NVIDIA Tesla P100 16GB PCIe 3.0 Passive GPU Accelerator (900-2H400-0000-000)

Product Information

If you want maximum Deep Learning performance, Tesla V100 is a great choice because of its performance. The dedicated TensorCores have huge performance potential for deep learning applications. NVIDIA has even termed a new “TensorFLOP” to measure this gain. a b Smith, Ryan (20 June 2016). "NVidia Announces PCI Express Tesla P100". Anandtech.com . Retrieved 21 June 2016. The extensions of these generative models have a tremendous effect on ML and computer vision. Pragmatically, such models are highly useful. They are applied in model-based reinforcement learning and planning world models, SLAM is s, or 3D content creation.

At the 2016 GPU Technology Conference in San Jose, NVIDIA CEO Jen-Hsun Huang announced the new NVIDIA Tesla P100, the most advanced accelerator ever built. Based on the new NVIDIA Pascal GP100 GPU and powered by ground-breaking technologies, Tesla P100 delivers the highest absolute performance for HPC, technical computing, deep learning, and many computationally intensive datacenter workloads. As an organization, when working in a diverse and competitive market like India, you need to have a well-defined customer acquisition strategy to attain success. However, this is where most startups struggle. Now, you may have a great product or service, but if you are not in the right place targeting the right demographic, you are not likely to get the results you want. Matrix-Matrix multiplication (BLAS GEMM) operations are at the core of neural network training and inferencing, and are used to multiply large matrices of input data and weights in the connected layers of the network. As Figure 6 shows, Tensor Cores in the Tesla V100 GPU boost the performance of these operations by more than 9x compared to the Pascal-based GP100 GPU. Figure 6: Tesla V100 Tensor Cores and CUDA 9 deliver up to 9x higher performance for GEMM operations. (Measured on pre-production Tesla V100 using pre-release CUDA 9 software.) The high performance of DGX-1 is due in part to the NVLink hybrid cube-mesh interconnect between its eight Tesla P100 GPUs, but that is not the whole story. Much of the performance benefit of DGX-1 comes from the fact that it is an integrated system, with a complete software platform aimed at deep learning. This includes the deep learning framework optimizations such as those in NVIDIA Caffe, cuBLAS, cuDNN, and other GPU-accelerated libraries, and NVLink-tuned collective communications through NCCL. This integrated software platform, combined with Tesla P100 and NVLink, ensures that DGX-1 outperforms similar off-the-shelf systems.

DGX-1 Software

Any business is enlivened by its customers. Therefore, a strategy to constantly bring in new clients is an ongoing requirement. In this regard, having a proper customer acquisition strategy can be of great importance. This simple training process is then scaled to trajectories, thousands of them creating a large number of views. The model samples the radiance fields totally from the previous distribution that the model has learned. During program execution, multiple Tensor Cores are used concurrently by a full warp of execution. The threads within a warp provide a larger 16x16x16 matrix operation to be processed by the Tensor Cores. CUDA exposes these operations as Warp-Level Matrix Operations in the CUDA C++ API. These C++ interfaces provide specialized matrix load, matrix multiply and accumulate, and matrix store operations to efficiently utilize Tensor Cores in CUDA C++ programs. High Performance Computing (HPC) is a fundamental pillar of modern science. From predicting weather, to discovering drugs, to finding new energy sources, researchers use large computing systems to simulate and predict our world. AI extends traditional HPC by allowing researchers to analyze large volumes of data for rapid insights where simulation alone cannot fully predict the real world.

NVLink uses NVIDIA’s new High-Speed Signaling interconnect (NVHS). NVHS transmits data over a differential pair running at up to 20 Gb/sec. Eight of these differential connections form a “Sub-Link” that sends data in one direction, and two sub-links—one for each direction—form a “Link” that connects two processors (GPU-to-GPU or GPU-to-CPU). A single Link supports up to 40 GB/sec of bidirectional bandwidth between the endpoints. Multiple Links can be combined to form “Gangs” for even higher-bandwidth connectivity between processors. The NVLink implementation in Tesla P100 supports up to four Links, allowing for a gang with an aggregate maximum theoretical bandwidth of 160 GB/sec bidirectional bandwidth. reconstruction is one of the most complex issues of deep learning systems. There have been multiple types of research in this field, and almost everything has been tried on it — computer vision, computer graphics and machine learning, but to no avail. However, that has resulted in CNN or convolutional neural networks foraying into this field, which has yielded some success. The Main Objective of the 3D Object Reconstruction Tesla P100 accelerators will be available in two forms: A traditional GPU accelerator board for PCIe-based servers, and an SXM2 module for NVLink-optimized servers. P100 for PCIe-based servers allows HPC data centers to deploy the most advanced GPUs within PCIe-based nodes to support a mix of CPU and GPU workloads. P100 for NVLink-optimized servers provides the best performance and strong scaling for hyperscale and HPC data centers running applications that scale to multiple GPUs, such as deep learning. The table below provides the complete specifications of both Tesla P100 accelerators. The Pascal GP100 Architecture: Faster in Every Way Walton, Mark (6 April 2016). "Nvidia unveils first Pascal graphics card, the monstrous Tesla P100". Ars Technica . Retrieved 19 June 2019.

Which GPU should be used when?

Today at their 2016 GPU Technology Conference, NVIDIA announced the first of their Pascal architecture powered Tesla cards, the Tesla P100. The P100 is the first major update to the Tesla HPC family since the launch of the first Kepler cards in late 2012, and represents a very sizable performance increase for the Tesla family thanks to the combination of the smaller 16nm manufacturing process and the Pascal architecture. NVIDIA Tesla Family Specification Comparison