This paper presents CAMP, a Configurable App for Memory Probing that facilitates assessment of intra-node deep memory hierarchies through performance measurements of synthetic kernels. The open-source tool provides several different access patterns and allows to vary kernels’ operational intensities. We introduce the supported features, their design and implementation, and analyse measurements for a deep memory hierarchy on a compute node of ARCHER2, the UK national supercomputer and compare it to measurements on a compute node on NEXTGenIO. We report results of a strong scaling study of contiguous, strided and stencil access patterns for various operational intensities and explore several thread placement options and data sizes. The results confirm that bandwidth saturation can be achieved with a relatively small number of threads on AMD Rome and that underpopulation may be beneficial as performance drops when the node is fully populated for configurations with lower operational intensity, whilst the effect is less pronounced on the less hierarchical Intel Cascade Lake. Finally we discuss potential benefits of sub-NUMA-node awareness and mention several suggestions for extending CAMP.