Shared memory and global memory are two distinct types of memory available in CUDA, each with its own performance characteristics and use cases. Understanding their differences is crucial for writing efficient CUDA kernels.
Shared Memory:
1. Description:
- Shared memory is a small, fast, on-chip memory space that is shared by all threads within a thread block. It is located physically close to the processing cores, allowing for very low-latency access.
2. Performance Characteristics:
- Low Latency: Shared memory has significantly lower latency compared to global memory, typically on the order of a few clock cycles.
- High Bandwidth: Shared memory offers much higher bandwidth compared to global memory because it is located on-chip and does not require accessing off-chip DRAM.
- Limited Size: Shared memory has a limited capacity, typically a few tens of kilobytes per thread block.
3. Use Cases:
- Inter-Thread Communication: Shared memory is ideal for communication and data sharing among threads within a thread block.
- Data Reuse: Shared memory can be used to store frequently accessed data, allowing threads to reuse the data without having to access global memory repeatedly.
- Staging Data: Data can be loaded from global memory into shared memory before being processed by threads, and then written back to global memory after processing. This can reduce the number of global memory accesses and improve performance.
- Implementing Local Reductions: Performing reductions (e.g., summing elements in an array) within a thread block can be efficiently done using shared memory to accumulate intermediate results.
- Implementing Caches: Shared memory can be used as a software-managed cache to improve data locality and reduce global memory accesses.
4. Example:
```c++
__global__ void sharedMemoryExample(float *in, float *out) {
__shared__ float sharedData[1....
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