Patrick Moorhead’s Post

The evolution of Nvidia GPUs from 1995-2020

Evolution of Nvidia GPUs from 1995-2020

Introducing Lambda 1-Click Clusters: On-Demand GPU Clusters featuring NVIDIA H100 Tensor Core GPUs with Quantum-2 InfiniBand. No long-term contract required. https://lnkd.in/eiY-ebq7

Cheaper but too slow, or faster but too expensive? 😑 Fear no more: we’re introducing new 1x, 2x, and 4x NVIDIA H100 SXM Tensor Core GPU instances, to create further access to high-performance NVIDIA GPUs (up to 51% faster than PCIe versions!), but in smaller chunks to remain affordable. Read more at https://bit.ly/3NhvQqg

Enabling NVIDIA GPUs for FEA solving can significantly accelerate FEA solve times when paired with the optimal number of CPU cores. However, an excessive number of CPU cores paired with a single GPU may impede performance due to PCI-Express bandwidth limitations. To witness the GPU's prowess in action during the solving process, navigate to the task manager and observe the GPU graph. Switch from the default 3D view to CUDA. You'll notice small spikes appearing once the CPU completes assembling the global matrix, subdividing the task into individual domains, and delegating matrix operations to CUDA cores. Each of these spikes signifies an individual increments within each of your load step.

With #PreonLab 6.1, we take another massive step towards our goal of providing the ultimate multi-platform support. While simulation on CPU is still the bread and butter for #CFD simulations, it is undeniable that GPUs provide a significant boost to simulation performance. But what is faster than GPU? - multi-GPU, of course.  With the multi-GPU capabilities of PreonLab 6.1, you can now transcend the memory limitations of single #GPU cards and accelerate your simulation performance even further.   That being said, it is important to know how the #simulation performance scales across multiple GPUs. That is why we recently conducted several performance benchmarks on CPU, GPU and multi-GPU hardware and would like to share some of these results over the next few weeks.   Performance Benchmarks:  We will kick things off with performance data from the well-established Marin Tank #benchmark, which can be seen in the following video.  The simulation consists of around 88 Million fluid #particles.  Result: The simulation scales in a nearly perfect manner from a single GPU to 2 GPUs as well as from 2 GPUs to 4 GPUs with a scaling factor of 1.9x!    Hardware used for this performance benchmark: 2x AMD EPYC 7543 CPU - TDP: 2x 225 W 1 to 4x Nvidia A100 SXM4 40GB GPU - TDP: 1 to 4 x 400W   Want to know more? Stay tuned for the next posts or get in touch with us!

POV: You found a #workstation that works as fast as your ideas. 😎💡 The Lenovo ThinkStation P7 is built to take on the toughest tasks with speed, power, and reliability that never quit. That's the kind of innovation you get when you combine Intel Corporation processors with NVIDIA GPUs. 🤝

[Sharing] Hi. How's it going! Today, we would like to share NVIDIA A30 24GB GPU with you. https://lnkd.in/g37zYz8D NVIDIA A30 24GB GPU features: FP64 NVIDIA Ampere architecture Tensor Cores that deliver the biggest leap in HPC performance since the introduction of GPUs. Combined with 24 gigabytes (GB) of GPU memory with a bandwidth of 933 gigabytes per second (GB/s), researchers can rapidly solve double-precision calculations. Go finding NVIDIA A30 24GB GPU in Century Tech System now! #Server #GPU #Graphiccard #NVIDIA #PCIe4 #FYI #Recommendations #New #Instock #Inventory #Centurytechsystem

[Sharing] Hi. How's it going! Today, we would like to share NVIDIA A30 24GB GPU with you. https://lnkd.in/g37zYz8D NVIDIA A30 24GB GPU features: FP64 NVIDIA Ampere architecture Tensor Cores that deliver the biggest leap in HPC performance since the introduction of GPUs. Combined with 24 gigabytes (GB) of GPU memory with a bandwidth of 933 gigabytes per second (GB/s), researchers can rapidly solve double-precision calculations. Go finding NVIDIA A30 24GB GPU in Century Tech System now! #Server #GPU #Graphiccard #NVIDIA #PCIe4 #FYI #Recommendations #New #Instock #Inventory #Centurytechsystem

Skeptical here. Nvidia managed to make itself a software company. AMD didn't. If all the wealth of CUDA-based software could be run on AMD GPUs, that would shift the competition more into chip design. The battlefield where Nvidia's superiority is, to say, not so obvious. Where AMD can deliver, ex., see all the success on the CPU side with EPYCs. But AMD worked on this software compatibility challenge for more than a decade now, and the results are still not here. #gpu #amd #nvidia #cuda