IntelPython · samaid · May 11, 2023 · May 11, 2023
diff --git a/README.md b/README.md
@@ -22,3 +22,43 @@ The following command will run the very first example of using **Data Parallel E
 ```
 python ./examples/01-hello_dpnp.py
 ```
+## Tutorials
+Jupyter Notebook-based Getting Started tutorials are located in `./notebooks` directory.
+
+To run the tutorial, in the command line prompt type:
+```
+jupyter notebook
+```
+This will print some information about the notebook server in your terminal, including the URL of the web application (by default, `http://localhost:8888`):
+
+```
+
+$ jupyter notebook
+[I 08:58:24.417 NotebookApp] Serving notebooks from local directory: /Users/catherine
+[I 08:58:24.417 NotebookApp] 0 active kernels
+[I 08:58:24.417 NotebookApp] The Jupyter Notebook is running at: http://localhost:8888/
+[I 08:58:24.417 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).
+```
+
+It will then open your default web browser to this URL.
+
+When the notebook opens in your browser, you will see the **Notebook Dashboard**, which will show a list of the notebooks, files, and subdirectories in the directory where the notebook server was started. Navigate to the notebook of your interest and open it in the dashboard.
+
+For more information please refer to [Jupyter documentation](https://docs.jupyter.org/en/latest/running.html)
+
+## Benchmarks
+Data Parallel Extensions for Python provide a set of benchmarks illustrating different aspects of implementing the performant code with Data Parallel Extensions for Python. 
+Benchmarks represent some real life numerical problem or some important part (kernel) of real life application. Each application/kernel is implemented in several variants (not necessarily all variants):
+- Pure Python: Typically the slowest and used just as a reference implementation
+- `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
+- `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-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` kernel: Kernel-style programming, which is close to `@cuda.jit` programming model used in vanilla 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.
+
+For more details please refer to `dpbench` [documentation](https://github.com/IntelPython/dpbench/blob/main/README.md).