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Welcome to the Geekbench CUDA Benchmark Chart. The data on this chart is calculated from Geekbench 5 results users have uploaded to the Geekbench Browser. To make sure the results accurately reflect the average performance of each GPU, the chart only includes GPUs with at least five unique results in the Geekbench Browser.

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Aug 03, 2019 · -cuda Track GPU events via CUDA-cupti Track GPU events via CUPTI (Also see env. variable TAU_CUPTI_API)-opencl Track GPU events via OpenCL-openacc Track GPU events via OpenACC(currently PGI only)-rocm Track ROCmevents via rocprofiler-ompt Track OpenMP events via OMPT interface-ebs Enable event-based sampling

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The README in the benchmarks/scripts/tf_cnn_benchmarks directory provides some example For comparison, the same command being run on a Tesla P100-PCIE-16GB (CUDA==9.2, cuDNN I have tried runnning benchmarks on my environment(Kernel 4.15, ROCm1.9.2, TF1.12 with RX 580).

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CUDA vs rocm. Question. Hi everyone, last time I checked (3 years ago) rocm was absolutely useless. Does anyone of you have a proper benchmark between consumer grade nvidia gpus with cuda and amd gpus using rocm?

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(Think of this as the equivalent of Nvidia-381 driver + CUDA some libraries kind of). https://rocm.github.io/dl.html (Honestly I still haven't tested the performance or tried to get it to work with more recent Mesa drivers yet. I will do that sometime. Add MiOpen to ROCM, and that is essentially CUDNN.

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Vega Frontier ($930) training benchmark only 10% behind Tesla V100 ($8000) in @TensorFlow. That's great news! Beginning of the end of the Deep Learning Hardware Limbo @Tim_Dettmers. But we need more time to take the benchmarks due to other priorities.

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Workaround for red_bug_51 failing on gfx908. For ROCm v3.3 and older releases, the clinfo path remains unchanged In this release, the following new Matrix Pruning functions are introduced. </p> <p>ROCm wurde im Hinblick auf Flexibilität und Kontrolle entwickelt und gibt der HPC-Community Zugang zu einer Reihe verschiedener Ressourcen. setting HSA_PATH environment variable). </p> <p>NOTE: The ...

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Nov 16, 2020 · The AMD Instinct MI100 uses the new AMD CDNA (Compute DNA) with all-new Matrix Core Technology to deliver a nearly 7x (FP16) performance boost for AI workloads vs AMD prior Gen. Scientific applications will benefit from MI100’s single-precision FP32 Matrix Core performance for a nearly 3.5x boost for HPC & AI workloads vs AMD prior gen ...
Less than a year ago, with its GP102 chip + 3584 CUDA Cores + 11GB of VRAM, the GTX 1080Ti was the apex GPU of last-gen Nvidia Pascal range (bar The RTX 2060 has roughly HALF the number of CUDA cores of the 1080Ti (1920 vs. 3584). Its memory bandwith is about 70% of the 1080Ti (336 vs...
Will AMD GPUs + ROCm ever catch up with NVIDIA GPUs + CUDA? When is it better to use the I will discuss CPUs vs GPUs, Tensor Cores, memory bandwidth, and the memory hierarchy of GPUs Debiased benchmark data suggests that the Tesla A100 compared to the V100 is 1.70x faster for...
In order to reliably perform complex tasks on the GPU, stdgpu offers flexible interfaces that can be used in both agnostic code, e.g. via the algorithms provided by thrust, as well as in native code, e.g. in custom CUDA kernels.
NVIDIA NVSwitch builds on the advanced communication capability of NVLink to solve this problem. It takes deep learning performance to the next level with a GPU fabric that enables more GPUs in a single server and full-bandwidth connectivity between them. Each GPU has 12 NVLinks per NVSwitch to enable high-speed, all-to-all communication.

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Because once you port your CUDA code using HIP to any other paltform, and you are good to go with it - everywhere. AI and ML are very well supported on AMD platform, and Vega 64 is offering 95% of performance of GV100 chip, but at the same time costs 1/6th the price. Buy 30 GPUs at once and you get the difference(900 000$ vs 150 000$).
What is the parallax (POM) GPU benchmark? A measure of a GPUs ability to render detailed surfaces with shadows via POM... more. A measure of a GPUs ability to compute and render an NBody particle system... more.With ROCm, you ideally write and maintain your code using the open HIP programming model, thus allowing portable code (for now, only between AMD and Nvidia platforms, though). As far as I understand, HIP copies the CUDA programming model as closely as possible, for familiarity and ease of porting for CUDA users.