add eggpu doc

Author: jessicawwen

docs/_images/eggpu_architecture.png

@@ -7,9 +7,10 @@ Overview
 EGGPU is a GPU-accelerated network analysis library that supports essential functions such as betweenness centrality, k-core centrality, and single-source shortest path. Built on top of the **EasyGraph** library, EGGPU delivers a user-friendly Python API while achieving remarkable speedups for large-scale network analysis.
 
 EGGPU is engineered with a three-layer architecture:
-- User Interface Layer: Developed in Python, this layer offers intuitive and easy-to-use APIs for end users.
-- Middleware Layer: Constructed in C++, this layer shares memory space with the Computation Layer and serves as the binding agent. It also provides a graph container responsible for graph loading, storage, and format conversion.
-- Computation Layer: Implemented in CUDA C/C++, this layer primarily executes the GPU-based network analysis functions, including betweenness centrality, k-core centrality, and SSSP.
+* User Interface Layer: Developed in Python, this layer offers intuitive and easy-to-use APIs for end users.
+* Middleware Layer: Constructed in C++, this layer shares memory space with the Computation Layer and serves as the binding agent. It also provides a graph container responsible for graph loading, storage, and format conversion.
+* Computation Layer: Implemented in CUDA C/C++, this layer primarily executes the GPU-based network analysis functions, including betweenness centrality, k-core centrality, and SSSP.
+
 .. image:: eggpu_architecture.png
 
 Installation

docs/_sources/eggpu.rst.txt

@@ -84,10 +84,10 @@ <h1>EGGPU<a class="headerlink" href="#eggpu" title="Permalink to this heading">
 <h2>Overview<a class="headerlink" href="#overview" title="Permalink to this heading"></a></h2>
 <p>EGGPU is a GPU-accelerated network analysis library that supports essential functions such as betweenness centrality, k-core centrality, and single-source shortest path. Built on top of the <strong>EasyGraph</strong> library, EGGPU delivers a user-friendly Python API while achieving remarkable speedups for large-scale network analysis.</p>
 <p>EGGPU is engineered with a three-layer architecture:
-- User Interface Layer: Developed in Python, this layer offers intuitive and easy-to-use APIs for end users.
-- Middleware Layer: Constructed in C++, this layer shares memory space with the Computation Layer and serves as the binding agent. It also provides a graph container responsible for graph loading, storage, and format conversion.
-- Computation Layer: Implemented in CUDA C/C++, this layer primarily executes the GPU-based network analysis functions, including betweenness centrality, k-core centrality, and SSSP.
-.. image:: eggpu_architecture.png</p>
+* User Interface Layer: Developed in Python, this layer offers intuitive and easy-to-use APIs for end users.
+* Middleware Layer: Constructed in C++, this layer shares memory space with the Computation Layer and serves as the binding agent. It also provides a graph container responsible for graph loading, storage, and format conversion.
+* Computation Layer: Implemented in CUDA C/C++, this layer primarily executes the GPU-based network analysis functions, including betweenness centrality, k-core centrality, and SSSP.</p>
+<img alt="_images/eggpu_architecture.png" src="_images/eggpu_architecture.png" />
 </section>
 <section id="installation">
 <h2>Installation<a class="headerlink" href="#installation" title="Permalink to this heading"></a></h2>