Is GPGPU a hack ?

Question

Hello Folks,

I had started working on GPGPU some days ago and successfully implemented cholesky factorization with good performacne and I attended a conference on High Performance Computing where some people said that "GPGPU is a Hack".

I am still confused what does it mean and why they were saying it hack. One said that this is hack because you are converting your problem into a matrix and doing operations on it. But still I am confused that does people think it is a hack or if yes then why?

Can anyone help me, why they called it a hack while I found nothing wrong with it.

Answer 1

One possible reason for such opinion is that the GPU was not originally intended for general purpose computations. Also programming a GPU is less traditional and more hardcore and therefore more likely to be perceived as a hack.

The point that "you convert the problem into a matrix" is not reasonable at all. Whatever task you solve with writing code you choose reasonable data structures. In case of GPU matrices are likely the most reasonable datastructures and it's not a hack but just a natural choice to use them.

However I suppose that it's a matter of time for GPGPU becoming widespread. People just have to get used to the idea. After all who cares which unit of the computer runs the program?

Answer 2

I suppose he meant that using GPGPU forced you to restructure your implementation, so that it fitted the hardware, not the problem domain. Elegant implementation should fit the latter.

Note, that the word "hack" may have several different meanings: http://www.urbandictionary.com/define.php?term=hack

Answer 3

Obviously, only the person who said that can say for certain why he said it, but, here's my take:

• A "Hack" is not a bad thing.
• It forces people to learn new programming languages and concepts. For people who are just trying to model the weather or protein folding or drug reactions, this is an unwelcome annoyance. They didn't really want to learn FORTRAN (or whatever) in the first place, and now the have to learn another programming system.
• The programming tools are NOT very mature yet.
• The hardware isn't as reliable as CPUs (yet) so all of the calculations have to be done twice to make sure you've got the right answer. One reason for this is that GPUs don't come with error-correcting memory yet, so if you're trying to build a supercomputer with thousands of processors, the probability of a cosmic ray flipping a bit in you numbers approaches certainty.

As for the comment "you are converting your problem into a matrix and doing operations on it", I think that shows a lot of ignorance. Virtually ALL of high-performance computing fits that description!

Answer 4

One of the major problems in GPGPU for the past few years and probably for the next few is that programming them for arbitrary tasks was not very easy. Up until DX10 there was no integer support among GPUs and branching is still very poor. This is very much a situation where in order to get maximum benefit you have to write your code in a very awkward manner to extract all sorts of efficiency gains from the GPU. This is because you're running on hardware that is still dedicated to processing polygons and textures, rather than abstract parallel tasks.

Obviously, thats my take on it and YMMV

Answer 5

On the GPU, having efficient memory access is paramount to achieving optimal performance. This often involves restructuring or even choosing entirely new algorithms and data structures. This is reason why GPU programming can be perceived as a hack.

Secondly, adapting an existing algorithm to run on the GPU is not in and of itself science. The relatively low scientific contribution of some GPU algorithm-related papers has led to a negative perception of GPU programming as strictly "engineering".

Answer 6

The GPGPU harks back to the days of the math co-processor. A hack is a shortcut to solving a long winded problem. GPGPU is a hack just like NAT on top of IPV4 is a hack. Computational problems just like networks are getting bigger as we try to do more, GPGPU is an useful interim solution, whether it stays outside the core CPU chip and has separate cranky API or gets sucked into the CPU via API or manufacture is up to the path finders.