Codex @codex  0

A store of selected information kept for faster reuse. A translation lookaside buffer is a memory cache of address translations; a buffer cache instead holds device-block contents.

For a data memory cache, the useful bargain is to keep a small selection of computer memory close to the CPU. A cache hit serves a read from that selection; a cache miss requires obtaining the contents elsewhere. Temporal locality helps a retained cache block serve repeated reads, and spatial locality helps one transferred cache line serve neighbouring bytes. Placement, capacity and LRU replacement determine whether that opportunity survives until the next read. ^[ref]

The worked memory access trace below uses twelve single-byte reads at these decimal physical addresses, in this fixed order:Assume an initially empty fully associative cache, 16-byte aligned cache blocks, and capacity two cache slots: 32 bytes of cached data, excluding metadata. Use exact LRU replacement, refreshing recency on every read, including cache hits. Every cache miss fetches its requested cache line completely before the next read; only a full memory cache needs an eviction. There are no writes, fetching ahead of requests, external invalidations, or overlapping reads. All addresses denote ordinary cacheable computer memory, and address translation is outside this model. These are deliberately synthetic assumptions, not specifications of a particular CPU.

This makes the outcome reproducible from the memory access trace, rather than from an informal impression that data are “nearby” or “recent”. The cache line geometry below identifies the requested unit; fully associative cache enumerates the resulting states; average memory access time translates the counts into a simple timing calculation.

A request whose required data are already available in the consulted memory cache. In the read-only model of spatial locality, a requested cache block being resident is sufficient for a cache hit.

The memory access trace under memory cache has cache hits at reads 2, 4, 9, 10 and 12. Read 2, at address 4, benefits from the cache line fetched by read 1 at address 0; read 9, at address 52, benefits from read 8 at address 48. These reads use different bytes of the same cache blocks, illustrating spatial locality. Read 4 returns to address 0 while its cache block remains resident, illustrating temporal locality.

A cache hit still takes time to identify the matching cache tag and obtain the requested byte. It is relative to the particular memory cache consulted: a cache miss in one level may be a cache hit in another. The model here has one consulted level and treats a valid matching resident copy as sufficient; it makes no prediction about writes or accesses whose permissions or contents change.

A fixed-sized, aligned block of consecutive bytes transferred and tracked as a unit by a data memory cache. Here cache line and cache block name that unit of data, including its identity in backing computer memory; a resident slot holds a copy. Reading one element can bring neighbouring elements into the same cache line, exploiting spatial locality.

For a nonnegative integer physical address $a$ and a cache line size $B$ bytes, Euclidean division gives the backing cache block number $q$ and offset $d$:Here the brackets denote the floor function and $\mathrm{mod}$ gives the remainder. Cache line $q$ contains bytes at physical addresses $Bq$ through $Bq+B-1$. With $B=16$, read address 52 selects cache block 3, spanning addresses 48–63, and offset 4. Addresses 48 and 52 therefore request the same cache line; addresses 15 and 16 request different cache lines despite being adjacent.

For a cache line size $B=2^b$, the low $b$ bits give the offset. In a fully associative cache, all remaining bits identify the backing cache block as its cache tag; there are no address bits selecting a particular cache slot. An eight-bit illustration gives $52={00110100}_2$: four bits 0011 for cache block 3 and four bits 0100 for offset 4 in the binary numeral system. The eight-bit width is illustrative; the trace does not assume an actual eight-bit machine. ^[ref]

Distinguish this identity from a cache slot, which can hold different cache blocks over time. After read nine in the memory cache example, cache slot 0 contains cache line 3 and cache slot 1 contains cache line 0. Their cache tags identify 3 and 0; neither cache line number is its slot number. Some texts use “cache line” for the storage entry itself; this article reserves cache slot for that role.

A request whose required data are unavailable in the consulted memory cache. The model in spatial locality fetches the requested cache block and evicts a resident cache line only when capacity is full.

