How GPU came to be used for general computation

http://igoro.com/archive/how-gpu-came-to-be-used-for-general-computation/

by urlwolf • 16 years ago

52 17 16 years ago

Aron• 16 years ago

This is a good introduction. I'm interested in finding articles that speculate about just how aligned the design requirements are between graphics, and most matrix-based scientific and data mining computation.

For instance, Nvidia has introduced double-precision support and L1 cache, which has marginal value in traditional graphics. This is going to hurt their profitability on the Fermi chip compared to the simpler ATI alternatives.

I am gonna enjoy watching how all this plays out.

liuliu3• 16 years ago

I was puzzled how that can impact data mining or machine learning as a whole, too. The difference between data mining algorithms and image processing/string matching algorithms is huge. It requires more data to get some meaningful intermediate results on one computing kernel. For example, a typical scene in my research is to compare the performance of many proposed features (tens of thousands) against large volume of data, and pick the best one. It is an embarrassing parallelization problem, but the data throughput is huge. On supercomputers, it is easy since every computer can have a local copy of either feature set or data set. But for GPGPU, there is no way for each core to have a local copy of either set. Thus, to compare against each other, GPGPU must go back and forth to its shared memory, and the limited bandwidth may harm the performance badly.

Disclaimer: I am not very experienced in GPGPU field, so my worrying may be proved wrong.

miloshh• 16 years ago

The GPU cores share RAM, so there is no need to have local copies. Though if you run out of the 4GB available on Tesla/Quadro cards, it gets more complicated (but so would it on the CPU).

andrewcooke4• 16 years ago

it's horribly hard to accurately predict what will work and what not, and they do have some caching ability (what would cache a texture map when rendering an image).

but what you are perhaps missing is that it's ok for gpus to read memory, as long as you have enough threads. they can switch context very quickly, so one set of threads can request memory (hopefully a contiguous chunk) and then drop into the background and let another set of threads do some work (on the same processing unit). this is critical to their efficiency and is very different to a cpu, which instead relies on cache and "sits doing nothing" if it needs to read data from "afar" (obviously there are trade-offs - there's only so much local memory for state, for example).

i worked on a problem that was not as "nice" as you might hope - the memory access was unpredictable to some degree. but i still got a speed up of "tens" on a cheap ($200) graphics card, compared to a meaty xeon. it's more robust than you might expect.

andrewcooke4• 16 years ago

i would guess double precision support is pretty much a no-brainer. it's a deal breaker for many numerical applications and isn't that expensive (amd/ati have 64 bit support too on the latest firestream cards).

fermi's cache, on the other hand, probably implies more trade-offs. but you can look at it as a necessary step in learning how to find a middle ground between gpus and cpus - which is the next big battle.

wazoox• 16 years ago

Now I'd like to know more about what general uses there are to these beasts. I see the point for simulating fluidsn etc., but out of climate researchers and aerospace engineers, who needs these sort of tools nowadays? sincerely wondering.

torial• 16 years ago

Extremely fast string matching -- I've seen a few research papers on the move to this. Which can play into DNA sequence matching, or information retrieval.

GPU is also good for anything that requires extensive parallel number crunching, like password cracking.

If you search for "GPU String Matching" you should find some good results.

hassy• 16 years ago

As torial said many algorithms lend themselves well for running on GPUs. Algorithms can also be designed to be run on GPUs.

These guys are using GPUs to convert series of images into 3D models in biology http://vidiowiki.com/watch/m5xbpad/

These guys use GPUs in sound synthesis: http://vidiowiki.com/watch/w9h9mfe/

andrewcooke4• 16 years ago

i write code for gpus, sometimes. they're nice for many inverse problems - anything that constructs a map or image from some kind of measurement. in my case i work on geophysical applications.

eru• 16 years ago

See http://www.wired.com/magazine/2010/02/ff_algorithm/all/1 for a fluffy introduction to what's possible. (I hope I got it right that compressed sensing is related to inverse problems.)

bravura• 16 years ago

You can use GPUs to accelerate machine learning algorithms.

What can you do with machine learning? Basically everything: "Applications for machine learning include machine perception, computer vision, natural language processing, syntactic pattern recognition, search engines, medical diagnosis, bioinformatics, brain-machine interfaces and cheminformatics, detecting credit card fraud, stock market analysis, classifying DNA sequences, speech and handwriting recognition, object recognition in computer vision, game playing, software engineering, adaptive websites and robot locomotion." (http://en.wikipedia.org/wiki/Machine_learning)

In our lab, we have developed a package called Theano (http://www.deeplearning.net/software/theano/), which allows you to take Python numpy code, adapt it slightly, and then automatically compile the mathematical functions in a optimized function graph which is transformed into C code and compiled to target the CPU or GPU. Which means to say, your matrix mathematics and machine learning algorithms just got a lot faster, with little cost in programmer time.

xenthral2• 16 years ago

I've used it to make my conway's game of life implementation, and make it run really fast with a naive brute force approach. http://vimeo.com/9516535

So add toy programs to that list :)

anigbrowl• 16 years ago

Very nice! I would love to mess with that if/when you're ready to release source.

How GPU came to be used for general computation

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