operator fusion doc

doc/source/getting_started/installation.rst

@@ -34,6 +34,7 @@ an older version of pandas, you should either upgrade your pandas or downgrade X
 ======= =================== ======== ========= ========== =========== ===========
 Xorbits Python              `NumPy`_ `pandas`_ `xgboost`_ `lightgbm`_ `datasets`_
 ======= =================== ======== ========= ========== =========== ===========
+0.8.2   3.9,3.10,3.11,3.12  2.2.1    2.2.3     2.1.3      4.5.0       3.2.0
 0.8.1   3.9,3.10,3.11,3.12  2.1.3    2.2.3     2.1.3      4.5.0       3.1.0
 0.8.0   3.9,3.10,3.11,3.12  2.1.3    2.2.3     2.1.2      4.5.0       3.1.0
 0.7.4   3.9,3.10,3.11       1.26.4   2.2.3     2.1.1      4.5.0       3.0.1
@@ -67,6 +68,7 @@ For example:
 ======= =================== ======== =========
 Xorbits Python              `CuPy`_  `cuDF`_  
 ======= =================== ======== =========
+0.8.2   3.10,3.11,3.12      13.3.0    24.10   
 0.8.1   3.10,3.11,3.12      13.3.0    24.10   
 ======= =================== ======== =========
 

doc/source/user_guide/best_practices.rst

@@ -14,3 +14,4 @@ practices, and helps users solve some common problems.
    loading_data
    storage_backend
    chunking
+   operator_fusion

doc/source/user_guide/operator_fusion.rst

@@ -0,0 +1,34 @@
+.. _operator_fusion:
+
+===============
+Operator Fusion
+===============
+
+Xorbits implements operator fusion optimization to reduce memory access overhead and improve computational efficiency. 
+The fusion engine combines multiple nearby operators into a single fused one. Rather than implementing our own fusion 
+engine from scratch, Xorbits leverages existing state-of-the-art fusion engines: NumExpr, JAX, or CuPy. Operator Fusion
+is available automatically when one of the fusion packages is installed in your Python environment. Operator fusion is 
+especially effective for ``xorbits.numpy``.
+
+How It Works
+-----------
+
+The optimization process works as follows:
+
+1. Identifies sequences of operations that can be fused together. 
+
+Note that NumExpr, JAX, and CuPy are single-machine fusion engines, while Xorbits is a distributed toolkit. 
+Xorbits will check which operations can be fused. For example, operations like single-axis reduction (``len(op.axis) == 1``
+for ``xorbits.numpy.sum()`` or ``xorbits.numpy.max()``) can be fused, while other reduction operations are not.
+
+2. Groups compatible operations into a single fused operation.
+
+3. Executes the fused operation using the appropriate fusion engines (JAX, NumExpr, or CuPy).
+
+This optimization reduces:
+
+* Memory allocation/deallocation overhead
+* Data movement between operations
+
+The fusion can optimize chains of element-wise operations and simple reductions, 
+where memory bandwidth is often the bottleneck rather than computational intensity.