Update docs/understand/programming_model.rst

Author: randyh62

docs/understand/programming_model.rst

@@ -132,7 +132,7 @@ In heterogeneous programming, the CPU is available for processing operations but
 
 1.	Initialize the HIP runtime and select the GPU: As described in :ref:`initialization`, refers to identifying and selecting a target GPU, setting up a context to let the CPU interact with the GPU.  
 2.	Data preparation: As discussed in :ref:`memory_management`, this includes allocating the required memory on the host and device, preparing input data and transferring it from the host to the device. The data is both transferred to the device, and passed as an input parameter when launching the kernel. 
-3.	Configure and launch the kernel on the GPU: As described in :ref:`device_program`, this defines kernel configurations and arguments, launches kernel to run on the GPU device using the triple chevron syntax or appropriate API call (for example ``hipLaunchKernelGGL``). On the GPU, multiple kernels can run on streams, with a queue of operations. Within the same stream, operations run in the order they were issued, but multiple streams are independent and can execute concurrently. In the HIP runtime, kernels run on the default stream when one is not specified, but specifying a stream for the kernel lets you increase concurrency in task scheduling and resource utilization, and launch and manage multiple kernels from the host program.
+3.	Configure and launch the kernel on the GPU: As described in :ref:`device_program`, this defines kernel configurations and arguments, launches kernel to run on the GPU device using the triple chevron syntax or appropriate API call (for example ``hipLaunchKernelGGL``). On the GPU, multiple kernels can run on streams, with a queue of operations. Within the same stream, operations run in the order they were issued, but on multiple streams operations are independent and can execute concurrently. In the HIP runtime, kernels run on the default stream when one is not specified, but specifying a stream for the kernel lets you increase concurrency in task scheduling and resource utilization, and launch and manage multiple kernels from the host program.
 4.	Synchronization: As described in :ref:`asynchronous_how-to`, kernel execution occurs in the context of device streams, specifically the default (`0`) stream. You can use streams and events to manage task dependencies, overlap computation with data transfers, and manage asynchronous processes to ensure proper sequencing of operations. Wait for events or streams to finish execution and transfer results from the GPU back to the host.
 5.	Error handling: As described in :ref:`error_handling`, you should catch and handle potential errors from API calls, kernel launches, or memory operations. For example, use ``hipGetErrorString`` to retrieve error messages.
 6.	Cleanup and resource management: Validate results, clean up GPU contexts and resources, and free allocated memory on the host and devices.