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I love AMD's innovation in this space, but for high-end gaming Nvidia is still destroying them in raw performance. RTX and G-Sync are definitely stupid, though

I love AMD's innovation in this space, but for high-end gaming Nvidia is still destroying them in raw performance. RTX and G-Sync are definitely stupid, though they are adding limited Freesync compatibility to recent cards now. If AMD made something that'd beat my 1080 Ti for a reasonable price, I'd definitely buy it. I certainly don't like Nvidia's Linux drivers, but the majority of my non-IGPU needs are Windows-based, so it's not as much of an issue. If I exclusively used Linux on my high-end PCs, I'd likely be more willing to lose some raw performance to go with AMD.

dragontamer 8y ago
Well, the Radeon VII looks like it is around the 1080 Ti / 2080 for $699. I think the main issue with AMD is that their compute drivers are clearly behind NVidia's. However, their ROCm development is now on Github, so we can publicly see releases and various development actions. AMD has been active on Github, so the drivers are clearly improving. But I think it is surprising to see just how far behind they are. ROCm is rewriting OpenCL from scratch, HIP / HCC / etc. etc. is built on top of C++ AMP but otherwise seems to be built from scratch as well. As such, there are still major issues like "ROCm / OpenCL doesn't work with Blender 2.79 yet". And since ROCm / OpenCL is a different compiler, it has different performance characteristics compared to AMDGPU-PRO (the old OpenCL compiler). So code that worked quickly on AMDGPU-PRO (ex: LuxRender) may work slowly on ROCm / OpenCL (or worst case: not at all, due to compiler errors or whatnot). EDIT: And the documentation... NVidia offers extremely good documentation. Not only a complete CUDA guide, but a "performance" guide, documented latencies on various instructions (not like Agner Fog level, but useful to understand which instructions are faster than others), etc. etc. AMD used to have an "OpenCL Optimization Guide" with similar information, but it hasn't been updated since the 7970. EDIT: AMD's Vega ISA documentation is lovely though. But its a bit too low level, and while it gives a great idea of how the GPU executes at an assembly level, it doesn't really have much about how OpenCL relates to it, or optimization tips for that matter. There are certainly nifty features, like DPP, or ds_permute instructions which probably can be used in a Bitonic Sort or something, but there's almost no "OpenCL-level" guide to how to use those instructions. (aside from: https://gpuopen.com/amd-gcn-assembly-cross-lane-operations/ https://gpuopen.com/amd-gcn-assembly-cross-lane-operations/. That's basically the best you've got) That's just the reality of the situation right now for anyone looking into AMD Compute. I'm hopeful that the situation will change as AMD works on fixing bugs and developing (there have been a LOT of development items pushed to their Github repo in the past year). But there's just so much software to be written to have AMD catch up to NVidia. Not just code, but also documentation of their GPUs.
- Rychard 8y ago
  I bought my 1080 TI just over a year ago (December 2017) from Newegg for $750. (Newegg item N82E16814126186) I'm glad AMD is finally catching up, but a savings of only $51 an entire year later doesn't exactly sound like a particularly great deal to me.
  - dragontamer 8y ago
    Welcome to the end of Moore's Law. 7nm is as expensive as 14nm was. Sure, you gained double the density, but it costs twice as much to make. So you only get improved performance / watt. Cost per transistor stayed equal in this 7nm generation. NVidia's 2080 (roughly equivalent to the 1080 Ti) is also $699 to $799, depending on which model you get. Its the nature of how the process nodes work now. ----------- Rumor is that the lower-end of the market will get price/performance upgrades, as maybe small-7nm chips will have enough yield to actually give cost-savings. But that's a bit of "hopes and dreams" leaking in, as opposed to any hard data. For now, it is clear that 300mm^2 7nm chips (like the Radeon VII) are going to be costly. Probably due to low yields, but its hard to know for sure. Note that Zen2 and Apple chips are all at around 100mm^2 or so (which seems to indicate that yields are fine for small chips... but even then, Apple's Phones definitely increased in price as they hit 7nm)
- keldaris 8y ago
  From my perspective (computational physics, not machine learning) the situation with GPU compute is very simple. If you are fine writing everything from scratch and won't need the CUDA ecosystem (which is really all there is for good sparse matrix, linear algebra, etc. support), write OpenCL 1.2 (or even GLSL if it's a visualization-heavy code with relatively simple compute) and buy whatever gets you the best compute/$ at that time. Otherwise - and this probably includes most people in this space - you have no choice but to keep using CUDA. There is just no meaningful compute ecosystem for AMD GPUs, sadly. I'm still very much looking forward to the Radeon VII due to the memory bandwidth, since I'm currently working on bandwidth-constrained CFD simulations. But that's a specific usecase and I write most things from scratch anyway.
  - 8y ago

