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Project Compilation (Parameter Computation) & FEA Analysis:
The speed of these tasks is primarily related to the maximum turbo frequency of the CPU.
A higher turbo frequency enables faster computation and compilation.
Design & Live Load Analysis (Critical Vehicle Position Finding Algorithm):
This algorithm is heavily dependent on multi-threaded performance, making CPUs with 8 or more cores ideal.
If the base clock speed of the efficiency cores is higher, it can improve performance for this task.
Most operations still rely on performance cores, so the maximum turbo frequency of the CPU remains critical.
WebGL Rendering (Graphics Performance):
The graphics card plays an important role in rendering nodes3D, 3D FEA models , and other visual elements.
A better graphics card reduces freeze times during rendering operations, improving the overall user experience.
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Memory: One Chrome tab is limited to using a maximum of 4GB of memory, even on 64-bit systems, due to Chrome's per-tab memory limit. As a result:
If you are working on a single project at a time, a system with 32GB of RAM is sufficient for most operations.
If you plan to run 5-6 different models simultaneously, you’ll require:
A CPU with more than 8 cores to handle the workload efficiently.
48-64GB of RAM to accommodate the increased memory demands across multiple tabs.
GPU: For optimal performance with OpenBrIM, it is recommended to use a new generation discrete GPU from NVIDIA or AMD (2022 or later), as these GPUs are well-equipped to handle the platform's requirements. If you prefer an Intel GPU, the Intel ARC series is a better choice than the Intel Iris integrated GPUs, offering significantly improved performance.
In certain scenarios, Intel ARC GPUs, as well as mid-tier AMD or NVIDIA discrete GPUs, can be more advantageous than high-end NVIDIA GPUs due to their lower power consumption and superior thermal efficiency, making them ideal for laptops prioritizing energy efficiency and temperature management.