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:
Two-dimensional kernels: Processing grid-structured data such as matrices and images.
Stencil operations: Updating array elements based on neighboring values.
Atomic operations: Ensuring data integrity during concurrent memory access.
Multi-kernel applications: Coordinating multiple GPU kernels to solve complex problems.
CPU-GPU cooperation: 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 Install HIP).
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:
Start with Two-dimensional kernels to understand basic GPU thread organization and memory access patterns.
Progress to stencil operations to learn about neighborhood dependencies.
Study atomic operations to understand concurrent memory access.
Explore multi-kernel programming for complex algorithmic patterns.
Check CPU-GPU cooperation to handle mixed-parallelism workloads.