update readme (elalish#384)

* updated readme

* forgot to save

* added performance plot

README.md

@@ -1,27 +1,41 @@
 [![codecov](https://codecov.io/github/elalish/manifold/branch/master/graph/badge.svg?token=IIA8G5HVS7)](https://codecov.io/github/elalish/manifold)
 [![twitter](https://img.shields.io/twitter/follow/manifoldcad?style=social&logo=twitter)](https://twitter.com/intent/follow?screen_name=manifoldcad)
 
+## Users
+
+[OpenSCAD](https://openscad.org/), [IFCjs](https://ifcjs.github.io/info/), [Grid.Space](https://grid.space/), and [OCADml](https://github.com/OCADml/OManifold) have all integrated our Manifold geometry kernel! Why? Because its reliability is guaranteed and it's 1,000 times faster than other libraries. See our [usage](https://github.com/elalish/manifold/discussions/340) and [performance](https://github.com/elalish/manifold/discussions/383) discussions for all the latest and to add your own projects & analyses. 
+
+For example, here is a log-log plot of Manifold's performance vs. earlier OpenSCAD geometry backends:
+
+<img src="https://github.com/elalish/manifold/samples/models/perfSpheres.png" width="350px"/>
+
 ## [ManifoldCAD.org](https://manifoldcad.org)
 
-If you like OpenSCAD / OpenJSCAD, you might also like ManifoldCAD - our own solid modelling web app. The operations are not GPU-accelerated, but it's still pretty fast and a good way to test out our Manifold library.
+If you like OpenSCAD / JSCAD, you might also like ManifoldCAD - our own solid modelling web app. Our WASM is not GPU-accelerated, but it's still quite fast and a good way to test out our Manifold library.
 
 ![A metallic Menger sponge](https://elalish.github.io/manifold/samples/models/mengerSponge3.webp "A metallic Menger sponge")
 
 # Manifold
 
-[**High-level Documentation**](https://elalish.blogspot.com/search/label/Manifold) | [**API Documentation**](https://elalish.github.io/manifold/docs/html/modules.html) | [**Algorithm Documentation**](https://github.com/elalish/manifold/wiki/Manifold-Library) | [**Web Examples**](https://elalish.github.io/manifold/model-viewer.html)
+[**API Documentation**](https://elalish.github.io/manifold/docs/html/modules.html) | [**Algorithm Documentation**](https://github.com/elalish/manifold/wiki/Manifold-Library) | [**Blog Posts**](https://elalish.blogspot.com/search/label/Manifold) | [**Web Examples**](https://elalish.github.io/manifold/model-viewer.html)
 
 [Manifold](https://github.com/elalish/manifold) is a geometry library dedicated to creating and operating on manifold triangle meshes. A [manifold mesh](https://github.com/elalish/manifold/wiki/Manifold-Library#manifoldness) is a mesh that represents a solid object, and so is very important in manufacturing, CAD, structural analysis, etc. Further information can be found on the [wiki](https://github.com/elalish/manifold/wiki/Manifold-Library).
 
-This is a modern C++ library that Github's CI verifies builds and runs on a variety of platforms. Additionally, we build bindings for JavaScript ([manifold-3d](https://www.npmjs.com/package/manifold-3d) on npm) and Python to make this library more portable and easy to use. We have only three dependencies (all built-in header-only libraries): graphlite provides a connected components algorithm, GLM is a compact vector library, and Thrust is Nvidia's parallel algorithms library (basically a superset of C++17 std::parallel_algorithms). We make use of submodules to ensure compatibility. 
+This is a modern C++ library that Github's CI verifies builds and runs on a variety of platforms. Additionally, we build bindings for JavaScript ([manifold-3d](https://www.npmjs.com/package/manifold-3d) on npm), Python, and C to make this library more portable and easy to use. 
+
+We have four core dependencies, making use of submodules to ensure compatibility: 
+- `graphlite`: connected components algorithm
+- `Clipper2`: provides our 2D subsystem
+- `GLM`: a compact vector library
+- `Thrust`: Nvidia's parallel algorithms library (basically a superset of C++17 std::parallel_algorithms)
 
 ## What's here
 
 This library is fast with guaranteed manifold output. As such you need manifold meshes as input, which this library can create using constructors inspired by the OpenSCAD API, as well as more advanced features like smoothing and signed-distance function (SDF) level sets. You can also pass in your own mesh data, but you'll get an error status if the imported mesh isn't manifold. Various automated repair tools exist online for fixing non manifold models, usually for 3D printing. 
 
 The most significant contribution here is a guaranteed-manifold [mesh Boolean](https://github.com/elalish/manifold/wiki/Manifold-Library#mesh-boolean) algorithm, which I believe is the first of its kind. If you know of another, please open a discussion - a mesh Boolean algorithm robust to edge cases has been an open problem for many years. Likewise, if the Boolean here ever fails you, please submit an issue! This Boolean forms the basis of a CAD kernel, as it allows simple shapes to be combined into more complex ones.
 
-To aid in speed, this library makes extensive use of parallelization, generally through Nvidia's Thrust library. You can switch between the CUDA, OMP and serial C++ backends by setting a CMake flag. Not everything is so parallelizable, for instance a [polygon triangulation](https://github.com/elalish/manifold/wiki/Manifold-Library#polygon-triangulation) algorithm is included which is serial. Even if compiled for CUDA, the code will still run without a GPU, falling back to the serial version of the algorithms. The WASM build is serial-only for now, but still fast.
+To aid in speed, this library makes extensive use of parallelization, generally through Nvidia's Thrust library. You can switch between the CUDA, OMP, TBB, and serial C++ backends by setting a CMake flag. Not everything is so parallelizable, for instance a [polygon triangulation](https://github.com/elalish/manifold/wiki/Manifold-Library#polygon-triangulation) algorithm is included which is serial. Even if compiled for CUDA, the code will still run without a GPU, falling back to the serial version of the algorithms. The WASM build is serial-only for now, but still fast.
 
 Look in the [samples](https://github.com/elalish/manifold/tree/master/samples) directory for examples of how to use this library to make interesting 3D models. You may notice that some of these examples bare a certain resemblance to my OpenSCAD designs on [Thingiverse](https://www.thingiverse.com/emmett), which is no accident. Much as I love OpenSCAD, my library is dramatically faster and the code is more flexible.