Slashdot Mirror
NVIDIA Shaking Up the Parallel Programming World
An anonymous reader writes "NVIDIA's CUDA system, originally developed for their graphics cores, is finding migratory uses into other massively parallel computing applications. As a result, it might not be a CPU designer that ultimately winds up solving the massively parallel programming challenges, but rather a video card vendor. From the article: 'The concept of writing individual programs which run on multiple cores is called multi-threading. That basically means that more than one part of the program is running at the same time, but on different cores. While this might seem like a trivial thing, there are all kinds of issues which arise. Suppose you are writing a gaming engine and there must be coordination between the location of the characters in the 3D world, coupled to their movements, coupled to the audio. All of that has to be synchronized. What if the developer gives the character movement tasks its own thread, but it can only be rendered at 400 fps. And the developer gives the 3D world drawer its own thread, but it can only be rendered at 60 fps. There's a lot of waiting by the audio and character threads until everything catches up. That's called synchronization.'"
Wow, I bet nobody on slashdot knew that!
Do it yourself, because no one else will do it yourself. [beta blockade 10-17 Feb]
The articles sums up the hurdles of parallel programming and says that NVIDIA's CUDA is doing something to solve them but it doesn't say what. Even the short Wikipedia entry at http://en.wikipedia.org/wiki/CUDA tells more about it.
Libertarian Leaning Political Discussion Forum.
Many moons ago, when most slashdotters were nippers, a British company named INMOS provided an extensible hardware and software platform that solved the problem of parallelism, in many ways similar to CUDA.
Ironically, some of the first demos I saw using transputers was raytracing demos.
The problem of parallelism and the solutions available are quite old (more than 20 years), but it's only now that limits are reached that we see the true need for it. But the true pioneers is not NVIDIA, because there were others long before them.
"News for Nerds, Stuff that matters".
But not if posted by The Ignorant.
What if the developer gives the character movement tasks its own thread, but it can only be rendered at 400 fps. And the developer gives the 3D world drawer its own thread, but it can only be rendered at 60 fps. There's a lot of waiting by the audio and character threads until everything catches up. That's called synchronization.
If a student of mine wrote this, a Fail will be the immediate consequence. How can 400 fps be 'only'? And why is threading bad, if the character movement is ready after 1/400 second? There is not 'a lot of waiting'; instead, there are a lot of cycles to calculate something else. and 'waiting' is not 'synchronisation'.
[The audio-rate of 7000 fps gave the author away; and I stopped reading. Audio does not come in fps.]
While we all agree on the problem of synchronisation in parallel programming, and maybe especially in the gaming world, we should not allow uninformed blurb on Slashdot.
Back in the early 80's I was working in Bristol UK for TDI (who were the UCSD p-system licensees) porting it to various machines... Well, we had one customer who wanted a VAX p-system so we trotted off to INMOS's office and sat around in the computer room. (VAX 11/780 I think). At the time they were running Transputer simulations on the machine so the VAX p-system took er... about 30 *minutes* to start. Just for comparison an Apple ][ running IV.x would take less than a minute. Almost an hour to make a tape. (About 15 users running emulation I think). Fond memories of the transputer. Almost bought a kit to play with it... Andy
CUDA is an interesting way to utilize NVIDIA's graphics hardware for tasks it wasn't really designed for, but it's not a solution to parallel computing in and of itself. (more on that momentarily) A few people have gotten their nice high end Quadros to do some pretty cool stuff, but to date it's been limited primarily to relatively minor academic purposes. I don't see CUDA becoming big in gaming circles anytime soon. Let's face it, most gamers buy *one* reasonably good video card and leave it at that. Your video card has better things to do than handle audio or physics when your multi-core CPU is probably being criminally underutilized. Nvidia, of course, wants people to buy wimpy CPU's and then load up on massive SLI rigs and then do all their multi-purpose computation in CUDA. Not gonna happen.
First of all, there are very few general purpose applications that special purpose NVIDIA hardware running CUDA can do significantly better than a real general purpose CPU, and Intel intends to cut even that small gap down within a few product cycles. Second, nobody wants to tie themselves to CUDA when it's built entirely for proprietary hardware. Third, CUDA still has a *lot* of limitations. It's not as easy to develop a physics engine for a GPU using CUDA as it is for a general purpose CPU.
Now, I haven't used CUDA lately, so I could be way off base here. However, multi-threading isn't the real challenge to efficient use of resources in a parallel computing environment. It's designing your algorithms to be able to run in parallel in the first place. Most multi-threaded software out there still has threads that have to run on a single CPU, and the entire package bottlenecks on the single CPU running that thread even if other threads are free to run on other processors. This sort of bottleneck can only be avoided at the algorithm level. This isn't something CUDA is going to fix.
