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

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Identifying current state – The model stores the prior records for optimal action definition for maximizing the results. For acting in the present state, the state needs to be identified and perform an action combination for it. MRR – Monthly recurring revenue, which tells you all the income that can be generated from all your income channels.

Tesla cards have four times the double precision performance of a Fermi-based Nvidia GeForce card of similar single precision performance. [ citation needed] Figure 5 shows deep learning training performance and scaling on DGX-1. The bars in Figure 5 represent training performance in images per second for the ResNet-50 deep neural network architecture using the Microsoft Cognitive Toolkit (CNTK), and the lines represent the parallel speedup of 2, 4, or 8 P100 GPUs versus a single GPU. The tests used a minibatch size of 64 images per GPU. Figure 5: DGX-1 (weak) scaling results and performance for training the ResNet-50 neural network architecture using the Microsoft Cognitive Toolkit (CNTK) with a batch size of 64 per GPU. The bars present performance on one, two, four, and eight Tesla P100 GPUs in DGX-1 using NVLink for inter-GPU communication (light green) compared to an off-the shelf system with eight Tesla P100 GPUs using PCIe for communication (dark green). The lines present the speedup compared to a single GPU. On eight GPUs, NVLink provides about 1.4x (1513 images/s vs. 1096 images/s) higher training performance than PCIe. Tests used NVIDIA DGX containers version 16.12, processing real data with cuDNN 6.0.5, NCCL 1.6.1, gradbits=32. The volumetric output will be done in both high and low resolution, and the surface output will be generated through parameterisation, template deformation and point cloud. Moreover, the direct and intermediate outputs will be calculated this way. Tesla products are primarily used in simulations and in large-scale calculations (especially floating-point calculations), and for high-end image generation for professional and scientific fields. [8]

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NVLink™—NVIDIA’s new high speed, high bandwidth interconnect for maximum application scalability; The degree of supervision used in 2D vs 3D supervision, weak supervision along with loss functions have to be included in this system. The training procedure is adversarial training with joint 2D and 3D embeddings. Also, the network architecture is extremely important for the speed and processing quality of the output images. a b Smith, Ryan (13 September 2016). "Nvidia Announces Tesla P40 & Tesla P4 - Network Inference, Big & Small". Anandtech . Retrieved 13 September 2016. Programs can call the new CUDA 9 warp synchronization function __syncwarp() to force reconvergence, as Figure 13 shows. In this case, the divergent portions of the warp might not execute Z together, but all execution pathways from threads within a warp will complete before any thread reaches the statement after the __syncwarp(). Similarly, placing the call to __syncwarp() before the execution of Z would force reconvergence before executing Z, potentially enabling greater SIMT efficiency if the developer knows that this is safe for their application. Figure 13: Programs can use explicit synchronization to reconverge threads in a warp. Starvation-Free Algorithms The latest DGX-1 multi-system clusters use a network based on a fat tree topology providing well-routed, predictable, contention-free communication from each system to every other system (see Figure 6). A fat tree is a tree-structured network topology with systems at the leaves that connect up through multiple switch levels to a central top-level switch. Each level in a fat tree has the same number of links providing equal bandwidth. The fat tree topology ensures the highest communication bisection bandwidth and lowest latency for all-to-all or all-gather type collectives that are common in computational and deep learning applications. Figure 6: Example multisystem cluster of 124 DGX-1 systems tuned for deep learning. DGX-1 Software

