Nvidia Hopper Speculation, Rumours and Discussion
Must have missed that part reading it through, but it still in no way indicates or suggests it would be the first they're using such technologies.
As for the economics of it, I can see plenty of scenarios where it's more economical to rent your "own hardware" from outside source rather than build similar sized server farm yourself.
TopSpoiler
Regular
Upcoming GTC sessions related to Hopper architecture.
Attendee Portal
register.nvidia.com
- Learn about the latest additions to the CUDA platform, Language and Toolkit, and what the new Hopper GPU architecture brings to CUDA. Presented by one of the architects of CUDA, this engineer-focused session covers all the latest developments for NVIDIA's GPU developer ecosystem as well as looking ahead to where CUDA will be going over the coming year.
Attendee Portal
- NVIDIA’s new Hopper GPUs contain advanced features that can unleash tremendous application performance, but they can also require some new techniques for different ways of coding your applications. Learn how to access Hopper’s advanced capabilities and squeeze all the juice from your hardware while retaining performance portability. We’ll cover how to opt in for performance features, design patterns for structuring code for compatibility and performance, and strategies for effective testing and QA of high-performance applications with an eye to portability.
Attendee Portal
- This session will introduce new features in CUDA for programming Hopper architecture. The new programming model for Hopper is more hierarchical and asynchronous. CUDA programming for Hopper introduces optional level of hierarchy called Thread Block clusters, that enable multiple thread blocks within the cluster to communicate using a common pool of shared memory. The asynchronous data movement is now hardware accelerated in all directions between global and shared memories. We will look at how to exploit the new programming model in applications for performance tuning.
Attendee Portal
- The upcoming Hopper-based platforms, as well as the Grace Hopper Superchip are exciting developments for high-performance computing. Taking advantage of this new hardware performance takes great software, and CUDA developer tools are here to help. We'll give a brief overview of the tools available for free to developers, then detail the newest features and explain how they help users identify performance and correctness issues, where they are occurring, and some options to fix them. We'll pay specific attention to features supporting new architectures. CUDA developer tools are designed in lockstep with the CUDA ecosystem, including hardware and software. With new technologies like the Grace Hopper Superchip, visibility and optimization of the entire platform are key to unleashing the next level of accelerated computing performance. This presentation will prepare you for that move to the leading edge.
There is no dedicacted Grace thread, so i put this here. nVidia disclosed more information about their server CPU: https://www.tomshardware.com/news/n...superchip-design-144-cores-on-4n-tsmc-process
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Deleted member 2197
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NVIDIA Hopper H100 With 4th Gen Tensor Core Is Twice As Fast Clock-For-Clock, Frequency Delivers 30% Performance Gain
NVIDIA is further dissecting its Hopper H100 GPU at Hot Chips 34, giving us a better taste of the 4th Gen Tensor Core architecture.
wccftech.com
- 132 SMs (2x Performance Per Clock)
- 4th Gen Tensor Cores
- Thread Block Clusters
- 2nd Gen Multi-Instance GPU
- Confidential Computing
- PCIe Gen 5.0 Interface
- World's First HBM3 DRAM
- Larger 50 MB L2 Cache
- 4th Gen NVLink (900 GB/s Total Bandwidth)
- New SHARP support
- NVLink Network
...
Rounding up the performance figures, NVIDIA's GH100 Hopper GPU will offer 4000 TFLOPs of FP8, 2000 TFLOPs of FP16, 1000 TFLOPs of TF32 and 60 TFLOPs of FP64 Compute performance. These record-shattering figures decimate all other HPC accelerators that came before it. For comparison, this is 3.3x faster than NVIDIA's own A100 GPU and 28% faster than AMD's Instinct MI250X in the FP64 compute. In FP16 compute, the H100 GPU is 3x faster than A100 and 5.2x faster than MI250X which is literally bonkers.
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neckthrough
Regular
According to the specs H100 is 1:2 non-tensor and 1:17 tensor (TF32).
Not that I’m laughing at either NVIDIA or AMD’s decisions here. I’m sure they’ve worked out the math.
Was the link above wrong then? It at least lists 1:1 FP32:FP64 cores?
