cachegrind simulates CPU caches (L1 instruction, L1 data, L2, L3) and reports cache miss rates and memory behavior. Like callgrind, it counts deterministically without sampling. Use it to understand cache efficiency and validate optimization strategies.

valgrind --tool=cachegrind ./program
cg_annotate cachegrind.out.12345

cachegrind simulates a CPU cache hierarchy and tracks every load and store, counting hits and misses. The output shows per-function cache statistics—useful for understanding which functions have poor cache behavior.

Output:

I   refs:      1,000,000
I1  misses:        5,000  (0.5%)
LLi misses:        1,000

D   refs:      2,000,000
D1  misses:       50,000  (2.5%)
LLd misses:       10,000

LL refs:        3,000,000
LL misses:      20,000  (0.67%)

“I” = instruction cache, “D” = data cache, “LL” = last-level (L3). The report shows misses as a count and percentage of all accesses.

Per-function annotation:

cg_annotate --auto=yes cachegrind.out.12345

This shows source code (or disassembly) with cache miss counts annotated on each line—revealing exactly which instructions cause cache misses.

Comparing cache profiles: Validate that your optimization actually improved cache behavior:

valgrind --tool=cachegrind --outfile=cache1.out ./prog_v1
valgrind --tool=cachegrind --outfile=cache2.out ./prog_v2
cg_annotate cache1.out cache2.out

This shows side-by-side cache statistics for two versions of the program.

Configuration: By default, cachegrind simulates a typical modern x86 cache (L1i 32KB, L1d 32KB, L2 256KB, L3 8MB). For embedded systems or older CPUs, configure the cache sizes:

valgrind --tool=cachegrind --I1=32,64,64 --D1=32,64,64 --LL=256,4,64 ./program

This sets I1 (instruction cache) to 32KB, 64-byte lines, 64-way associative, and so on.

Advantages: 1. Deterministic: reproducible cache profiles 2. Insightful: per-line cache miss analysis 3. Configurable: simulate different cache architectures

Disadvantages: 1. Simulation: doesn't reflect real CPU prefetching or TLB behavior 2. Slow: 5-20x overhead 3. Not real-world: simulated miss counts may differ from hardware due to prefetching and out-of-order execution

Use cachegrind to understand algorithmic cache behavior and validate optimization strategies. Use perf with cache-related events to measure real hardware cache misses.