Faster and simpler marching-cubes implementation, added simplified ma…

…rching-cubes for solid surface rendering, refactoring in OpenCL rendering kernels

README.md

@@ -3,7 +3,7 @@
 The fastest and most memory efficient lattice Boltzmann CFD software, running on all GPUs via [OpenCL](https://github.com/ProjectPhysX/OpenCL-Wrapper "OpenCL-Wrapper"). Free for non-commercial use.
 
 <a href="https://youtu.be/-MkRBeQkLk8"><img src="https://img.youtube.com/vi/o3TPN142HxM/maxresdefault.jpg" width="50%"></img></a><a href="https://youtu.be/oC6U1M0Fsug"><img src="https://img.youtube.com/vi/oC6U1M0Fsug/maxresdefault.jpg" width="50%"></img></a><br>
-<a href="https://youtu.be/XOfXHgP4jnQ"><img src="https://img.youtube.com/vi/XOfXHgP4jnQ/maxresdefault.jpg" width="50%"></img></a><a href="https://youtu.be/BStzTRmLW7Q"><img src="https://img.youtube.com/vi/BStzTRmLW7Q/maxresdefault.jpg" width="50%"></img></a>
+<a href="https://youtu.be/XOfXHgP4jnQ"><img src="https://img.youtube.com/vi/XOfXHgP4jnQ/maxresdefault.jpg" width="50%"></img></a><a href="https://youtu.be/clAqgNtySow"><img src="https://img.youtube.com/vi/clAqgNtySow/maxresdefault.jpg" width="50%"></img></a>
 (click on images to show videos on YouTube)
 
 <details><summary>Update History</summary>
@@ -220,7 +220,7 @@ $$f_j(i\\%2\\ ?\\ \vec{x}+\vec{e}_i\\ :\\ \vec{x},\\ t+\Delta t)=f_i^\textrm{tem
   - FluidX3D (D3Q19) requires only 55 Bytes/cell with [Esoteric-Pull](https://doi.org/10.3390/computation10060092)+[FP16](https://www.researchgate.net/publication/362275548_Accuracy_and_performance_of_the_lattice_Boltzmann_method_with_64-bit_32-bit_and_customized_16-bit_number_formats)<br>
     - 🟧🟧🟧🟧🟦🟦🟦🟦🟦🟦🟦🟦🟦🟦🟦🟦🟨🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩<br>(density 🟧, velocity 🟦, flags 🟨, DDFs 🟩; each square = 1 Byte)
     - allows for 19 Million cells per 1 GB VRAM
-    - in-place streaming with [Esoteric-Pull](https://doi.org/10.3390/computation10060092): eliminates redundant copy `B` of density distribution functions (DDFs) in memory; almost cuts memory demand in half and slightly increases performance due to implicit bounce-back boundaries; offers optimal memory access patterns for single-cell in-place streaming
+    - in-place streaming with [Esoteric-Pull](https://doi.org/10.3390/computation10060092): eliminates redundant copy of density distribution functions (DDFs) in memory; almost cuts memory demand in half and slightly increases performance due to implicit bounce-back boundaries; offers optimal memory access patterns for single-cell in-place streaming
     - [decoupled arithmetic precision (FP32) and memory precision (FP32 or FP16S or FP16C)](https://www.researchgate.net/publication/362275548_Accuracy_and_performance_of_the_lattice_Boltzmann_method_with_64-bit_32-bit_and_customized_16-bit_number_formats): all arithmetic is done in FP32 for compatibility on all hardware, but DDFs in memory can be compressed to FP16S or FP16C: almost cuts memory demand in half again and almost doubles performance, without impacting overall accuracy for most setups
     - <details><summary>only 8 flag bits per lattice point (can be used independently / at the same time)</summary>
 
@@ -460,6 +460,7 @@ Colors: 🔴 AMD, 🔵 Intel, 🟢 Nvidia, ⚪ Apple, 🟡 ARM, 🟤 Glenfly
 | 🔴&nbsp;Radeon&nbsp;RX&nbsp;5700&nbsp;XT         |               9.75 |           8 |          448 |             1368 (47%) |              3253 (56%) |              3049 (52%) |
 | 🔴&nbsp;Radeon&nbsp;RX&nbsp;5600&nbsp;XT         |               6.73 |           6 |          288 |             1136 (60%) |              2214 (59%) |              2148 (57%) |
 | 🔴&nbsp;Radeon&nbsp;RX&nbsp;Vega&nbsp;64         |              13.35 |           8 |          484 |             1875 (59%) |              2878 (46%) |              3227 (51%) |
+| 🔴&nbsp;Radeon&nbsp;RX&nbsp;590                  |               5.53 |           8 |          256 |             1257 (75%) |              1573 (47%) |              1688 (51%) |
 | 🔴&nbsp;Radeon&nbsp;RX&nbsp;580&nbsp;4GB         |               6.50 |           4 |          256 |              946 (57%) |              1848 (56%) |              1577 (47%) |
 | 🔴&nbsp;Radeon&nbsp;R9&nbsp;390X                 |               5.91 |           8 |          384 |             1733 (69%) |              2217 (44%) |              1722 (35%) |
 | 🔴&nbsp;Radeon&nbsp;HD&nbsp;7850                 |               1.84 |           2 |          154 |              112 (11%) |               120 ( 6%) |               635 (32%) |