dragontamer 8y ago

Well, the Radeon VII looks like it is around the 1080 Ti / 2080 for $699. I think the main issue with AMD is that their compute drivers are clearly behind NVidia's. However, their ROCm development is now on Github, so we can publicly see releases and various development actions. AMD has been active on Github, so the drivers are clearly improving. But I think it is surprising to see just how far behind they are. ROCm is rewriting OpenCL from scratch, HIP / HCC / etc. etc. is built on top of C++ AMP but otherwise seems to be built from scratch as well. As such, there are still major issues like "ROCm / OpenCL doesn't work with Blender 2.79 yet". And since ROCm / OpenCL is a different compiler, it has different performance characteristics compared to AMDGPU-PRO (the old OpenCL compiler). So code that worked quickly on AMDGPU-PRO (ex: LuxRender) may work slowly on ROCm / OpenCL (or worst case: not at all, due to compiler errors or whatnot). EDIT: And the documentation... NVidia offers extremely good documentation. Not only a complete CUDA guide, but a "performance" guide, documented latencies on various instructions (not like Agner Fog level, but useful to understand which instructions are faster than others), etc. etc. AMD used to have an "OpenCL Optimization Guide" with similar information, but it hasn't been updated since the 7970. EDIT: AMD's Vega ISA documentation is lovely though. But its a bit too low level, and while it gives a great idea of how the GPU executes at an assembly level, it doesn't really have much about how OpenCL relates to it, or optimization tips for that matter. There are certainly nifty features, like DPP, or ds_permute instructions which probably can be used in a Bitonic Sort or something, but there's almost no "OpenCL-level" guide to how to use those instructions. (aside from: https://gpuopen.com/amd-gcn-assembly-cross-lane-operations/ https://gpuopen.com/amd-gcn-assembly-cross-lane-operations/. That's basically the best you've got) That's just the reality of the situation right now for anyone looking into AMD Compute. I'm hopeful that the situation will change as AMD works on fixing bugs and developing (there have been a LOT of development items pushed to their Github repo in the past year). But there's just so much software to be written to have AMD catch up to NVidia. Not just code, but also documentation of their GPUs.

Rychard 8y ago
I bought my 1080 TI just over a year ago (December 2017) from Newegg for $750. (Newegg item N82E16814126186) I'm glad AMD is finally catching up, but a savings of only $51 an entire year later doesn't exactly sound like a particularly great deal to me.
- dragontamer 8y ago
  Welcome to the end of Moore's Law. 7nm is as expensive as 14nm was. Sure, you gained double the density, but it costs twice as much to make. So you only get improved performance / watt. Cost per transistor stayed equal in this 7nm generation. NVidia's 2080 (roughly equivalent to the 1080 Ti) is also $699 to $799, depending on which model you get. Its the nature of how the process nodes work now. ----------- Rumor is that the lower-end of the market will get price/performance upgrades, as maybe small-7nm chips will have enough yield to actually give cost-savings. But that's a bit of "hopes and dreams" leaking in, as opposed to any hard data. For now, it is clear that 300mm^2 7nm chips (like the Radeon VII) are going to be costly. Probably due to low yields, but its hard to know for sure. Note that Zen2 and Apple chips are all at around 100mm^2 or so (which seems to indicate that yields are fine for small chips... but even then, Apple's Phones definitely increased in price as they hit 7nm)
keldaris 8y ago
From my perspective (computational physics, not machine learning) the situation with GPU compute is very simple. If you are fine writing everything from scratch and won't need the CUDA ecosystem (which is really all there is for good sparse matrix, linear algebra, etc. support), write OpenCL 1.2 (or even GLSL if it's a visualization-heavy code with relatively simple compute) and buy whatever gets you the best compute/$ at that time. Otherwise - and this probably includes most people in this space - you have no choice but to keep using CUDA. There is just no meaningful compute ecosystem for AMD GPUs, sadly. I'm still very much looking forward to the Radeon VII due to the memory bandwidth, since I'm currently working on bandwidth-constrained CFD simulations. But that's a specific usecase and I write most things from scratch anyway.
- 8y ago