Computer Science · Digital & Computing
Cache Hit Rate Calculator
Calculates cache hit rate, miss rate, and effective memory access time (EMAT) from hit/miss counts and access latencies.
Calculator
Formula
H = number of cache hits; M = number of cache misses; h = hit rate (fraction, 0–1); T_{cache} = cache access time (ns); T_{mem} = main memory access time (ns). EMAT is the Effective Memory Access Time, representing the average time per memory reference weighted by hit and miss probabilities.
Source: Patterson & Hennessy, 'Computer Organization and Design', 5th Edition, Chapter 5: Memory Hierarchy.
How it works
Modern processors operate orders of magnitude faster than main memory (DRAM). To bridge this speed gap, systems use a hierarchy of small, fast cache memories (L1, L2, L3) that store recently or frequently accessed data. When the CPU requests data, it first checks the cache. A cache hit occurs when the data is found there; a cache miss occurs when it is not, forcing a slower fetch from main memory. The hit rate — the fraction of total accesses that result in hits — is the primary metric for evaluating cache effectiveness. A hit rate of 95% means only 1 in 20 requests goes to slow main memory, dramatically reducing average latency.
The core formula has two parts. The hit rate is simply H / (H + M), where H is the hit count and M is the miss count. The miss rate is its complement: 1 − h. The Effective Memory Access Time (EMAT) combines both latencies into a single weighted average: EMAT = h × T_cache + (1 − h) × T_mem. T_cache is the cache access latency (typically 1–10 ns for L1/L2), and T_mem is the main memory latency (typically 60–100 ns). This formula assumes a simultaneous or hierarchical lookup model; some texts use a slightly different form for stall-based models that add miss-penalty cycles instead.
Practical applications span nearly every area of systems engineering. CPU architects use hit rate targets (often 95–99% for L1) when sizing cache. Database administrators profile buffer pool hit rates to determine if more RAM would reduce disk I/O. Web engineers monitor CDN cache hit ratios to reduce origin server load. Application developers use profiling tools (like Intel VTune or Linux perf) to identify data structures causing excessive L1/L2 misses, then restructure memory layouts — a technique called cache-friendly programming or data-oriented design — to improve spatial and temporal locality.
Worked example
Suppose a processor executes a workload that makes 9,500 cache hits and 500 cache misses out of 10,000 total memory accesses. The cache access latency is 2 ns and main memory latency is 100 ns.
Step 1 — Total accesses: 9,500 + 500 = 10,000 accesses
Step 2 — Hit rate: 9,500 / 10,000 = 0.95 (95%)
Step 3 — Miss rate: 500 / 10,000 = 0.05 (5%)
Step 4 — EMAT: EMAT = 0.95 × 2 ns + 0.05 × 100 ns = 1.90 ns + 5.00 ns = 6.90 ns
Compare this to the 100 ns cost of going to main memory every time — the cache reduces the effective access time by over 93%. Now suppose we optimize the code to raise the hit rate to 99%: EMAT = 0.99 × 2 + 0.01 × 100 = 1.98 + 1.00 = 2.98 ns — a further 57% improvement in effective latency, demonstrating why squeezing out the last few percent of hit rate matters enormously in high-performance systems.
Limitations & notes
This calculator uses the simultaneous-access EMAT model, which assumes hit/miss time can be linearly combined. Real processors often use a hierarchical model where a miss incurs the cache lookup time plus the memory penalty, giving EMAT = T_cache + (1 − h) × T_penalty. Always verify which model your architecture uses. The formula also assumes a single cache level; multi-level hierarchies (L1/L2/L3) require cascading EMAT calculations. Hit/miss counts gathered from hardware performance counters may include speculative accesses, prefetches, or hardware-prefetcher hits that inflate the apparent hit rate without representing true workload behavior. Finally, EMAT is an average — it masks variance in access latency, which matters for real-time and latency-sensitive systems where worst-case miss storms (e.g., cold starts or cache thrashing) are the actual bottleneck.
Frequently asked questions
What is a good cache hit rate?
For CPU L1 caches, a hit rate above 95% is generally considered good, with well-optimized workloads achieving 98–99%. L2 and L3 caches have lower hit rates since they only see misses from the previous level. For web CDN or database buffer caches, target hit rates depend heavily on working set size but are typically set above 90% for cost efficiency.
What is the difference between hit rate and hit ratio?
Hit rate and hit ratio are the same metric — both refer to the fraction (or percentage) of total accesses that result in a cache hit. Hit rate is sometimes expressed as a decimal (0.95) and hit ratio as a percentage (95%), but they describe identical quantities. The term 'hit ratio' is common in older literature and database contexts.
How does cache size affect hit rate?
Larger caches can hold more of the working set, reducing compulsory and capacity misses. However, the relationship is not linear — doubling cache size rarely doubles the hit rate. The improvement follows a diminishing-returns curve described by stack distance profiles. Beyond a certain size, further increases yield negligible hit rate gains because conflict and compulsory misses dominate.
What causes cache misses?
Cache misses are categorized into the '3 Cs': Compulsory (cold) misses on first access to a block, Capacity misses when the working set exceeds cache size, and Conflict misses when too many addresses map to the same cache set. A fourth C — Coherence misses — occurs in multi-processor systems when cache lines are invalidated due to writes by other cores.
How do I calculate EMAT for a two-level cache hierarchy?
For two cache levels (L1 and L2), the cascading formula is: EMAT = h1 × T_L1 + (1 − h1) × [h2 × T_L2 + (1 − h2) × T_mem], where h1 and h2 are the local hit rates of L1 and L2 respectively. This can be extended to three or more levels by nesting additional terms. Each level's hit rate is measured against accesses that reached that level, not total accesses.
Last updated: 2025-01-15 · Formula verified against primary sources.