Hello CUDA community,
I’m struggling with a concept in CUDA programming. I’ve noticed that when implementing iterative algorithms, we can’t read from and write to the same global memory array within a single kernel execution. Instead, we need to use a ping-pong buffering technique (two arrays - one for reading and one for writing).
I understand this is required for correctness, but I’d appreciate if someone could explain:
- Why exactly does CUDA prohibit reading and writing to the same global memory location?
- Is this related to the CUDA execution model with multiple threads?
- Are there any workarounds besides ping-pong buffering when implementing iterative algorithms?
I’ve implemented ping-pong buffering (swapping input/output arrays between kernel calls), but I’d like to understand the underlying reasons better.
CUDA does not prohibit reading from and writing to the same memory location in a kernel.
Using a ping-pong buffer is a simple way to ensure that data which need to be read by one cuda thread is not changed by a write from a different cuda thread. The alternative would be to synchronize the threads between read access and write access.
“Is there a sequential execution order within each warp, where faster warps compute their results first, causing slower warps to be affected? Could this be why modifying and then writing back to the same array causes a multi-fold increase in execution time? Might it be related to cache coherency issues, where some warps modify values in the cache first, triggering cache coherency protocols that generate substantial message traffic to synchronize cache values?”
There is a whole discipline or at least style of value oriented programming, where you never modify structures, but clearly separate input and output. This is also done in functional programming (pure functions).
It has huge advantages for parallelization and memory performance, areas which are important in Cuda programming.
There are instructions for memory barriers that allow you to modify structures from different threads and blocks, so perhaps those were missing in your program.
My advisor mainly asked me to explain the principle. Haha, it’s confirmed that the need for spontaneous synchronization is caused by different threads executing in different orders, not the communication time spent due to cache coherence protocols triggered by cache changes.