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Deleted member 2197
Guest
They are talking about Intel's Ponte Vecchio.
Huh?
https://wccftech.com/nvidia-hopper-h100-4th-gen-tensor-core-twice-as-fast-clock-for-clock/
for sure isn't talking about Ponte Vecchio, could be simple mistake too of course but they list same amount of FP32 and FP64 CUDA cores
TopSpoiler
Regular
Wccftech is wrong. See official specs here, The number of FP64 CUDA cores are half of FP32.
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Deleted member 2197
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NVIDIA NVLink4 NVSwitch at Hot Chips 34
We got a treat at Hot Chips 34 with a look at the NVLink4 NVSwitch and how NVIDIA is expanding the use of the technology
www.servethehome.com
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DegustatoR
Legend
NVIDIA Hopper Features "SM-to-SM" Comms Within GPC That Minimize Cache Roundtrips and Boost Multi-Instance Performance
NVIDIA in its HotChips 34 presentation revealed a defining feature of its "Hopper" compute architecture that works to increase parallelism and help the H100 processor better perform in a multi-instance environment. The hardware component hierarchy of "Hopper" is typical of NVIDIA architectures...
NVIDIA Grace CPU Specs Remind Us Why Intel Never Shared x86 with the Green Team
NVIDIA designed the Grace CPU, a processor in the classical sense, to replace the Intel Xeon or AMD EPYC processors it was having to cram into its pre-built HPC compute servers for serial-processing roles, and mainly because those half-a-dozen GPU HPC processors need to be interconnected by a...
DavidGraham
Veteran
NVIDIA says H100 is up to 4X times faster than A100.
wccftech.com
NVIDIA Clean Sweeps MLPerf AI Benchmarks With Hopper H100 GPU, Up To 4.5x Performance Uplift Over Ampere A100
NVIDIA's Hopper H100 GPU has made its debut on the MLPerf AI Benchmark list and shattered all previous records achieved by Ampere A100.
wccftech.com
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Deleted member 2197
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DavidGraham
Veteran
Shaheen III HPC Super Computer deploys 2800 of Hopper Grace chips (GPU + CPU), Alongside thousands of Zen 4 CPUs from AMD.
www.nextplatform.com
KAUST Goes For Nvidia CPU-GPU, AMD CPU Combo In Shaheen-III Supercomputer
The King Abdullah University of Science and Technology (KAUST) in Saudi Arabia has introduced the latest in its long-standing lineup of successive
www.nextplatform.com
TopSpoiler
Regular
NVIDIA updates Hopper H100 data-center GPU FP32 performance from 60 to 67 TFLOPS - VideoCardz.com
NVIDIA updates H100 specs The preliminary specs have been updated with more accurate data. NVIDIA has updated the specs for the H100, just 2 weeks after confirming its availability date. Hopper H100 Data-Center GPU, Source: NVIDIA NVIDIA is now displaying more accurate single-precision and...
videocardz.com
Frontier Testing and Tuning Problems Downplayed by Oak Ridge - High-Performance Computing News Analysis | insideHPC
Everything about the Frontier supercomputer, the world’s first exascale system residing at Oak Ridge National Laboratory, is outsized: its power, its scale and the attention it draws. In HPC circles, the attention and talk have increasingly focused on performance problems as the lab gets the...
insidehpc.com
Distributed computing is hard. Just building a linpack chip and outdated interconnections is not enough. Optical NVLink is so far ahead right know that real scientific workloads will be running so much better on their plattform.
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Seriously, you need to fix your hostility.Frontier Testing and Tuning Problems Downplayed by Oak Ridge - High-Performance Computing News Analysis | insideHPC
Everything about the Frontier supercomputer, the world’s first exascale system residing at Oak Ridge National Laboratory, is outsized: its power, its scale and the attention it draws. In HPC circles, the attention and talk have increasingly focused on performance problems as the lab gets the...
Distributed computing is hard. Just building a linpack chip and outdated interconnections is not enough. Optical NVLink is so far ahead right know that real scientific workloads will be running so much better on their plattform.
Slingshot interconnect launched in 2020 was definitely not outdated when they specced Frontier and the nodes it uses. Optical NVLink was nowhere near ready when those systems were specced.