Pascal and earlier NVIDIA GPUs execute groups of 32 threads—known as warps—in SIMT (Single Instruction, Multiple Thread) fashion. The Pascal warp uses a single program counter shared amongst all 32 threads, combined with an “active mask” that specifies which threads of the warp are active at any given time. This means that divergent execution paths leave some threads inactive, serializing execution for different portions of the warp as Figure 10 shows. The original mask is stored until the warp reconverges at the end of the divergent section, at which point the mask is restored and the threads run together once again. Figure 10: Thread scheduling under the SIMT warp execution model of Pascal and earlier NVIDIA GPUs. Capital letters represent statements in the program pseudocode. Divergent branches within a warp are serialized so that all statements in one side of the branch are executed together to completion before any statements in the other side are executed. After the else statement, the threads of the warp will typically reconverge. To provide the highest possible computational density, DGX-1 includes eight NVIDIA Tesla P100 accelerators (Figure 3). Application scaling on this many highly parallel GPUs is hampered by today’s PCIe interconnect. NVLink provides the communications performance needed to achieve good ( weak and strong) scaling on deep learning and other applications. Each Tesla P100 GPU has four NVLink connection points, each providing a point-to-point connection to another GPU at a peak bandwidth of 20 GB/s. Multiple NVLink connections can be bonded together, multiplying the available interconnection bandwidth between a given pair of GPUs. The result is that NVLink provides a flexible interconnect that can be used to build a variety of network topologies among multiple GPUs. Pascal also supports 16 lanes of PCIe 3.0. In DGX-1, these are used for connecting between the CPUs and GPUs. PCIe is also used for high-speed networking interface cards.Reinforcement is a subsection of ML. This part of ML is related to the action in which an environmental agent participates in a reward-based system and uses Reinforcement Learning to maximize the rewards. Reinforcement Learning is a different technique from unsupervised learning or supervised learning because it does not require a supervised input/output pair. The number of corrections is also less, so it is a highly efficient technique. a b "Accelerating Hyperscale Datacenter Applications with Tesla GPUs | Parallel Forall". Devblogs.nvidia.com. 10 November 2015 . Retrieved 11 December 2015. CLV – Customer lifetime value tells you how much a customer is willing to spend on your business during your mutual relationship duration. a b Smith, Ryan (20 June 2016). "NVidia Announces PCI Express Tesla P100". Anandtech.com . Retrieved 21 June 2016.

The Volta architecture is designed to be significantly easier to program than prior GPUs, enabling users to work productively on more complex and diverse applications. Volta GV100 is the first GPU to support independent thread scheduling, which enables finer-grain synchronization and cooperation between parallel threads in a program. One of the major design goals for Volta was to reduce the effort required to get programs running on the GPU, and to enable greater flexibility in thread cooperation, leading to higher efficiency for fine-grained parallel algorithms. Prior NVIDIA GPU SIMT ModelsDeveloping this deep learning technology aims to infer the shape of 3D objects from 2D images. So, to conduct the experiment, you need the following: a b Smith, Ryan (10 May 2017). "NVIDIA Volta Unveiled: GV100 GPU and Tesla V100 Accelerator Announced". Anandtech . Retrieved 10 May 2017. So, if you are just starting your business, or planning to expand it, read on to learn more about this concept. The 8-GPU configuration features two NVLink fully-connected P100 GPU quads that are tied together by four additional NVLinks in a Hybrid Cube Mesh topology (See the 8-GPU NVLink diagram above). Every GPU in a quad is also directly connected via PCIe to a PCIe switch that connects to a CPU. The bottom line is that an NVIDIA DGX-1 server with eight Tesla P100 accelerators can deliver over 12x the Deep Learning performance compared to previous GPU-accelerated solutions. Another HBM2 benefit is native support for error correcting code (ECC) funtionality, which provides higher reliability for technical computing applications that are sensitive to data corruption, such as in large-scale clusters and supercomputers, where GPUs process large datasets with long application run times.

High performance network topology support to enable data transfer between multiple systems simultaneously with minimal contention; a b Oh, Nate (20 June 2017). "NVIDIA Formally Announces V100: Available later this Year". Anandtech.com . Retrieved 20 June 2017. In this blog post we will provide an overview of the Volta architecture and its benefits to you as a developer. Tesla V100: The AI Computing and HPC Powerhouse 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.For each port, 8 data lanes operating at 25 Gb/s or 200 Gb/s total (4 lanes in (100 Gb/s) and 4 lanes out (100 Gb/s) simultaneously); It is interesting to note that Figure 12 does not show execution of statement Z by all threads in the warp at the same time. This is because the scheduler must conservatively assume that Z may produce data required by other divergent branches of execution in which case it would be unsafe to automatically enforce reconvergence. In the common case where A, B, X, and Y do not consist of synchronizing operations, the scheduler can identify that it is safe for the warp to naturally reconverge on Z, as on prior architectures.