Hardware Sensitivity



Frameworks and GPU Specifications


Here we present our performance study for different type of GPUs. The following table shows the specifications of the GPUs we tested:

# of Multi-processors Core count Max Clock Rate (MHz) Memory Size (GB) LLC Size (MB) Memory Bus Type Memory BW (GB/s) Bus Interface
TitanXp 30 3840 1582 12 3 GDDR5X 547.6 PCIe 3.0
1080 Ti 30 3584 1582 11 2.75 GDDR5X 352.2 PCIe 3.0
P4000 14 1792 1480 8 2 GDDR5 243 PCIe 3.0
Titan V 80 5120 1455 12 4.5 HBM2 652.8 PCIe 3.0
V100-PCIe 80 5120 1370 32 6 HBM2 900 PCIe 3.0
V100-SXM2 80 5120 1455 16 6 HBM2 900 NVLink
P100[12GB] 56 3584 1303 12 4 HBM2 549 PCIe 3.0
P100[16GB] 56 3584 1480 16 4 HBM2 720 PCIe 3.0
2080 Ti 68 4352 1635 11 6 GDDR6 616 PCIe 3.0

Here are the framework and batch size details of all benchmarks that we used for our hardware sensitivity study.

ResNet-50 InceptionV3 Sockeye NMT Transformer A3C Faster RCNN
Framework MXNet v1.1.0 MXNet v1.1.0 MXNet v1.1.0 TensorFlow v1.8.0 TensorFlow v1.8.0 MXNet v1.1.0 MXNet v1.1.0
Batch Size 32 images 32 images 32 sentences 128 sentences 2048 sentences 32 snapshots 1 image + 128 ROIs