Updated link to Intel OpenCL CPU Runtime in Readme

ProjectPhysX · ProjectPhysX · commit 39ba06359062 · 2023-01-22T20:10:29.000+01:00
diff --git a/README.md b/README.md
@@ -443,10 +443,10 @@ Multi-GPU benchmarks are done at the largest possible grid resolution with a cub
 
 - <details><summary>Why no multi-relaxation-time (MRT) collision operator?</summary><br>The idea of MRT is to linearly transform the DDFs into "moment space" by matrix multiplication and relax these moments individually, promising better stability and accuracy. In practice, in the vast majority of cases, it has zero or even negative effects on stability and accuracy, and simple SRT is much superior. Apart from the kinematic shear viscosity and conserved terms, the remaining moments are non-physical quantities and their tuning is a blackbox. Although MRT can be implemented in an efficient manner with only a single matrix-vector multiplication in registers, leading to identical performance compared to SRT by remaining bandwidth-bound, storing the matrices vastly elongates and over-complicates the code for no real benefit.<br><br></details>
 
-- <details><summary>Can FluidX3D run on multiple GPUs at the same time?</summary><br>Yes. The simulation grid is then split in domains, one for each GPU (domain decomposition method). The GPUs essentially pool their memory, enabling much larger grid resolution and higher performance. Rendering is parallelized across multiple GPUs as well; each GPU renders its own domain with a 3D offset, then rendered frames from all GPUs are overlayed with their z-buffers. Communication between domains is done over PCIe, so no SLI/Crossfire/NVLink/InfinityFabric is required. All GPUs must however be installed in the same node (PC/laptop/server). Even unholy combinations of Nvidia/AMD/Intel GPUs will work, although it is recommended to only use GPUs with similar memory capacity and bandwidth together. Using a fast gaming GPU and slow integrated GPU together would only decrease performance due to communication overhead.<br><br></details>
-
 ### Hardware
 
+- <details><summary>Can FluidX3D run on multiple GPUs at the same time?</summary><br>Yes. The simulation grid is then split in domains, one for each GPU (domain decomposition method). The GPUs essentially pool their memory, enabling much larger grid resolution and higher performance. Rendering is parallelized across multiple GPUs as well; each GPU renders its own domain with a 3D offset, then rendered frames from all GPUs are overlayed with their z-buffers. Communication between domains is done over PCIe, so no SLI/Crossfire/NVLink/InfinityFabric is required. All GPUs must however be installed in the same node (PC/laptop/server). Even unholy combinations of Nvidia/AMD/Intel GPUs will work, although it is recommended to only use GPUs with similar memory capacity and bandwidth together. Using a fast gaming GPU and slow integrated GPU together would only decrease performance due to communication overhead.<br><br></details>
+
 - <details><summary>I'm on a budget and have only a cheap computer. Can I run FluidX3D on my toaster PC/laptop?</summary><br>Absolutely. Today even the most inexpensive hardware, like integrated GPUs or entry-level gaming GPUs, support OpenCL. You might be a bit more limited on memory capacity and grid resolution, but you should be good to go. I've tested FluidX3D on very old and inexpensive hardware and even on my Samsung S9+ smartphone, and it runs just fine, although admittedly a bit slower.<br><br></details>
 
 - <details><summary>I don't have an expensive workstation GPU, but only a gaming GPU. Will performance suffer?</summary><br>No. Efficiency on gaming GPUs is exactly as good as on their "professional"/workstation counterparts. Performance often is even better as gaming GPUs have higher boost clocks.<br><br></details>
@@ -457,7 +457,7 @@ Multi-GPU benchmarks are done at the largest possible grid resolution with a cub
 
 - <details><summary>I don't have a dedicated graphics card at all. Can I still run FluidX3D on my PC/laptop?</summary><br>Yes. FluidX3D also runs on all integrated GPUs since around 2012, and also on CPUs.<br><br></details>
 
-- <details><summary>I need more memory than my GPU can offer. Can I run FluidX3D on my CPU as well?</summary><br>Yes. You only need to install the <a href="https://www.intel.com/content/www/us/en/developer/articles/tool/opencl-drivers.html">OpenCL Runtime for Intel CPUs</a>.<br><br></details>
+- <details><summary>I need more memory than my GPU can offer. Can I run FluidX3D on my CPU as well?</summary><br>Yes. You only need to install the <a href="https://github.com/intel/llvm/releases/tag/2022-09">OpenCL Runtime for Intel CPUs</a>.<br><br></details>
 
 - <details><summary>In the benchmarks you list some very expensive hardware. How do you get access to that?</summary><br>I'm a scientist (PhD candidate in computational physics) and I use FluidX3D for my research, so I have access to BZHPC, SuperMUC-NG and JURECA-DC supercomputers.<br><br></details>
 
