cuda-basics

GPU thread hierarchy organizes parallel execution using thread blocks and grids. Proper grid and block configuration ensures correct indexing and maximizes hardware utilization for common parallel kernels.

The CUDA thread hierarchy organizes parallel execution using thread blocks and grids. Proper grid and block configuration ensures correct indexing and maximizes hardware utilization for common parallel kernels.

Basic C/C++ knowledge and a CUDA toolkit are required to start programming GPU kernels. These prerequisites provide the foundational syntax and compilation environment needed to build and execute parallel code.

Writing a simple CUDA kernel involves defining a parallel function and configuring grid dimensions. You execute the vector addition kernel and measure memory throughput to evaluate GPU performance.

Data movement optimization is important because GPU memory bandwidth is a primary bottleneck. Minimizing data transfers between host and device and efficiently utilizing memory hierarchies significantly improves parallel execution speed.

Common CUDA optimization techniques include memory coalescing, utilizing shared memory, and proper thread block configuration. Applying these basic patterns reduces latency and accelerates simple parallel workloads effectively.