4/30/2023 0 Comments Gflops gpugeeks fp64All benchmarks were run on bare-metal without a container. The ResNet-50 TensorFlow implementation from Google’s submission was used, and all other models’ implementations from NVIDIA’s submission were used. The summary of MLPerf benchmarks used for this evaluation is shown in Table 2. The initial released version is v0.5 and it covers model implementations in different machine learning domains including image classification, object detection and segmentation, machine translation and reinforcement learning. MLPerf is a benchmarking tool that was assembled by a diverse group from academia and industry including Google, Baidu, Intel, AMD, Harvard, and Stanford etc., to measure the speed and performance of machine learning software and hardware. MLPerf was chosen to evaluate the performance of T4 in deep learning training. The specification differences of T4 and V100-PCIe GPU are listed in Table 1. T4 is the GPU that uses NVIDIA’s latest Turing architecture. The system features Intel Skylake processors, up to 24 DIMMs, and up to 3 double width V100-PCIe or 4 single width T4 GPUs in x16 PCIe 3.0 slots. The Dell EMC PowerEdge R740 is a 2-socket, 2U rack server. MLPerf performance on T4 will also be compared to V100-PCIe on the same server with the same software. This blog will quantify the deep learning training performance of T4 GPUs on Dell EMC PowerEdge R740 server with MLPerf benchmark suite. It was designed for High-Performance Computing (HPC), deep learning training and inference, machine learning, data analytics, and graphics. Turing architecture is NVIDIA’s latest GPU architecture after Volta architecture and the new T4 is based on Turing architecture.
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