The surprising variety of simulating fluids - a vivid exploration.Updated timeline for the new integrated view for Gmail.Did I make it harder to sell your crappy, used crypto mining graphics card? Good.New Firefox privacy feature strips URLs of tracking parameters.Rust programming language 1.62.0 released.I compiled on a single table the values I found from various articles and reviews over the web. Use the same num_iterations in benchmarking and reporting.Here is the GFLOPS comparative table of recent AMD Radeon and NVIDIA GeForce GPUs in FP32 (single precision floating point) and FP64 (double precision floating point).Check the repo directory for folder -.logs (generated by benchmark.sh).Input a proper gpu_index (default 0) and num_iterations (default 10).We'd love it if you shared the results with us by emailing or tweeting Step #1: Clone Benchmark Repository git clone -recursive CPU: Xeon Gold 6148 / RAM: 256 GB DDR4 2400 MHz ECC V100 Benchmarks: Lambda Hyperplane - Tesla V100 Server.Multi-GPU training: Lambda Blade - Deep Learning GPU Server.Single-GPU training: Lambda Quad - Deep Learning GPU Workstation.Multi-GPU training was performed using model-level parallelism.We used synthetic data, as opposed to real data, to minimize non-GPU related bottlenecks.For example, on ResNet-50, the V100 used a batch size of 192 the RTX 2080 Ti use a batch size of 64. For each GPU / neural network combination, we used the largest batch size that fit into memory.For each model we ran 10 training experiments and measured # of images processed per second we then averaged the results of the 10 experiments.Lowering precision to FP16 may interfere with convergence. This gives an average speed-up of +71.6%.Ĭaveat emptor: If you're new to machine learning or simply testing code, we recommend using FP32. Compared with FP32, FP16 training on the Titan V is.Īs measured by the # of images processed per second during training. Using eight Tesla V100s is 9.68 / 5.18 = 1.87x faster than using eight Titan VsįP16 can reduce training times and enable larger batch sizes/models without significantly impacting model accuracy.Using eight Tesla V100s will be 9.68x faster than using a single Titan V.Using eight Titan Vs will be 5.18x faster than using a single Titan V.The chart below provides guidance as to how each GPU scales during multi-GPU training of neural networks in FP32. Titan V - FP16 TensorFlow Performance (1 GPU)įor FP16 training of neural networks, the NVIDIA Titan V is.Īs measured by the # images processed per second during training.įP32 Multi-GPU Scaling Performance (1, 2, 4, 8 GPUs)įor each GPU type (Titan V, RTX 2080 Ti, RTX 2080, etc.) we measured performance while training with 1, 2, 4, and 8 GPUs on each neural networks and then averaged the results. Titan V - FP32 TensorFlow Performance (1 GPU)įor FP32 training of neural networks, the NVIDIA Titan V is.Īs measured by the # images processed per second during training. Tensor Cores were utilized on all GPUs that have them.V100 Benchmarks were run on Lambda's SXM3 Tesla V100 Server.Multi-GPU benchmarks were run on the Lambda's PCIe GPU Server.Single-GPU benchmarks were run on the Lambda's deep learning workstation.We measure the # of images processed per second while training each network. We use the Titan V to train ResNet-50, ResNet-152, Inception v3, Inception v4, VGG-16, AlexNet, and SSD300. In this post, Lambda Labs benchmarks the Titan V's Deep Learning / Machine Learning performance and compares it to other commonly used GPUs.
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