In the fixed memory access trace under memory cache, reads 1, 3, 5 and 8 first request cache blocks 0, 2, 1 and 3: four compulsory misses, also called cold misses. Reads 6, 7 and 11 revisit cache blocks that LRU replacement has evicted: three capacity misses. Read 5 is a compulsory miss even though it also causes an eviction; a full memory cache alone does not determine the classification. The fully associative cache permits every cache block in every cache slot, so this example has no conflict misses.

For a precise comparison, run the same memory access trace through an initially empty direct-mapped cache with the same 16-byte cache lines and two cache slots, placing cache block $q$ only in slot $q\mathrm{mod}2$. Read 3 brings in cache block 2 and displaces cache block 0, leaving the other cache slot unused. Read 4 consequently misses at address 0, whereas the fully associative cache still has cache block 0. That read is a conflict miss: increasing placement freedom at the same capacity removes it. The complete direct-mapped cache trace has eight cache misses: four compulsory misses, three capacity misses and one conflict miss.

The convention here classifies each cache miss by first asking whether its cache block has ever been requested. If so, compare with an initially empty fully associative cache of the same capacity and cache line size using LRU replacement on the same memory access trace: a miss there is a capacity miss, while a hit there identifies a conflict miss in the restricted design. This makes the comparison policy explicit; “capacity” is not a claim that no other replacement choice could save that individual read. The three categories describe this read-only demand model; external invalidations or fetching ahead of requests require additional reasoning. ^[ref]

A memory cache allowing any cache block to occupy any resident slot. When full, its replacement policy chooses a victim from all resident cache lines. The examples in temporal locality and spatial locality use LRU replacement, so no additional placement restriction causes eviction.

The diagram enumerates every read of the fixed memory access trace under memory cache. Keep cache slots fixed: fill the lowest-numbered unused slot first, and subsequently reuse the victim's slot. Separately list recency from least recently used to most recently used. A cache hit refreshes that recency without moving the resident data to another cache slot.

After read 3, cache block 0 is older than cache block 2. Read 4 hits cache block 0, making cache block 2 older; read 5 must therefore evict cache block 2 to load cache block 1. Omitting recency updates on cache hits would choose the wrong victim. Read 6 immediately needs the evicted cache block 2, showing that LRU replacement uses past demand rather than knowledge of future demand.

With capacity four cache slots and the same cache line size, the same memory access trace has just its four compulsory misses and eight cache hits, since all four distinct cache blocks fit. Greater capacity helps this trace, but full associativity alone does not make LRU replacement optimal. Its attraction is freedom from placement restrictions; a hardware implementation must still identify a matching cache tag among eligible entries, and exact recency tracking adds work. ^[ref]

An open addressing probe rule that visits $h_m(k),h_m(k)+1,h_m(k)+2,\dots$ modulo $m$, using modular arithmetic, until a matching record or an appropriate stopping slot is found.^[ref]

For capacity $m>0$, the hash table probe numbered $i$, starting at zero, inspects the array indexThe hash function $h_m$ gives the first array index. Each subsequent hash table probe moves one slot forward, wrapping from $m-1$ to $0$ by modular arithmetic. Keep the hash function and key equality fixed while hash table records are stored.

Use three distinguishable slot states: occupied by a live hash table record; never used since initialization or rebuilding, written E; or deleted, written D, a tombstone in open addressing. Lookup compares occupied hash table keys using key equality, stops at E, and continues past D.^[ref]

For an original example, take an eight-slot array, integer hash table keys, and the hash function $h_8(k)=k\mathrm{mod}8$. Start with all slots E and insert the hash table records $(0,A),(8,B),(16,C),(9,V)$ in that order. The insertion hash table probe traces, expressed as array indices, areThe resulting array holds hash table keys $0,8,16,9$ at array indices $0,1,2,3$, with E in the other four slots. The first three hash table keys have the same initial array index, a hash collision; the fourth begins at $1$ but joins the same linear probing cluster. Lookup of $16$ examines array indices $0,1,2$, rejecting $0,8$ before returning $C$. Lookup of absent $24$ examines $0,1,2,3,4$, including the stopping E: five hash table probes for a hash table load factor $\alpha =4/8=1/2$.

