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AMD has uProf [1], which while maybe not at full parity, definitely does performance counters (along with energy and power profiling, call graphs, and the other

by lhl 7y ago

AMD has uProf [1], which while maybe not at full parity, definitely does performance counters (along with energy and power profiling, call graphs, and the other basic things you'd expect). I use ECC on a (consumer) Ryzen chip/board and edac-util seems to give me the same information that it does on Intel - what's missing? One the CPU/platform side of things, my biggest annoyance is how far behind k10temp is (Zen2 support not in mainline until 5.4?), and how bad sensors support is in general on the boards (requiring reverse engineered non-mainline modules for my Zen/Zen2 workstations). While I agree that on the GPGPU-front Nvidia is still ahead, I'm very happy these days on my workstations with the state of AMDGPU in the mainline kernels and much prefer AMD GPUs for my workstations now vs Nvidia cards, which has led me to pay a bit of attention to ROCm - it looks like they are making very steady progress and TF and PyTorch support seems pretty good at this point (also, stuff like MIVisionX/OpenVX, CenterNet, BERT support seem to all be working relatively painlessly [3]), although it'd be useful if anyone has a resource that does continual benchmarking comparing on-prem/cloud perf of the various platforms, it'd be nice to get good $/perf and W/perf numbers over time. (I assume that anything running with Nvidia's tensor cores still completely blows away what AMD has to offer atm). [1] https://developer.amd.com/amd-uprof/ https://developer.amd.com/amd-uprof/ [2] https://github.com/RadeonOpenCompute/ROCm/commits/master/README.md https://github.com/RadeonOpenCompute/ROCm/commits/master/REA... [3] http://blog.gpueater.com/en/ http://blog.gpueater.com/en/

dragontamer 7y ago
uProf is decent, but the instruction-based sampling approach is the only way to get pinpoint accuracy on instructions. > I use ECC on a (consumer) Ryzen chip/board and edac-util seems to give me the same information that it does on Intel - what's missing? Event-based sampling on Intel is accurate to an instruction-level (while event-based sampling on AMD is less accurate. You're forced to use the more complicated IBS metrics if you want instruction-level accuracy of events). Intel also has branch-history data stored. Super useful for some developer tools, but I forget which tools those were... ----------- I think AMD uProf is certainly usable. And the price is good (free). But Intel vTune is just light-years ahead. AMD vs CUDA on the other hand is... closer than I think most people realize. CUDA has a bunch of libraries (Thrust, TensorFlow support, etc. etc.) which helps. But if you're doing high-performance coding, you'll likely have to write your own specialized data-structures. At least, that's the approach I'm doing with some GPU hobby code I'm writing. TensorFlow (due to Tensorcores) and BLAS are solidly NVidia advantages. But general purpose libraries (ex: Thrust) is more of a convenience. AMD's main disadvantage is documentation. But the tools are actually quite usable. AMD documents the lowest level well (the ISA), but their HIP / HCC / etc. etc. documents are lacking and difficult for beginners to follow. AMD should work on updating their beginner guides (their OpenCL guides) to their ROCm framework. Even if its ROCm OpenCL 2.0 stuff, its important to get beginners to use their platform. Or at least, update their beginner guides to reference GPUs that have come out within the past 5 years...

by lhl 7y ago

dragontamer 7y ago

uProf is decent, but the instruction-based sampling approach is the only way to get pinpoint accuracy on instructions. > I use ECC on a (consumer) Ryzen chip/board and edac-util seems to give me the same information that it does on Intel - what's missing? Event-based sampling on Intel is accurate to an instruction-level (while event-based sampling on AMD is less accurate. You're forced to use the more complicated IBS metrics if you want instruction-level accuracy of events). Intel also has branch-history data stored. Super useful for some developer tools, but I forget which tools those were... ----------- I think AMD uProf is certainly usable. And the price is good (free). But Intel vTune is just light-years ahead. AMD vs CUDA on the other hand is... closer than I think most people realize. CUDA has a bunch of libraries (Thrust, TensorFlow support, etc. etc.) which helps. But if you're doing high-performance coding, you'll likely have to write your own specialized data-structures. At least, that's the approach I'm doing with some GPU hobby code I'm writing. TensorFlow (due to Tensorcores) and BLAS are solidly NVidia advantages. But general purpose libraries (ex: Thrust) is more of a convenience. AMD's main disadvantage is documentation. But the tools are actually quite usable. AMD documents the lowest level well (the ISA), but their HIP / HCC / etc. etc. documents are lacking and difficult for beginners to follow. AMD should work on updating their beginner guides (their OpenCL guides) to their ROCm framework. Even if its ROCm OpenCL 2.0 stuff, its important to get beginners to use their platform. Or at least, update their beginner guides to reference GPUs that have come out within the past 5 years...