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I've been curious about the Xeon Phi. It seems like they would fit the same purpose as the GPU (especially with the Christmas sale last year that you can get a

by NhanH 12y ago

I've been curious about the Xeon Phi. It seems like they would fit the same purpose as the GPU (especially with the Christmas sale last year that you can get a 31s1p for ~$120-170), it has more memory and similar FLOPS/price. Why are they not as competitive as GPU?

ogrisel 12y ago
First that would require adapting the deep learning libraries (e.g. theano, torch and Caffe) to use it efficiently. Those libraries have optimal CUDA kernels for efficient GEMM, spatial pooling and FFT convolution operations that would need to be rewritten to leverage the capacity of a Xeon Phi chip.
varelse 12y ago
Bluntly, it's a great processor on paper, but for the most part, its performance falls severely short of its paper specs. While I was really looking forward to Intel shipping a competitive processor to NVIDIA's GPUs, reality disappointed. Intel put 2 engineers on porting a piece of code I wrote for a year. That code is roughly 20x faster on a Maxwell class GPU than on a Haswell class CPU (all cores firing). 2 man years later, instead of the Xeon Phi performing on par with a C1060/GTX 285 as it initially did, it managed to accelerate the CPU code by ~10%. In that time, NVIDIA shipped GK104, GK110 and then GM204 accelerating my code by a factor of 2 relative to GK104. When I tried to help one of the Intel engineers catch up to at least Fermi class GPUs, he got mad at my algorithmic choices and stormed out of the room. Intel IMO will resort to dumping these things at rock bottom prices into data centers in order to spoof just enough low information government wage HPC administrators into believing these things are good for anything more than playing Jenga with them. And that will keep NVIDIA from achieving total HPC dominance in the short term. No idea what the long-term will bring. Intel ought to be able to deliver a compelling part one day, but I suspect that will be a CPU with wider SIMD and more cores.

by NhanH 12y ago

ogrisel 12y ago

First that would require adapting the deep learning libraries (e.g. theano, torch and Caffe) to use it efficiently. Those libraries have optimal CUDA kernels for efficient GEMM, spatial pooling and FFT convolution operations that would need to be rewritten to leverage the capacity of a Xeon Phi chip.

varelse 12y ago

Bluntly, it's a great processor on paper, but for the most part, its performance falls severely short of its paper specs. While I was really looking forward to Intel shipping a competitive processor to NVIDIA's GPUs, reality disappointed. Intel put 2 engineers on porting a piece of code I wrote for a year. That code is roughly 20x faster on a Maxwell class GPU than on a Haswell class CPU (all cores firing). 2 man years later, instead of the Xeon Phi performing on par with a C1060/GTX 285 as it initially did, it managed to accelerate the CPU code by ~10%. In that time, NVIDIA shipped GK104, GK110 and then GM204 accelerating my code by a factor of 2 relative to GK104. When I tried to help one of the Intel engineers catch up to at least Fermi class GPUs, he got mad at my algorithmic choices and stormed out of the room. Intel IMO will resort to dumping these things at rock bottom prices into data centers in order to spoof just enough low information government wage HPC administrators into believing these things are good for anything more than playing Jenga with them. And that will keep NVIDIA from achieving total HPC dominance in the short term. No idea what the long-term will bring. Intel ought to be able to deliver a compelling part one day, but I suspect that will be a CPU with wider SIMD and more cores.