From 1328a2b7ca6209831aced3fd842b489cd1891d05 Mon Sep 17 00:00:00 2001 From: Xuan Gu <xuagu37@gmail.com> Date: Thu, 20 Oct 2022 15:14:59 +0200 Subject: [PATCH] Update README.md --- README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index cf98fef..253cfae 100644 --- a/README.md +++ b/README.md @@ -77,7 +77,7 @@ TF32 (TensorFloat32) mode is for accelerating FP32 convolutions and matrix multi AMP (Automatic Mixed Precision) offers significant computational speedup by performing operations in half-precision (FP16) format, while storing minimal information in single-precision (TF32) to retain as much information as possible in critical parts of the network. We run 100 iterations for each set of parameters. -- Observation 1: when batch_size is small (1, 2, 4, 8), throughput_amp ≈ throughput_tf32; +**Observation 1**: when batch_size is small (1, 2, 4, 8), throughput_amp ≈ throughput_tf32; when batch_size is large (16, 32, 64, 128), throughput_amp > throughput_tf32. <img src="https://github.com/xuagu37/Benchmark_nnU-Net_for_PyTorch/blob/main/figures/benchmark_throughput_batch_size.png" width="400"> @@ -90,11 +90,11 @@ when batch_size is large (16, 32, 64, 128), throughput_amp > throughput_tf32. - The expected throughput for dim = 2, nodes = 1, gpus = 8, batch_size = 128 would be 4700 ± 500 (TF32). - The expected throughput for dim = 2, nodes = 2, gpus = 16, batch_size = 128 would be 9250 ± 150 (TF32). -- Observation 3: Ideally, the improvement of throughput would be linear when batch_size increases. In practice, throughtput stays below the ideal curve when batch_size > 16. +**Observation 3**: Ideally, the improvement of throughput would be linear when batch_size increases. In practice, throughtput stays below the ideal curve when batch_size > 16. <img src="https://github.com/xuagu37/Benchmark_nnU-Net_for_PyTorch/blob/main/figures/benchmark_throughput_batch_size_ideal.png" width="400"> -- Observation 4: Ideally, the improvement of throughput would be linear when the number of GPUs increases. In practice, throughtput stays below the ideal curve when the number of gpus increases. +**Observation 4**: Ideally, the improvement of throughput would be linear when the number of GPUs increases. In practice, throughtput stays below the ideal curve when the number of gpus increases. <img src="https://github.com/xuagu37/Benchmark_nnU-Net_for_PyTorch/blob/main/figures/benchmark_throughput_gpus_ideal.png" width="400"> -- GitLab