Float 16bit / Mixed Precision LearningĬoncerning inference jobs, a lower floating point precision and even lower 8 or 4 bit integer resolution is granted and used to improve performance. For most training situation float 16bit precision can also be applied for training tasks with neglectable loss in training accuracy and can speed-up training jobs dramatically. Applying float 16bit precision is not that trivial as the model has to be adjusted to use it. As not all calculation steps should be done with a lower bit precision, the mixing of different bit resolutions for calculation is referred as " mixed precision". The full potential of mixed precision learning will be better explored with Tensor Flow 2.X and will probably be the development trend for improving deep learning framework performance. #Fp64 of gtx 1080 ti vs classic gtx titan full We provide benchmarks for both float 32bit and 16bit precision as a reference to demonstrate the potential. The visual recognition ResNet50 model in version 1.0 is used for our benchmark. As the classic deep learning network with its complex 50 layer architecture with different convolutional and residual layers, it is still a good network for comparing achievable deep learning performance. As it is used in many benchmarks, a close to optimal implementation is available, driving the GPU to maximum performance and showing where the performance limits of the devices are. We used our AIME A4000 server for testing. It is an elaborated environment to run high performance multiple GPUs by providing optimal cooling and the availability to run each GPU in a PCIe 4.0 x16 slot directly connected to the CPU. The NVIDIA Ampere generation benefits from the PCIe 4.0 capability, it doubles the data transfer rates to 31.5 GB/s to the CPU and between the GPUs. The Python scripts used for the benchmark are available on Github at: Tensorflow 1.x Benchmark Single GPU Performance The technical specs to reproduce our benchmarks: The connectivity has a measurable influence to the deep learning performance, especially in multi GPU configurations.Īlso the AIME A4000 provides sophisticated cooling which is necessary to achieve and hold maximum performance. The results of our measurements is the average image per second that could be trained while running for 100 batches at the specified batch size. When training with float 16bit precision the compute accelerators A100 and V100 increase their lead. #Fp64 of gtx 1080 ti vs classic gtx titan fullīut also the RTX 3090 can more than double its performance in comparison to float 32 bit calculations.#Fp64 of gtx 1080 ti vs classic gtx titan 32 bit.#Fp64 of gtx 1080 ti vs classic gtx titan how to.
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