GPU Supercomputers

HowlerMonkey

You surely won't be shoving X86 code through any supercomputer.

The software has to be written for the hardware.

This is why you used to get many different versions of windows NT.

It was written for a few different platforms.

Most "supercomputing" uses parallel rather than linear processing so the software has to be written for parallel processing.

HowlerMonkey

I wanted to address emulation.

Emulation is a band aid that reduces performance VS a rig running on it's native code.

It's great for versatility but speed is not one of it's virtues.

wuliheron

GPU and GPGPU computing are the current big thing because the technology is maturing, while some are saying reconfigurable computing is the next big thing. That's where the software can physically adapt the hardware to whatever need it has on the fly. For example, with memristors what is memory can become transistors and vice versa allowing a small number of parts to do the job of many. They allow for computing massive recursive functions and other things that would either be impossible or impractical using conventional technology.

Just to give you some idea of how powerful the technology can be IBM's goal for their new neuromorphic chip that incorporates memristors is to have the equivalent of a cat or human brain's neurons on a single chip sometimes within the next ten years. That's immensely compact functionality and it appears the experimentalists might soon leave the theorists in the dust scratching their heads and trying to figure out how best to leverage the technology.

chiro

Bingo.

The algorithms and the development of such, especially for particular architectures like GPU's and the GPGPU's are the more important aspect of computing and not the hardware per se.

If you don't think this is an issue, go ask the theoretical computer scientists what it would mean if many NP-hard problems were transformed into a lower complexity class and whether this would be preferred over having a single 10x increase in computing throughput power and the answer won't be in the least bit surprising.

wuliheron

IBM's neuromorphic chip isn't programmed. It's an adaptive system that learns from experience.

chiro

So what's the point in reference to the response on algorithms? Do you agree/disagree or have any specific comments? I don't know what you are trying to get at.

HowlerMonkey

This neuromorphic stuff from IBM seems very similar to the Ni1000 (nestor/intel) neural network hardware of 1994.

wuliheron

People have been imitating neurons in software and circuitry for decades, however, the ni1000 only had about 3 million transistors while memristors are in the 5nm range allowing for 100gb per square centimeter and even more if they go 3 dimensional which is entirely possible. There's just no comparison. In addition IBM used the latest brain scanning technology to study how the neurons in a cat's frontal lobes are networked. It's a step into wonderland where all our theories start to break down and we don't even have existing supercomputers that can crunch the numbers.

The "Super Turing Model" is also being pursued at the same time so we will hopefully see some convergence between the experimental and theoretical approaches some time in the next decade or so.

HowlerMonkey

It's called progress.

It has been 19 years since the Ni1000

wuliheron

Past a certain point progress attains the status of unexplored territory.