before (and hit issues with large datasets, like you've mentioned). While HSA is presumably going to make things a lot easier, I'm curious about AMD's partnership with Oracle - how much of a performance hit do you reckon we'll see if Java is implemented? And wouldn't it have been easier to go with something like Python that also supports multithreading and (to the best of my limited knowledge) has better scientific libraries?

It's likely their implementation of Java will add constructs to aid parallelization, but Java already has very robust threading support with no GIL.

Yeah perhaps I could have worded my comment better. I meant Python (just like Java) supports parallelization as well, plus it seems to have better scientific libraries.

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