With Hopper Confidential Computing, this scalable compute power can secure sensitive applications on shared data center infrastructure. For small jobs, H100 can be partitioned down to right-sized Multi-Instance GPU (MIG) partitions. H100 accelerates exascale scale workloads with a dedicated Transformer Engine for trillion parameter language models. The NVIDIA ® H100 Tensor Core GPU enables an order-of-magnitude leap for large-scale AI and HPC with unprecedented performance, scalability, and security for every data center and includes the NVIDIA AI Enterprise software suite to streamline AI development and deployment. Will all Pascal designs use 64 CUDA cores per SM, or could we see a split where other GPUs drop some of the FP64 units in order to reduce the die size or add additional cores? That doesn't seem likely, but stranger things have happened.NVIDIA H100 PCIe Unprecedented Performance, Scalability, and Security for Every Data Center It will be interesting to see how much of what has been revealed here applies to other Pascal offerings, as GP100 at least clearly has some features that are unnecessary in client workloads (e.g. That could be useful for things like GeForce Now, where at present GPUs are allocated to subscribers on a 1-to-1 basis with Pascal GPUs it would potentially become easier to run multiple games on each GPU, though obviously the need to store the registry file, cache, and other state variables every time you preempt means you don't want to do it very often.įrom the compute perspective, Pascal and GP100 are shaping up to be a large jump in performance, just as we expected from the move to 16nm FinFET. This could be beneficial for things like VR and asynchronous time warp, but it also allows better GPU sharing on servers and in the cloud. With Pascal, Nvidia has added the ability to preempt at the instruction level, so it's possible to switch tasks at any time to run a different workload. One of these is particularly interesting, however: compute preemption. Finally, outside of the recently launched 24GB Tesla M40, the P100 also has more memory than the previous Tesla offerings.įinally, there are other elements of the Pascal architecture that weren't really covered in much detail. Memory bandwidth is also greatly improved thanks to HBM2, with GP100 getting 2.5 times as much bandwidth. For FP64, meanwhile, P100 is more than 20 times faster than M40, and a sizeable 3.8X jump in performance over K40. K40 and M40 only support FP32 or FP64, and in those workloads the difference isn't quite as large, though P100 is still around 50 percent faster than M40 and nearly 150 percent faster than K40. In terms of peak throughput (the 21 TFLOPS number Nvidia has bandied about), GP100 does best with FP16. Finally, Nvidia has added a Page Migration Engine, which combined with unified memory allows the addressing of up to 512TB of virtual memory. The Tesla P100 will also use HBM2 (High Bandwidth Memory), similar to AMD's Fiji architecture but with significantly more capacity. NVLink improves inter-processor communication, relieving the PCI Express bus of this burden and providing much higher throughput for enhanced scalability. With Tesla P100, all workloads will run optimally on a single architecture.Īlong with the improvements to the raw performance, Pascal brings several other technologies that will help with compute. For workloads that don't need the extra precision, Maxwell was fine, but many scientific applications stuck with Tesla K40/K80 (K110B GPUs) for this reason while others were able to move to the Tesla M40 to take advantage of its superior FP32 performance. In some ways, Pascal is similar to the Fermi architecture, which was the last time Nvidia offered FP64 performance running at half the FP32 rate Kepler dropped to 1/3 performance on FP64 while Maxwell was limited to only 1/32 FLOPS rate on FP64.
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