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Does anyone know why they are using Xeon processors instead of the AMD Threadripper? Is it the support for ECC memory? If so, why is it that so important? Exam

Does anyone know why they are using Xeon processors instead of the AMD Threadripper? Is it the support for ECC memory? If so, why is it that so important? Example: https://www.pugetsystems.com/nav/peak/tower_single/customize.php https://www.pugetsystems.com/nav/peak/tower_single/customize...

zamadatix 8y ago
The Xeon they are using is 14 cores in a single NUMA node so maybe that's it since a 16 core Threadripper is 2 separate nodes in one die. I'm pretty sure Threadripper supports ECC: "With the most memory channels you can get on desktop, the Ryzen™ Threadripper™ processor can support Workstation Standard DDR4 ECC (Error Correcting Mode) Memory to keep you tight, tuned and perfectly in sync." from https://www.amd.com/en/products/ryzen-threadripper https://www.amd.com/en/products/ryzen-threadripper
- simcop2387 8y ago
  I can definitely confirm the ECC support on TR cpus. I've got one running right now and have had it report errors and corrections when trying to overclock things (I don't know what I'm doing with that so it's nice to have it warn me that I'm at the limit).
  - mychael 8y ago
    How does that warning surface? From the underlying operating system?
jamesblonde 8y ago
Because it's a single-root pci complex. See here: https://www.servethehome.com/how-intel-skylake-sp-changes-impacted-single-root-pcie-deep-learning-servers/ https://www.servethehome.com/how-intel-skylake-sp-changes-im...
- scientist 8y ago
  Could you please explain your comment? The link that you have provided explains that with the new Intel Xeon Scalable generation it is difficult to implement single-root PCI complex on typically available motherboards, while according to [1] "the new Intel® Xeon® W processors are based on the Intel® Xeon® Scalable processor microarchitecture". Therefore, Intel Xeon W would have the same problems for supporting single-root PCI complex as the Xeon Scalable mentioned in the link you have provided. [1] https://www.intel.com/content/www/us/en/processors/xeon/xeon-scalable-w-workstation-brief.html https://www.intel.com/content/www/us/en/processors/xeon/xeon...
celrod 8y ago
The Xeon-W 2175 has avx-512. Threadrippers cannot compete in numbering work relative to price point on well optimized code. sgemm on 5000x5000 matrices takes about 600ms on a Threadrippers 1950x, but only around 150ms on the comparatively priced i9 7900x. Vector libraries for special functions, eg Intel VML or SLEEF also provide a similar performance advantage there. If you're mostly crunching numbers, and either compiling the code you run with avx512 enabled (eg, -mprefer-vector-width=512 on gcc, otherwise it's disabled) or using explicitly vectorized libraries, you will see dramatically better performance from avx512, regardless of any thermal throttling. Number crunching is what it's made for. Granted, you should be offloading most of those computations to the GPU, which will be many times faster. But I'd you're in the business of ML or statistics, I'd still way that more heavily than the difference in how long it takes them to compile code.
- AnthonyMouse 8y ago
  > Granted, you should be offloading most of those computations to the GPU, which will be many times faster. But I'd you're in the business of ML or statistics, I'd still way that more heavily than the difference in how long it takes them to compile code. I don't follow the logic. It sounds like you're saying that if you care about that specific type of highly vectorized computation being fast what you really want is a GPU rather than any particular CPU. So how should that have a major influence on which CPU you choose? Particularly when the CPU which is slower at that is faster at many other things that aren't suitable for a GPU.
  - celrod 8y ago
    I'm saying there is a reason to favor a CPU with avx512. The reason may not apply to you / your work flow. If your number crunching is just neural networks on your GPU, then the CPU doesn't matter. But there's probably a lot of overlap between the folks who train neural networks, and those who may do linear algebra, MCMC, or traditional stats that are much better suited to the CPU. That is, conditioning on person A being someone who trains NNs, there is a higher probability that they're someone who would be interested in CPU intensive tasks that benefit from vectorization. If that isn't you, don't factor it into your decision. I do most of my number crunching on the CPU, so my choice is clear. The reviews of avx512 are generally poor (disable it so you don't get thermal throttling!), while the Threadrippers receive a lot of praise. But within it's own niche (linear algebra, many iterative algorithms), the widest vectors are king.
Erlich_Bachman 8y ago
AMDs usually and historically supports ECC memory. In fact, in some ways it supports it more than Intel: Intel disables ECC support (for no other real reason than marketing efforts and because they can charge more money that way) on non-Xeon processors, while AMD keeps it enabled on most models, even desktop-oriented ones.

