Add GPU programming patterns tutorials (#1918)

Update projects/hip/docs/tutorial/programming-patterns/atomic_operations_histogram.rst


WIP

Co-authored-by: Julia Jiang <56359287+jujiang-del@users.noreply.github.com>

projects/hip/docs/tutorial/programming-patterns.rst

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+.. meta::
+  :description: GPU programming patterns and tutorials
+  :keywords: AMD, ROCm, HIP, GPU, programming patterns, parallel computing, tutorial
+
+.. _gpu_programming-patterns:
+
+********************************************************************************
+GPU programming patterns
+********************************************************************************
+
+GPU programming patterns are fundamental algorithmic structures that enable
+efficient parallel computation on GPUs. Understanding these
+patterns is essential for developers looking to effectively harness the massive parallel
+processing capabilities of modern GPUs for scientific computing, machine learning,
+image processing, and other computationally intensive applications.
+
+These tutorials describe core programming patterns demonstrating how to
+efficiently implement common parallel algorithms using the HIP runtime API and
+kernel extensions. Each pattern addresses a specific computational challenge and
+provides practical implementations with detailed explanations.
+
+Common GPU programming challenges
+==================================
+
+GPU programming introduces unique challenges not present in traditional CPU
+programming:
+
+* **Memory coherence**: GPUs lack robust cache coherence mechanisms, requiring
+  careful coordination when multiple threads access shared memory.
+
+* **Race conditions**: Concurrent memory access requires atomic operations or
+  careful algorithm design.
+
+* **Irregular parallelism**: Real-world algorithms often have varying amounts of
+  parallel work across iterations.
+
+* **CPU-GPU communication**: Data transfer overhead between host and device must
+  be minimized.
+
+Tutorial overview
+=================
+
+This collection provides comprehensive tutorials on essential GPU programming
+patterns:
+
+* :doc:`Two-dimensional kernels <./programming-patterns/matrix_multiplication>`:
+  Processing grid-structured data such as matrices and images.
+
+* :doc:`Stencil operations  <./programming-patterns/stencil_operations>`:
+  Updating array elements based on neighboring values.
+
+* :doc:`Atomic operations <./programming-patterns/atomic_operations_histogram>`:
+  Ensuring data integrity during concurrent memory access.
+
+* :doc:`Multi-kernel applications <./programming-patterns/multikernel_bfs>`:
+  Coordinating multiple GPU kernels to solve complex problems.
+
+* :doc:`CPU-GPU cooperation <./programming-patterns/cpu_gpu_kmeans>`: Strategic
+  work distribution between CPU and GPU.
+
+Prerequisites
+-------------
+
+To get the most from these tutorials, you should have:
+
+* Basic understanding of C/C++ programming.
+
+* Familiarity with parallel programming concepts.
+
+* HIP runtime environment installed (see :doc:`../install/install`).
+
+* Basic knowledge of GPU architecture (recommended).
+
+Getting started
+---------------
+
+Each tutorial is self-contained and can be studied independently, though we
+recommend following the order presented for a comprehensive understanding:
+
+1. **Start with Two-dimensional kernels** to understand basic GPU thread
+   organization and memory access patterns.
+2. **Progress to stencil operations** to learn about neighborhood dependencies.
+3. **Study atomic operations** to understand concurrent memory access.
+4. **Explore multi-kernel programming** for complex algorithmic patterns.
+5. **Check CPU-GPU cooperation** to handle mixed-parallelism workloads.