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.
Raja Sekhar Goriparti, P.E.’s Post
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Got time for some accelerated Llama 3.1? Check out my latest video where I show Llama 3.1 dynamically accelerated by a disaggregated GPU
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CFD on GPU. The C means "consistent" 😎 https://lnkd.in/g3pQniFf If I had to name ONE user need when it comes to CFD I could quote the German chancellor: "Faster.... Faster!" And there is one hardware opportunity for this! 💻 Historically, CPUs where the hardware to use for CFD. Why? Because you gotta fit that discretization (you mesh) into the fastest memory available. And that was RAM, because VRAM is just orders of magnitude more expensive. 👩💻 Things are changing on the hardware - and so are things in our software! 🖨 Despite exploiting these opportunities, one aspect is critical: 🤗 CPU, GPU, ARM, x86.... whatever you use, we will deliver the same results. 🎡 We're not inventing the wheel anew. Our GPU solvers are our CPU solvers. Evolution. Consistency. Seamless disruption. Why are you running your CFD simulation still on CPU? #simcenter #cfd #simulation #gpu #nvidia #amd
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6moGreat post!