Now, I can certainly see why NVIDIA is playing up CUDA for all they're worth. Video game graphics rendering could be on the cusp of a technological singularity. Namely, ray tracing. Ray tracing is becoming feasible to do in real time. It's a stretch at present, but time will change that. Ray tracing is a significant step forward in terms of visual quality, but it also makes coding a lot of other things relatively easy. Valve's recent "Portal" required some rather convoluted hacks to render the portals with acceptable performance, but in a ray tracing engine those same portals only take a couple lines of code to implement and have no impact on performance. Another advantage of ray tracing is that it's dead simple to parallelize. While current approaches to video game graphics are going to get more and more difficult to work with as parallel processing rises, ray tracing will remain simple.
The real question is whether NVIDIA is poised to do ray-tracing better than Intel in the next few product cycles. Intel is hip to all of the above, and they can smell blood in the water. If they can beef up the floating point performance of their processors then dedicated graphics cards may soon become completely unnecessary. NVIDIA is under the axe and they know it, which might explain all the recent anti-Intel smack-talk. Still, it remains to be seen who can actually walk the walk.
From my experience, CUDA was much harder to take advantage of then multi-core programming. CUDA requires you to use a specific model of programming that can make it difficult to take advantage of the full hardware. The restricted caching scheme makes memory management a pain, and the global synchronization mechanism is very crude - there's a barrier after each kernel execution, and that's it. It took me a week to 'parallelize' port some simple code I had written to CUDA, whereas it took my an hour or so to add the OpenMP statements to my 'reference' CPU code. Sorry Nvidia - there is no silver bullet. By making some parts of parallel programming easy, you make others hard or impossible.
Consider that if you've ever done UNIX programming, you've been doing MT programming all along - just by a different name.. Multi-Processing. Pipelines are, in IMO the best implementation of parallel programming (and UNIX is FULL of pipes). You take a problem and break it up into wholly independent stages, then multi process or multi-thread the stages. If you can split the problem up using message-passing then you can farm the work out to decoupled processes on remote machines, and you get farming / clustering. Once you have the problem truely clustered, then multi-threading is just a cheaper implementation of multi-processing (less overhead per worker, less number of physical CPUs, etc).
Consider this parallel programing pseudo-example
find | tar | compress | remote-execute 'remote-copy | uncompress | untar'
This is a 7 process FULLY parallel pipeline (meaning non-blocking at any stage - every 512 bytes of data passed from one stage to the next gets processed immediately). This can work with 2 physical machines that have 4 processing units each, for a total of 8 parallel threads of execution.
Granted, it's hard to construct a UNIX pipe that doesn't block.. The following variation blocks on the xargs, and has less overhead than separate tar/compress stages but is single-threaded
find name-pattern | xargs grep -l contents-pattern | tar-gzip | remote-execute 'remote-copy | untar-unzip'
Here the message-passing are serialized/linearized data.. But that's the power of UNIX.
In CORBA/COM/GNORBA/Java-RMI/c-RPC/SOAP/HTTP-REST/ODBC, your messages are 'remoteable' function calls, which serialize complex parameters; much more advanced than a single serial pipe/file-handle. They also allow synchronous returns. These methodologies inherently have 'waiting' worker threads.. So it goes without saying that you're programming in an MT environment.
This class of Remote-Procedure-Calls is mostly for centralization of code or central-synchronization. You can't block on a CPU mutex that's on another physically separate machine.. But if your RPC to a central machine with a single variable mutex then you can.. DB locks are probably more common these days, but it's the exact same concept - remote calls to a central locking service.
Another benifit in this class of IPC (Inter Process Communication) is that a stage or segment of the problem is handled on one machine.. BUt a pool of workers exists on each machine.. So while one machine is blocking, waiting for a peer to complete a unit of work, there are other workers completing their stage.. At any given time on every given CPU there is a mixture of pending and processing threads. So while a single task isn't completed any faster, a collection of tasks takes full advantage of every CPU and physical machine in the pool.
The above RPC type models involve explicit division of labor. Another class are true opaque messages.. JMS, and even UNIX's 'ipcs' Message Queues. In Java it's JMS. The idea is that you have the same workers as before, but instead of having specific UNIQUE RPC URI's (addresses), you have a common messaging pool with a suite of message-types and message-queue-names. You then have pools of workers that can live ANYWHERE which listen to their queues and handle an array of types of pre-defined messages (defined by the application designer). So now you can have dozens or hundreds of CPUs, threads, machines all symmetriclly passing asynchronous messages back and forth.
To my knowledge, this is the most scaleable type of problem.. You can take most procedural problems and break them up into stages, then define a message-type as the explicit name of each stage, then divide up the types amongst different queues (which would allow partitioning/grouping of computational resources), then receive-message/process-message/forward-or-reply-message. So long as the amount of work far exceeds the overhead of message passing, you can very nicely scale with the amount of hardware you can throw at the problem.
-Michael