I'm trying to write a CUDA program for a simulation, but I can't get it to perform very well. I've used the Nvidia NSIGHT compute tool to get profiles, and it's saying that I'm bottlenecked by global reads. What are some things that I can do to fix these bottlenecks?
Here are some things you can try to optimize your CUDA program and reduce the global read bottlenecks:

1. Increase coalescing: Global memory accesses in CUDA are coalesced when they occur in contiguous blocks of memory, so it is important to try to access memory in a contiguous manner to reduce the number of memory transactions.

2. Use shared memory: Shared memory is a type of on-chip memory that is shared between all threads within a block. Using shared memory can significantly improve the performance of your kernel as it allows for faster memory access times compared to global memory.

3. Use texture memory: Texture memory is another type of memory optimized for fast read access. It is best suited for read-only data that is accessed in a scattered pattern. Consider using texture memory for your simulation data if it meets these criteria.

4. Optimize memory access patterns: Try to access memory in a pattern that maximizes coalescing, such as accessing memory along the x-dimension first, then the y-dimension, and finally the z-dimension. This will ensure that all threads in a warp access contiguous memory locations.

5. Use constant memory: Constant memory is a type of memory that is cached on-chip, and is also optimized for read-only access. Consider using constant memory for read-only data that is accessed frequently.

6. Avoid bank conflicts: Shared memory is divided into banks, and accesses to these banks can result in bank conflicts. Bank conflicts can slow down memory access and impact performance. To avoid bank conflicts, try to access shared memory in a strided pattern that evenly distributes accesses across all banks.

7. Consider using Compute Unified Device Architecture (CUDA) streams: CUDA streams allow you to overlap data transfers and computation, which can reduce the overall time required for your simulation.


These are some of the ways to improve the performance of your CUDA program and reduce global read bottlenecks. I hope these tips will help you get the most out of your simulation!