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Nonetheless, Epiphany does see a few situations where it might perform better than a classic CPU and better than a classic GPU. Its just a different softwar

by c0g 13y ago

Nonetheless, Epiphany does see a few situations where it might perform better than a classic CPU and better than a classic GPU. Its just a different software architecture, focusing on a different problem niche. Do you have any examples?

dspillett 13y ago
By my understanding any situation where you need the computing power with the requirement for very low energy input and waste energy output. I can't think of any specific examples (perhaps an embedded system that needs to perform a pile of cryptographic operations?) but its ability to do a significant amount of processing on the 2W power footprint it is in a different class to current desktop CPUs and GPUs. Even if this $99 unit only does 1/8 of what a $99 GPU can do (caveat: I pulled that "1/8" figure from my arse) it'll be doing it on 1/60 of the power (based on GPU reviews where people have tried to compare power draw with GPU idle to power draw with GPU at 100% but everything else as idle as possible, which indicate modern GPUs pull between 120 and 150W (http://www.guru3d.com/articles_pages/radeon_hd_6850_6870_review,10.html http://www.guru3d.com/articles_pages/radeon_hd_6850_6870_rev... is the first such analysis Google found)). That potential computation-per-watt (or computation-per-energy-$ if the cost is more important than the energy supply+dissipation problem) of units like this could be very useful. Of course where power input and heat dissipation are not massive concerns, current GPUs still win on computation-per-device and computation-per-hardware-$.
- c0g 13y ago
  I think the interesting comparison will be between something like Tegra 5/Exynos 5 and this. Most scientific computing these days is CUDA, and getting something like cuBLAS onto this chip will be a struggle. The Exynos/Android combo has already lowered it's ability to interest me by not even accepting OpenCL as a first class citizen. EDIT: Hopefully NVidia will give me CUDAndroid, so I have fun.
Ecio78 13y ago
just guessing, maybe all the cases where you need parallel computing AND low power: computer vision, robotics (drones, self driving cars etc..), automation and so on?

by c0g 13y ago

dspillett 13y ago

By my understanding any situation where you need the computing power with the requirement for very low energy input and waste energy output. I can't think of any specific examples (perhaps an embedded system that needs to perform a pile of cryptographic operations?) but its ability to do a significant amount of processing on the 2W power footprint it is in a different class to current desktop CPUs and GPUs. Even if this $99 unit only does 1/8 of what a $99 GPU can do (caveat: I pulled that "1/8" figure from my arse) it'll be doing it on 1/60 of the power (based on GPU reviews where people have tried to compare power draw with GPU idle to power draw with GPU at 100% but everything else as idle as possible, which indicate modern GPUs pull between 120 and 150W (http://www.guru3d.com/articles_pages/radeon_hd_6850_6870_review,10.html http://www.guru3d.com/articles_pages/radeon_hd_6850_6870_rev... is the first such analysis Google found)). That potential computation-per-watt (or computation-per-energy-$ if the cost is more important than the energy supply+dissipation problem) of units like this could be very useful. Of course where power input and heat dissipation are not massive concerns, current GPUs still win on computation-per-device and computation-per-hardware-$.

c0g 13y ago
I think the interesting comparison will be between something like Tegra 5/Exynos 5 and this. Most scientific computing these days is CUDA, and getting something like cuBLAS onto this chip will be a struggle. The Exynos/Android combo has already lowered it's ability to interest me by not even accepting OpenCL as a first class citizen. EDIT: Hopefully NVidia will give me CUDAndroid, so I have fun.

Ecio78 13y ago

just guessing, maybe all the cases where you need parallel computing AND low power: computer vision, robotics (drones, self driving cars etc..), automation and so on?