Document compute sanitizer memcheck support [skipci]

README.md

@@ -556,6 +556,22 @@ Note that debug logging is different from the CSV memory allocation logging prov
 `rmm::mr::logging_resource_adapter`. The latter is for logging a history of allocation /
 deallocation actions which can be useful for replay with RMM's replay benchmark.
 
+## RMM and CUDA Memory Bounds Checking
+
+Memory allocations taken from a memory resource that allocates a pool of memory (such as
+`pool_memory_resource` and `arena_memory_resource`) are part of the same low-level CUDA memory 
+allocation. Therefore, out-of-bounds or misaligned accesses to these allocations are not likely to 
+be detected by CUDA tools such as 
+[CUDA Compute Sanitizer](https://docs.nvidia.com/cuda/compute-sanitizer/index.html) memcheck.
+
+Exceptions to this are `cuda_memory_resource`, which wraps `cudaMalloc`, and 
+`cuda_async_memory_resource`, which uses `cudaMallocAsync` with CUDA's built-in memory pool 
+functionality (CUDA 11.2 or later required). Illegal memory accesses to memory allocated by these 
+resources are detectable with Compute Sanitizer Memcheck.
+
+It may be possible in the future to add support for memory bounds checking with other memory 
+resources using NVTX APIs.
+
 ## Using RMM in Python Code
 
 There are two ways to use RMM in Python code: