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@@ -14,8 +14,7 @@ The minimiums are listed as follows:
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GPU acceleration is suggested for large dataset computation. Therefore, the requirements for GPU acceleration is listed as follows:
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* NVIDIA GPUs, with a minimum `compute capability <https://developer.nvidia.com/cuda-gpus>`__ of 5.0, i.e., later than GTX 750.
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* It is suggested that, make ``Nx`` × ``Ny`` × ``Nz`` × ``Nt`` less than 200,000,000 Lagrangian particles on each Gigabyte of GPU memory for double-side computation under default numerical methods for one still. Generally half it under high-order computations.
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For example, you can execute on a 600×300×300 mesh for c.a. 80 time steps for a still on a welcomed RTX 2080ti 22G customized GPU, with the price of ~$400.
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* It is suggested that, make ``Nx`` × ``Ny`` × ``Nz`` × ``Nt`` less than 200,000,000 Lagrangian particles on each Gigabyte of GPU memory for double-side computation under default numerical methods for one still. Generally half it under high-order computations. For example, you can execute on a 600×300×300 mesh for c.a. 80 time steps for a still on a welcomed RTX 2080ti 22G customized GPU, with the price of ~$400.
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* The general short for GPU is VRAM, not computational power, hence old-but-large GPUs are great for use. Newers can be faster, but smallers can run nothing.
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* The GPU requirements for dynamic LCS are undergoing experiments by the author.
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@@ -67,5 +66,6 @@ For already-installed ParaView, set environment variable ``PARAVIEW_PATH`` point
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Quickstart
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-----------------
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After configurations of environment, you can run a very first demo to test the environment as well as the code, and learn how to use this toolbox for your own data towards new insights.
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An computation example coming with a set of our in-house downscaled Direct Navier-Stokes Simulation (DNS) data is provided, including :math:
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The dataset can be downloaded from `here <https://noting.noting>`__, and the case descriptions can be referred to paper ``I.P.``.
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