zamadatix 8y ago

The Xeon they are using is 14 cores in a single NUMA node so maybe that's it since a 16 core Threadripper is 2 separate nodes in one die. I'm pretty sure Threadripper supports ECC: "With the most memory channels you can get on desktop, the Ryzen™ Threadripper™ processor can support Workstation Standard DDR4 ECC (Error Correcting Mode) Memory to keep you tight, tuned and perfectly in sync." from https://www.amd.com/en/products/ryzen-threadripper https://www.amd.com/en/products/ryzen-threadripper

simcop2387 8y ago
I can definitely confirm the ECC support on TR cpus. I've got one running right now and have had it report errors and corrections when trying to overclock things (I don't know what I'm doing with that so it's nice to have it warn me that I'm at the limit).
- mychael 8y ago
  How does that warning surface? From the underlying operating system?

jamesblonde 8y ago

Because it's a single-root pci complex. See here: https://www.servethehome.com/how-intel-skylake-sp-changes-impacted-single-root-pcie-deep-learning-servers/ https://www.servethehome.com/how-intel-skylake-sp-changes-im...

scientist 8y ago
Could you please explain your comment? The link that you have provided explains that with the new Intel Xeon Scalable generation it is difficult to implement single-root PCI complex on typically available motherboards, while according to [1] "the new Intel® Xeon® W processors are based on the Intel® Xeon® Scalable processor microarchitecture". Therefore, Intel Xeon W would have the same problems for supporting single-root PCI complex as the Xeon Scalable mentioned in the link you have provided. [1] https://www.intel.com/content/www/us/en/processors/xeon/xeon-scalable-w-workstation-brief.html https://www.intel.com/content/www/us/en/processors/xeon/xeon...

celrod 8y ago

The Xeon-W 2175 has avx-512. Threadrippers cannot compete in numbering work relative to price point on well optimized code. sgemm on 5000x5000 matrices takes about 600ms on a Threadrippers 1950x, but only around 150ms on the comparatively priced i9 7900x. Vector libraries for special functions, eg Intel VML or SLEEF also provide a similar performance advantage there. If you're mostly crunching numbers, and either compiling the code you run with avx512 enabled (eg, -mprefer-vector-width=512 on gcc, otherwise it's disabled) or using explicitly vectorized libraries, you will see dramatically better performance from avx512, regardless of any thermal throttling. Number crunching is what it's made for. Granted, you should be offloading most of those computations to the GPU, which will be many times faster. But I'd you're in the business of ML or statistics, I'd still way that more heavily than the difference in how long it takes them to compile code.

AnthonyMouse 8y ago
> Granted, you should be offloading most of those computations to the GPU, which will be many times faster. But I'd you're in the business of ML or statistics, I'd still way that more heavily than the difference in how long it takes them to compile code. I don't follow the logic. It sounds like you're saying that if you care about that specific type of highly vectorized computation being fast what you really want is a GPU rather than any particular CPU. So how should that have a major influence on which CPU you choose? Particularly when the CPU which is slower at that is faster at many other things that aren't suitable for a GPU.
- celrod 8y ago
  I'm saying there is a reason to favor a CPU with avx512. The reason may not apply to you / your work flow. If your number crunching is just neural networks on your GPU, then the CPU doesn't matter. But there's probably a lot of overlap between the folks who train neural networks, and those who may do linear algebra, MCMC, or traditional stats that are much better suited to the CPU. That is, conditioning on person A being someone who trains NNs, there is a higher probability that they're someone who would be interested in CPU intensive tasks that benefit from vectorization. If that isn't you, don't factor it into your decision. I do most of my number crunching on the CPU, so my choice is clear. The reviews of avx512 are generally poor (disable it so you don't get thermal throttling!), while the Threadrippers receive a lot of praise. But within it's own niche (linear algebra, many iterative algorithms), the widest vectors are king.

Erlich_Bachman 8y ago

AMDs usually and historically supports ECC memory. In fact, in some ways it supports it more than Intel: Intel disables ECC support (for no other real reason than marketing efforts and because they can charge more money that way) on non-Xeon processors, while AMD keeps it enabled on most models, even desktop-oriented ones.