So I start the day contemplating writing some NEON code (and as usually happens - that was from the moment I was awake), and I finally got the Parallella slogan 'A super computer for everyone'.
These days we can get more FLOPS than we can imagine for a few dollars - for the performance, GPU's are cheaper than anything that has ever come before them - but can anyone get those flops out of them?
Threads - are hard enough. And they don't even get you very far.
OpenCL - is hard. If threads were hard enough, try a SIMT programming model. Even when you know what you're doing it can still be a lot of work because once you go beyond simple 'per pixel' type algorithms you have to do all sorts of tricks to get the performance. Again a lot of fun, but rather expensive for whomever is paying the bills. OpenCL is another option for accessing SIMD units which is at least improved over intrinsics and assembly language - but it still has a lot of requirements and overheads which make it harder to code. Any any attempt to reduce that complexity through abstraction - will reduce it's performance.
Assembly language (for SIMD coding) - is even harder. It's fun, and I enjoy it (well, so long as it isn't something as fucked up as x86), but even I sometimes just want a result and not to have to deal with the complexities. But without it you're leaving a big chunk of your CPU's performance on the floor - as there's no real practical way to access the SIMD units otherwise (i include 'intrinsics' as 'assembly language' - TBH it's easier just writing the assembly).
One can get away with using pre-written libraries some of the time - but someone still has to write those libraries, and sometimes they aren't available. And even if they are libraries come with their own costs - such as first learning how to use their API, and then finding their bugs and limitations and learning how to work around those.
The Eiphany architecture does away with a huge amount of complexity on the hardware side - because it needs to to fit on the chip - but it also means it does away with a huge amount of complexity on the software side too. There is no need for SIMD as you have a ton of processors, so there is no need to learn how to write SIMD software. And since it isn't a wide vector processor, there's no need to support dozens of in-flight hardware threads and 'wavefronts', and all the complexity of coding they provide. And this goes double for compiler writers - trying to auto-vectorise code still doesn't work very well after plenty of attempts, and if there are no 'too hard to use' instructions, there's no wasted silicon either. There just isn't need for much abstraction because there isn't anything to abstract. I think it doesn't even have cache - it just has local memory - with fast access, and slower memory as you go further from the chip.
The 16 core chips might just be teasers, but 64 cores is when things start to get interesting.
Will one such architecture be the be-all and end-all for processing? Dunno. The HSA stuff from AMD+co is all about moving the processing to where it best fits. For 'wide loads' this will be a GPU, for 'branchy' stuff it will be a CPU. But GPU's are converging on CPU's anyway - the GCN ISA looks more like a scalar cpu + wide SIMD unit than a GPU. It's the complexity of the total solution that has me most worried though - can AMD pull it off?
Complexity in general is a form of vendor lock-in, so i'm all for anything that reduces it.
Update 19/10/12: So well, I put my money where my mouth is, at the moment I've chucked $550 into the hat.
One insomniacal morning this week I had a look at their facedetect code. Well, ... it's certainly not easy. Took them 7 weeks to write - apparently that indicates it is easy, but then it is a whole new architecture. Then again OpenCV is a pretty horrible library to start with.
I'm not sure on the performance as they don't specify which 'ghz class x86 cpu' they used (and even if they did that wouldn't help much), and any windowed face detection algorithm is highly dependent on the search parameters (it's in the code, but there's a lot to look through at 4am in the morning). But I think they were searching from scale 1 which is by far the most expensive step. It's a pretty strange application to port actually because windowed/cascaded object detection is such a shit algorithm fit for ANY cpu. Let alone one that is optimised for floats.
You'd think something like JPEG or video DCT would be a good example. Wavelets should be a good fit too. But if they really want the punters excited, show a video decode, or even a `phong shaded boing' demo. Fibonacci sequences ... jesus h christ.
The way they wrote the code reminded me more of SPU programming than anything else, but without all that fucking around with SIMD code required to get any performance out of it.
So all that and the fact i'm pretty sick of 'closed' hardware is why I put the dough up. I guess it wont make it anyway with only a week to go but we'll see. It's about the same amount my OpenPandora order cost me ... I wonder which I'll get first ...
Oh, and that it looks insanely fun to code for, not to mention being almost exactly the hardware I want for work ... ;-)