.. because the cache gives you better single threaded performance?
Typically, those would be sequential algorithms with large memory needs and very random (think: hash table) memory accesses.
Examples: SAT solvers, anything relying on sparse linear algebra
Users might realize how many of their cores and cycles are being effectively wasted by limits of the memory / cache hierarchy, and stop thinking of their workloads as “CPU bound”.
I appreciate it’s a complicated and subjective measurement: Hyperthreading, superscalar, out-of-order all mean that a core can be operating at some fraction of its peak (and what does that mean, exactly?) due to memory stalls, vs. being completely idle. And reads meet the instruction pipeline in a totally different way than writes do.
But a synthesized approximation that could serve as the memory stall equivalent of -e cycles for perf would be a huge boon to performance analysis & optimization.