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Having read through the documents, I see that it is capable of ~200 GFLOPS. Obviously, this outshines my macbook performance of ~50GFLOPS, but it is substantial

Having read through the documents, I see that it is capable of ~200 GFLOPS. Obviously, this outshines my macbook performance of ~50GFLOPS, but it is substantially less than my workstations GPU, an Nvidia GTX 770, capable of ~3000 GFLOPS. I suppose my question is why would I choose to build something like this over using a GPU?

bri3d 12y ago
Here's some previous discussion: https://news.ycombinator.com/item?id=4702456 https://news.ycombinator.com/item?id=4702456 In terms of raw GFLOPS, you shouldn't. Parallela offers something halfway between a multi-CPU cluster, differing in terms of memory access, and a GPU, differing in that each core is a real core and can be independently making calls, branching, etc. Another point to note is that every GPU architecture is different and that some support a different degree of control flow parallelism vs. data parallelism. Ultimately it depends on the kernel you're working with - if you're to a point where you've got strictly linear SIMD and you depend entirely on floating-point math throughput or memory bandwidth, it wouldn't make sense not to use a GPU instead.
- moconnor 12y ago
  Although if you like or have code suitable for a multicore architecture you would get better performance from a Xeon Phi or the forthcoming Knight's Landing from Intel, both of which run x86_64 Linux. Between ARM, OpenPower and Intel's Phi successors this is becoming a hyper-competitive space. Interesting times ahead!
toddan 12y ago
I guess it is easier to program, I am only a simple web developer and i would be more productive programming on this than learning to use cuda.
illumen 12y ago
For the experience building it mainly I guess. The author also mentions machine learning and image processing. Doing multiple different tasks at once. Which I think is possible with the latest CUDA and Nvidia gear though. It has 8 FPGAs on there too. Useful for highspeed IO and other tasks. Also, there are 8 HDMI outputs on there. Which would make this a pretty interesting video wall machine. If it had the 64 core chips instead of the 16 core chips, this thing would be ~ 800 GFLOPS I guess.
rasz_pl 12y ago
Its like gardening. No point really, other than hobby.
- angersock 12y ago
  But when I'm gardening, I prefer to see bugs. It means everything is healthy and happy. :(
vidarh 12y ago
The Parallella is a testbed for their chips, to let people experiment with the platform. The real value is for embedded use (of the chips, not the whole current design), and as they scale up the chips - their roadmap is for chips with thousands of cores. Compared with your GPU, there's also another important factor: These are entirely independent cores. For stuff that your GPU does well - that is, stuff where you're doing the exact same manipulation of a large number of values in parallel - it's likely to keep beating the epiphany chips. For stuff it doesn't do well, that is, stuff where you need indpendent threads of execution for each data stream, the Epiphany chips may become a better fit.

bri3d 12y ago

Here's some previous discussion: https://news.ycombinator.com/item?id=4702456 https://news.ycombinator.com/item?id=4702456 In terms of raw GFLOPS, you shouldn't. Parallela offers something halfway between a multi-CPU cluster, differing in terms of memory access, and a GPU, differing in that each core is a real core and can be independently making calls, branching, etc. Another point to note is that every GPU architecture is different and that some support a different degree of control flow parallelism vs. data parallelism. Ultimately it depends on the kernel you're working with - if you're to a point where you've got strictly linear SIMD and you depend entirely on floating-point math throughput or memory bandwidth, it wouldn't make sense not to use a GPU instead.

moconnor 12y ago
Although if you like or have code suitable for a multicore architecture you would get better performance from a Xeon Phi or the forthcoming Knight's Landing from Intel, both of which run x86_64 Linux. Between ARM, OpenPower and Intel's Phi successors this is becoming a hyper-competitive space. Interesting times ahead!

toddan 12y ago

I guess it is easier to program, I am only a simple web developer and i would be more productive programming on this than learning to use cuda.

illumen 12y ago

For the experience building it mainly I guess. The author also mentions machine learning and image processing. Doing multiple different tasks at once. Which I think is possible with the latest CUDA and Nvidia gear though. It has 8 FPGAs on there too. Useful for highspeed IO and other tasks. Also, there are 8 HDMI outputs on there. Which would make this a pretty interesting video wall machine. If it had the 64 core chips instead of the 16 core chips, this thing would be ~ 800 GFLOPS I guess.

rasz_pl 12y ago

Its like gardening. No point really, other than hobby.

angersock 12y ago
But when I'm gardening, I prefer to see bugs. It means everything is healthy and happy. :(

vidarh 12y ago

The Parallella is a testbed for their chips, to let people experiment with the platform. The real value is for embedded use (of the chips, not the whole current design), and as they scale up the chips - their roadmap is for chips with thousands of cores. Compared with your GPU, there's also another important factor: These are entirely independent cores. For stuff that your GPU does well - that is, stuff where you're doing the exact same manipulation of a large number of values in parallel - it's likely to keep beating the epiphany chips. For stuff it doesn't do well, that is, stuff where you need indpendent threads of execution for each data stream, the Epiphany chips may become a better fit.