CUDA (Compute Unified Device Architecture) is a parallel computing platform and programming model created by NVIDIA. It allows software developers to use a CUDA-enabled graphics processing unit (GPU) for general purpose processing. This document delves into some of the advanced topics in CUDA.
What is CUDA?
CUDA is designed to make it possible to use a high-performance GPU for general-purpose computing. This is achieved by exposing the parallel processing capabilities of the GPU to the developer, allowing them to leverage the massive parallelism of the GPU to speed up applications.
Key CUDA Concepts
- Kernels: Kernels are the core of CUDA programming. They are functions written in CUDA C/C++ or Fortran and executed on the GPU.
- Threads: CUDA uses a many-core architecture, and each core is called a thread.
- Memory Hierarchy: CUDA has a memory hierarchy similar to CPUs, including global memory, shared memory, and registers.
Performance Optimization
One of the key aspects of CUDA programming is optimizing performance. Here are some tips:
- Memory Access: Access global memory in a coalesced manner to improve bandwidth.
- Thread Synchronization: Use barriers and atomic operations judiciously to synchronize threads.
- Occupancy: Maximize the occupancy of the GPU to ensure efficient use of resources.
Advanced CUDA Features
- Unified Memory: Simplifies memory management by providing a single memory address space accessible by both the CPU and GPU.
- Dynamic Parallelism: Allows kernels to launch new kernels and create new threads dynamically.
- Streams: Enable concurrent execution of multiple kernels and memory transfers.
Example of a CUDA Kernel
__global__ void add(int *a, int *b, int *c) {
int index = threadIdx.x;
c[index] = a[index] + b[index];
}
This kernel adds corresponding elements of arrays a and b, storing the result in array c.
Learn More
For more in-depth information on CUDA, consider reading the following: