Merge branch 'branch-25.04' into branch-25.06-merge-25.04

Author: nv-rliu

README.md

@@ -1,44 +1,62 @@
-# nx-cugraph
+# <div align="left"><img src="img/rapids_logo.png" width="90px"/>&nbsp;nx-cugraph - GPU Backend for NetworkX</div>
 
 ## Description
-[RAPIDS](https://rapids.ai) nx-cugraph is a [backend to NetworkX](https://networkx.org/documentation/stable/backends.html)
-to run supported algorithms with GPU acceleration.
+[nx-cugraph](https://rapids.ai/nx-cugraph) is a [backend to NetworkX](https://networkx.org/documentation/stable/backends.html) to run algorithms with zero code change GPU acceleration.
+
+- [System Requirements](#system-requirements)
+- [Installation](#installation)
+- [Enabling nx-cugraph](#enabling-nx-cugraph)
+- [Supported Algorithms](#supported-algorithms)
+
+---
 
 ## System Requirements
 
-nx-cugraph requires the following:
- * NVIDIA GPU, Volta architecture or later, with [compute capability](https://developer.nvidia.com/cuda-gpus) 7.0+
- * CUDA 11.4-11.8 or 12.0-12.5
- * Python version 3.10, 3.11, or 3.12
- * NetworkX >= version 3.2 (version 3.4 or higher recommended)
+ * **GPU:** NVIDIA Volta architecture or later, with [compute capability](https://developer.nvidia.com/cuda-gpus) 7.0+
+   * Pascal GPU support was [removed in 24.02](https://docs.rapids.ai/notices/rsn0034/). Compute capability 7.0+ is required for RAPIDS 24.02 and later.
+ * **CUDA Version:** 11.4 - 11.8 or 12.0 - 12.5
+ * **Python Version:** 3.10, 3.11, or 3.12
+ * **NetworkX Version:** minimum 3.2 (version 3.4 or higher recommended)
+
+Note: nx-cugraph is supported only on Linux, and with Python versions 3.10 and later.
+
+See [RAPIDS System Requirements](https://docs.rapids.ai/install#system-req) for detailed information on OS and Versions.
 
-More details about system requirements can be found in the [RAPIDS System Requirements documentation](https://docs.rapids.ai/install#system-req).
 
 ## Installation
 
-nx-cugraph can be installed using either conda or pip.
+nx-cugraph can be installed using either conda or pip with the following commands.
 
 ### conda
-#### latest nightly version
+
+nx-cugraph can be installed with conda (via [Miniforge](https://github.com/conda-forge/miniforge)) from the `rapidsai` channel.
 ```
-conda install -c rapidsai-nightly -c conda-forge -c nvidia nx-cugraph
+conda install -c rapidsai -c conda-forge -c nvidia nx-cugraph
 ```
-#### latest stable version
+
+We also provide [nightly Conda packages](https://anaconda.org/rapidsai-nightly/nx-cugraph) built from the HEAD of our latest development branch.
 ```
-conda install -c rapidsai -c conda-forge -c nvidia nx-cugraph
+conda install -c rapidsai-nightly -c conda-forge -c nvidia nx-cugraph
 ```
+
 ### pip
-#### latest nightly version
+
+nx-cugraph can be installed via `pip` from the NVIDIA Python Package Index.
+
+#### For CUDA 11.x:
+
+Latest nightly version
 ```
 python -m pip install nx-cugraph-cu11 --extra-index-url https://pypi.anaconda.org/rapidsai-wheels-nightly/simple
 ```
-#### latest stable version
+
+Latest stable version
 ```
 python -m pip install nx-cugraph-cu11 --extra-index-url https://pypi.nvidia.com
 ```
 Notes:
  * The pip example above installs for CUDA 11. To install for CUDA 12, replace `-cu11` with `-cu12`
- * Additional information relevant to installing any RAPIDS package can be found [here](https://rapids.ai/#quick-start).
+ * Try out the [RAPIDS Install Selector Tool](https://docs.rapids.ai/install/#install-rapids) to install other RAPIDS packages.
 
 ## Enabling nx-cugraph
 
@@ -281,4 +299,4 @@ Below is the list of algorithms that are currently supported in nx-cugraph.
 </pre>
 
 To request nx-cugraph backend support for a NetworkX API that is not listed
-above, visit the [nx-cugraph GitHub repo](https://github.com/rapidsai/nx-cugraph).
+above, [file an issue](https://github.com/rapidsai/nx-cugraph/issues/new/choose).

benchmarks/pytest-based/bench_algos.py

@@ -982,6 +982,20 @@ def bench_jaccard(benchmark, graph_obj, backend_wrapper):
     assert type(result) is list
 
 
+def bench_forceatlas2(benchmark, graph_obj, backend_wrapper):
+    G = get_graph_obj_for_benchmark(graph_obj, backend_wrapper)
+
+    result = benchmark.pedantic(
+        target=backend_wrapper(nx.forceatlas2_layout),
+        args=(G,),
+        rounds=rounds,
+        iterations=iterations,
+        warmup_rounds=warmup_rounds,
+    )
+
+    assert type(result) is dict
+
+
 @pytest.mark.skip(reason="benchmark not implemented")
 def bench_complete_bipartite_graph(benchmark, graph_obj, backend_wrapper):
     pass