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Search results with tag "Tesla v100"

GPU Computing Guide

GPU Computing Guide

updates.cst.com

Tesla V100-PCIE-32GB 32 900 14 7 Tesla V100-SXM2-16GB 16 900 15 7.5 Tesla V100-PCIE-16GB 16 900 14 7 Tesla P100-SXM2 16 732 10.6 5.3 Tesla P100-PCIE-16GB 16 732 9.3 4.7 Tesla P100 16GB 16 732 9.3 4.7. 3DS.COM/SIMULIA c Dassault Systèmes GPU …

  Tesla, V001, Tesla v100

NVIDIA TESLA V100 GPU ARCHITECTURE

NVIDIA TESLA V100 GPU ARCHITECTURE

images.nvidia.com

The NVIDIA Tesla V100 accelerator is the world’s highest performing parallel processor, designed to power the most computationally intensive HPC, AI, and graphics workloads. The GV100 GPU includes 21.1 billion transistors with a die size of 815 mm 2 .

  Tesla, V001, Tesla v100

GPU Computing Guide

GPU Computing Guide

updates.cst.com

Hardware Type NVIDIA Tesla V100 SXM 16GB NVIDIA Tesla V100 PCIe 16GB (for Servers) Min. CST version required 2018 SP 1 2018 SP 1 Number of GPUs 1 1 Max. Problem Size (Transient Solver) approx. 160 million mesh cells approx. 160 million mesh cells Form Factor Chip Passive Cooling Dual-Slot PCI-Express Passive Cooling Memory 16 GB CoWoS HBM2 16 ...

  Guide, Computing, Tesla, V001, Tesla v100, Gpu computing guide

NVIDIA TESLA V100 GPU ACCELERATOR

NVIDIA TESLA V100 GPU ACCELERATOR

images.nvidia.com

scientists, researchers, and engineers to tackle challenges that were once thought impossible. SPECIFICATIONS Tesla V100 PCle SXM2 GPU Architecture NVIDIA Volta NVIDIA Tensor Cores 640 NVIDIA CUDA® Cores 5,120 Double-Precision Performance 7 TFLOPS 7.8 TFLOPS Single-Precision Performance 14 TFLOPS 15.7 TFLOPS Tensor Performance 112 TFLOPS 125 ...

  Tesla, Engineer, Scientist, V001, And engineers, Tesla v100

Learning Spatio-Temporal Transformer for Visual Tracking

Learning Spatio-Temporal Transformer for Visual Tracking

openaccess.thecvf.com

(30 v.s. 5 fps) on a Tesla V100 GPU, as shown in Fig.1 Considering recent trends of over-fitting on small-scale benchmarks, we collect a new large-scale tracking benchmark called NOTU, integrating all sequences from NFS [24], OTB100 [58], TC128 [33], and UAV123 [42]. In summary, this work has four contributions.

  Tesla, V001, Tesla v100

Number of parameters (M)

Number of parameters (M)

arxiv.org

and batch=1 on a single Tesla V100. YOLOv3 baseline Our baseline adopts the architec-to YOLOv3-SPP in some papers [1,7]. We slightly change some training strategies compared to the orig-inal implementation [25], adding EMA weights updat-ing, cosine lr schedule, IoU loss and IoU-aware branch. We use BCE Loss for training cls and obj branch, reg ...

  Tesla, V001, Tesla v100

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