changes as per suggestions by @gmarkall

sklam · Mar 23, 2023 · Mar 18, 2023 · Mar 22, 2023 · Mar 22, 2023 · Mar 23, 2023
commit 217ae5cb2abcd4ce552a8f2685259c21fd4d4607
diff --git a/docs/source/cuda/memory.rst b/docs/source/cuda/memory.rst
@@ -159,7 +159,8 @@ traditional dynamic memory management.
 Dynamic Shared Memory
 ---------------------
 
-In order to use dynamic shared memory in kernel code declare shared array of size 0:
+In order to use dynamic shared memory in kernel code declare a shared array of
+size 0:
 
 .. code-block:: python
 
@@ -168,19 +169,21 @@ In order to use dynamic shared memory in kernel code declare shared array of siz
       dyn_arr = cuda.shared.array(0, dtype=np.float32)
       ...
 
-and specify the size of dynamic shared array in bytes during kernel invokation (dispatcher object instantiation):
+and specify the size of dynamic shared array in bytes during kernel invocation:
 
 .. code-block:: python
 
    kernel_func[32, 32, 0, 128](x)
 
-In above code dispatcher object is instantiated with 4 parameters:
+In the above code the kernel launch is configured with 4 parameters:
 
 .. code-block:: python
 
-   kernel_func[grid_dim, block_dim, stream_id, dyn_shared_mem_size]
+   kernel_func[grid_dim, block_dim, stream, dyn_shared_mem_size]
 
-**Note:** that all dynamic shared memory arrays *alias*, so if you want to have multiple dynamic shared arrays, you need to take *disjoint* views of the arrays. For example, consider:
+**Note:** that all dynamic shared memory arrays *alias*, so if you want to have
+multiple dynamic shared arrays, you need to take *disjoint* views of the arrays.
+For example, consider:
 
 .. code-block:: python
 
@@ -198,19 +201,27 @@ In above code dispatcher object is instantiated with 4 parameters:
    f[1, 1, 0, 4]()
    cuda.synchronize()
 
-This allocates 4 bytes of shared memory (large enough for one int32 or one float32) and declares dynamic shared memory arrays of type int32 and of type float32. When `f32_arr[0]` is set, this also sets the value of `i32_arr[0]`, because they're pointing at the same memory. So we see as output:
+This allocates 4 bytes of shared memory (large enough for one ``int32`` or one
+``float32``) and declares dynamic shared memory arrays of type ``int32`` and of
+type ``float32``. When ``f32_arr[0]`` is set, this also sets the value of
+``i32_arr[0]``, because they're pointing at the same memory. So we see as
+output:
 
-.. code-block:: pycon
+.. code-block:: python
 
    3.140000
    1078523331
 
-because `1078523331` is the `int32` represented by the bits of the `float32` IEEE754 `3.14`.
+because 1078523331 is the ``int32`` represented by the bits of the ``float32``
+value 3.14.
 
 If we take disjoint views of the dynamic shared memory:
 
 .. code-block:: python
 
+   from numba import cuda
+   import numpy as np
+
    @cuda.jit
    def f_with_view():
       f32_arr = cuda.shared.array(0, dtype=np.float32)
@@ -223,9 +234,11 @@ If we take disjoint views of the dynamic shared memory:
    f_with_view[1, 1, 0, 8]()
    cuda.synchronize()
 
-This time we declare 8 dynamic shared memory bytes, using the first 4 for a `float32` value and the next 4 for an `int32` value. Now we can set both the `int32` and `float32` value without them aliasing:
+This time we declare 8 dynamic shared memory bytes, using the first 4 for a
+``float32`` value and the next 4 for an ``int32`` value. Now we can set both the
+``int32`` and ``float32`` value without them aliasing:
 
-.. code-block:: pycon
+.. code-block:: python
 
    3.140000
    1