To address the memory-bound nature of the FSBM scheme and improve performance on GPUs, several strategies can be employed:
Data Locality Optimization: Enhancing data locality by restructuring data access patterns can significantly reduce memory latency. This can be achieved by ensuring that data accessed together is stored contiguously in memory, thereby improving cache utilization.
Reducing Memory Footprint: Minimizing the number of temporary arrays and global variables can help reduce memory usage. Instead of using large arrays for collision processes, as demonstrated in the study, functions can be created to compute values on-the-fly, which can save memory and improve performance.
Using Shared Memory: Leveraging shared memory on GPUs can provide faster access to frequently used data. By storing critical data in shared memory, the number of global memory accesses can be reduced, leading to improved performance.
Optimizing Data Transfers: Implementing explicit data transfer management using OpenMP directives can help minimize unnecessary data transfers between the host and device. This includes using map clauses effectively to control which data is transferred and when.
Kernel Fusion: Combining multiple kernels into a single kernel can reduce the overhead associated with launching multiple kernels and improve memory access patterns. This technique can be particularly effective in reducing the number of global memory accesses.
Profiling and Tuning: Continuous profiling using tools like NVIDIA Nsight Compute can help identify memory bottlenecks and guide further optimizations. Tuning kernel parameters, such as block size and grid size, can also lead to better memory performance.