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The article mentions that to write for Intels Xeon Phis, its x86. I imagine this is mostly accurate but is creating writing for this as simple as creating anoth

The article mentions that to write for Intels Xeon Phis, its x86. I imagine this is mostly accurate but is creating writing for this as simple as creating another thread? That seems naive Looked into it, [0] looks like you add some compiler instructures before code you want to outsource to the coprocessor. It doesnt look extreme, mostly naming which variables need to be sent and to where. Ive never worked with cuda so I dont know if its a pain or not. Looking into it, it seems instead of x86 code it is cuda prefixed functions that will offload. Granted, there are speed advantages to having direct accessbto memory, I dont think Api is the biggest selling point. Im quite interested in seeing if nvidia thinks this is a threat and how they will respond. New systems may stay with nvidia just because the experience and failures have been there. Though if the speed gains are even 25% I would bet there would be a migration [0] https://software.intel.com/sites/default/files/managed/ee/4e/intel-xeon-phi-coprocessor-quick-start-developers-guide.pdf https://software.intel.com/sites/default/files/managed/ee/4e...

deckar01 10y ago
> The basis of the Intel MIC architecture is to leverage x86 legacy by creating a x86-compatible multiprocessor architecture that can utilize existing parallelization software tools. Programming tools include OpenMP, OpenCL, Cilk/Cilk Plus and specialised versions of Intel's Fortran, C++ and math libraries. https://en.m.wikipedia.org/wiki/Xeon_Phi https://en.m.wikipedia.org/wiki/Xeon_Phi It's not threads, it's driver and compiler support for existing multiprocessing frameworks.
- frozenport 10y ago
  Actually, there are several modes of operation. In one mode, it acts like a 60 core machine running your favorite x86 Linux applications, in another mode you can run OCL kernels on it. The former, appears to give poor performance, see: https://cug.org/proceedings/cug2015_proceedings/includes/files/tut105.pdf https://cug.org/proceedings/cug2015_proceedings/includes/fil...
m_mueller 10y ago
Here is the main problem IMO: To get reasonable performance (i.e. comparable to GPU), you need to make use of vector instructions as well. And it's still not easier to vectorize your x86 code rather than to just port it to CUDA. With CUDA you get two parallelisms done in one programming paradigm: Both vector and multiprocessor parallelism just gets mapped to the kernel scatter initialisation, and the kernel itself is just written as a scalar operations plus some index calculations. In terms of readability / ease of use it's [naive x86] > [CUDA] > [performance portable parallelized + vectorized x86]. An if you don't have the last version of x86, which almost noone has, then Xeon Phi will be just as much portation work as GPU, if not more (because Nvidia has a considerable lead in tooling support and has actually even gained more lead since Intel's Phi came out because Intel is just not very agile).
- gcp 10y ago
  And it's still not easier to vectorize your x86 code rather than to just port it to CUDA. This a million times! The SIMT programming model that the GPUs (except Intel's) support so well is way easier to program and parallelize for than the explicit SIMD that x86 requires. PS. Are you the person that wrote gogui?
  - m_mueller 10y ago
    Nope, but I'm writing this: https://github.com/muellermichel/Hybrid-Fortran https://github.com/muellermichel/Hybrid-Fortran

deckar01 10y ago

> The basis of the Intel MIC architecture is to leverage x86 legacy by creating a x86-compatible multiprocessor architecture that can utilize existing parallelization software tools. Programming tools include OpenMP, OpenCL, Cilk/Cilk Plus and specialised versions of Intel's Fortran, C++ and math libraries. https://en.m.wikipedia.org/wiki/Xeon_Phi https://en.m.wikipedia.org/wiki/Xeon_Phi It's not threads, it's driver and compiler support for existing multiprocessing frameworks.

frozenport 10y ago
Actually, there are several modes of operation. In one mode, it acts like a 60 core machine running your favorite x86 Linux applications, in another mode you can run OCL kernels on it. The former, appears to give poor performance, see: https://cug.org/proceedings/cug2015_proceedings/includes/files/tut105.pdf https://cug.org/proceedings/cug2015_proceedings/includes/fil...

m_mueller 10y ago

Here is the main problem IMO: To get reasonable performance (i.e. comparable to GPU), you need to make use of vector instructions as well. And it's still not easier to vectorize your x86 code rather than to just port it to CUDA. With CUDA you get two parallelisms done in one programming paradigm: Both vector and multiprocessor parallelism just gets mapped to the kernel scatter initialisation, and the kernel itself is just written as a scalar operations plus some index calculations. In terms of readability / ease of use it's [naive x86] > [CUDA] > [performance portable parallelized + vectorized x86]. An if you don't have the last version of x86, which almost noone has, then Xeon Phi will be just as much portation work as GPU, if not more (because Nvidia has a considerable lead in tooling support and has actually even gained more lead since Intel's Phi came out because Intel is just not very agile).

gcp 10y ago
And it's still not easier to vectorize your x86 code rather than to just port it to CUDA. This a million times! The SIMT programming model that the GPUs (except Intel's) support so well is way easier to program and parallelize for than the explicit SIMD that x86 requires. PS. Are you the person that wrote gogui?
- m_mueller 10y ago
  Nope, but I'm writing this: https://github.com/muellermichel/Hybrid-Fortran https://github.com/muellermichel/Hybrid-Fortran