Fixing docs

Author: samaid

docs/sources/benchmarks.rst

@@ -14,45 +14,27 @@ Each application/kernel is implemented in several variants (not necessarily all
 
 - ``numpy``: Same application/kernel implemented using NumPy library
 
-- ``dpnp``: Modified numpy implementation to run on a specific device. You can use numpy as a
-baseline while evaluating the dpnp implementation and its performance
+- ``dpnp``: Modified numpy implementation to run on a specific device. You can use numpy as a baseline while evaluating the dpnp implementation and its performance
 
-- ``numba @njit`` array-style: application/kernel implemented using NumPy and compiled with Numba.
-You can use numpy as a baseline when evaluate numba @njit array-style implementat and its performance
+- ``numba @njit`` array-style: application/kernel implemented using NumPy and compiled with Numba. You can use numpy as a baseline when evaluate numba @njit array-style implementat and its performance
 
-- ``numba @njit`` direct loops (prange): Same application/kernel implemented using Numba
-compiler using direct loops. Sometimes array-style programming is cumbersome and
-performance inefficient. Using direct loop programming may lead to more
-readable and performance code. Thus, while evaluating the performance of direct loop
-implementation it is useful to compare array-style Numba implementation as a baseline
+- ``numba @njit`` direct loops (prange): Same application/kernel implemented using Numba compiler using direct loops. Sometimes array-style programming is cumbersome and performance inefficient. Using direct loop programming may lead to more readable and performance code. Thus, while evaluating the performance of direct loop implementation it is useful to compare array-style Numba implementation as a baseline
 
-- ``numba-dpex @dpjit`` array-style: Modified numba @njit array-style implementation to
-compile and run on a specific device. You can use vanilla Numba implementation as
-a baseline while comparing numba-dpex implementation details and performance.
-You can also compare it against dpnp implementation to see how much extra performance
-numba-dpex can bring when you compile NumPy code for a given device
+- ``numba-dpex @dpjit`` array-style: Modified numba @njit array-style implementation to compile and run on a specific device. You can use vanilla Numba implementation as a baseline while comparing numba-dpex implementation details and performance. You can also compare it against dpnp implementation to see how much extra performance numba-dpex can bring when you compile NumPy code for a given device
 
-- ``numba-dpex @dpjit`` direct loops (prange): Modified numba @njit direct loop implementation
-to compile and run on a specific device. You can use vanilla Numba implementation
-as a baseline while comparing numba-dpex implementation details and performance.
-You can also compare it against dpnp implementation to see how much extra performance
-numba-dpex can bring when you compile NumPy code for a given device
+- ``numba-dpex @dpjit`` direct loops (prange): Modified numba @njit direct loop implementation to compile and run on a specific device. You can use vanilla Numba implementation as a baseline while comparing numba-dpex implementation details and performance. You can also compare it against dpnp implementation to see how much extra performance numba-dpex can bring when you compile NumPy code for a given device
 
-- ``numba-dpex @dpjit`` kernel: Kernel-style programming, which is close to @cuda.jit
-programming model used in vanilla Numba
+- ``numba-dpex @dpjit`` kernel: Kernel-style programming, which is close to @cuda.jit programming model used in vanilla Numba
 
-- ``numba-mlir``: Array-style, direct loops and kernel-style implementations
-for experimental MLIR-based backend for Numba
+- ``numba-mlir``: Array-style, direct loops and kernel-style implementations for experimental MLIR-based backend for Numba
 
 - ``cupy``: NumPy-like implementation using CuPy to run on CUDA-compatible devices
 
 - ``@cuda.jit``: Kernel-style Numba implementation to run on CUDA-compatible devices
 
-- Native SYCL: Most applications/kernels also have DPC++ implementation, which
-can be used to compare performance of above implementations to DPC++ compiled code.
+- Native SYCL: Most applications/kernels also have DPC++ implementation, which can be used to compare performance of above implementations to DPC++ compiled code.
 
-These benchmarks are implemented in ``dpbench`` framework, which allows you to run
-all or select benchmarks and variants to evaluate their performance on different hardware.
+These benchmarks are implemented in ``dpbench`` framework, which allows you to run all or select benchmarks and variants to evaluate their performance on different hardware.
 
 For more details please refer to ``dpbench``
 `documentation <https://github.com/IntelPython/dpbench/blob/main/README.md>`_.

docs/sources/demos.rst

@@ -6,14 +6,11 @@ Demos
 
 There are several demo applications illustrating the power of the **Data Parallel Extensions for Python**. They are:
 
-- `Monte Carlo Pi <https://github.com/IntelPython/DPEP/tree/main/demos/mcpi>`_ -
-The Monte Carlo method to estimate the value of :math:`\pi`.
+- `Monte Carlo Pi <https://github.com/IntelPython/DPEP/tree/main/demos/mcpi>`_ - The Monte Carlo method to estimate the value of :math:`\pi`.
 
-- `Mandelbrot Set <https://github.com/IntelPython/DPEP/tree/main/demos/mandelbrot>`_ -
-Visualization of the breathtaking process of diving in the famous Mandelbrot fractal
+- `Mandelbrot Set <https://github.com/IntelPython/DPEP/tree/main/demos/mandelbrot>`_ - Visualization of the breathtaking process of diving in the famous Mandelbrot fractal
 
-- `Game of Life <https://github.com/IntelPython/DPEP/tree/main/demos/game-of-life>`_ -
-Visualization of the life evolution using famous Conway's model
+- `Game of Life <https://github.com/IntelPython/DPEP/tree/main/demos/game-of-life>`_ - Visualization of the life evolution using famous Conway's model
 
 All demos are located in the `GitHub repository <https://github.com/IntelPython/DPEP/tree/main/demos>`_.
 For more details please refer to the documentation located in the individual demo directory.