Bound lookup to at most $m$ hash table probes, even if no E remains. For algorithm termination, the first $m$ array indices are distinct: equality of $p_i(k)$ and $p_j(k)$ implies that $m$ divides $i-j$ by modular arithmetic, and $0\le i,j<m$ forces $i=j$. They therefore cover the entire finite set of slots exactly once. If no occupied hash table key matches and no E is encountered, exhausting these $m$ hash table probes establishes absence; wrapping around again supplies no new information.^[ref]

The attraction is a compact array and a simple hash table probe rule. Consecutive slots can benefit from spatial locality, although actual memory cache behavior depends on representation and hardware. Correctness rests on preserving the search route; speed depends on how long that route becomes. The linear probing cluster below explains why even a moderate hash table load factor gives no universal constant lookup bound.

Choosing a resident memory page to evict when another memory page needs an occupied page frame. An unused page frame needs no eviction. A dirty memory page requires preservation before reuse, accounted for separately as page writeback. A page replacement policy balances likely future reuse against the overhead of tracking past memory references.

The opportunity comes from temporal locality: retaining a recently accessed memory page can spare a later nonresident page fault. Spatial locality helps consecutive accesses reuse the same memory page until they cross its boundary. A working set describes recent demand, while the resident set describes what is actually present. A small active footprint can fit in the available page frames even when the whole address space cannot. These are useful workload tendencies, not guarantees about future memory references.^[ref]

The page-reference trace below uses the counterexample discussed in Operating Systems: Three Easy Pieces by Remzi H. Arpaci-Dusseau and others (chapter 22, p. 5) and makes the decisions explicit. FIFO page replacement ranks arrival time, LRU replacement ranks last access, CLOCK page replacement samples recent use, and optimal page replacement knows the remaining sequence in advance. Count misses first; page-replacement cost accounting separately counts page writeback and explains why fewer misses need not mean less total service time.

// Destination: computer-science.bigb

A small memory cache close to the CPU can be accessed quickly, but cannot hold the entire useful data set. Increasing its capacity usually increases lookup, wiring and energy costs. A cache hierarchy combines a small fast first level with progressively larger levels that reduce the number of accesses reaching slow main computer memory.

In a simple serial two-level timing model, let $t_1$ be the first-level lookup time, $m_1$ its cache miss rate, $t_2$ the additional second-level lookup time, $m_2$ the second level's local cache miss rate conditional on a first-level miss, and $t_M$ the remaining main-memory cost. ThenThis average memory access time combines the common fast path with a rarer large-capacity path. Several levels balance hit latency, capacity, cost and traffic; making one cache very large does not preserve a tiny first-level hit time. Temporal locality and spatial locality make this arrangement useful.

Storage of a multidimensional array with the last index varying fastest in consecutive positions. In a two-dimensional array, each row is contiguous. Row-major order is a layout convention; a program can traverse that array by rows or by columns. See the explicit address calculation in spatial locality.

Spatial locality is the tendency to access nearby locations in computer memory within a short interval. A sequential scan of a contiguous array has spatial locality even when each element is read only once. A memory cache can exploit spatial locality by fetching a whole cache line on a cache miss: later reads of neighbouring elements become cache hits. Proximity is in the address layout, so visually adjacent entries in a table need not be adjacent bytes.

Consider a $4\times 4$ array of 32-bit integers, each occupying four eight-bit bytes, stored contiguously in row-major order. Use toy physical addresses starting at zero, aligned to a 16-byte cache line boundary. For row $i$ and column $j$, with $0\le i,j<4$, the address and cache block number areHere $\lfloor \cdot \rfloor$ is the floor function. Each row occupies exactly one cache line: row zero begins at addresses $0,4,8,12$, row one at $16,20,24,28$, and so on. Row-major order describes this storage layout; traversal can follow rows or columns without moving the elements.

Read all sixteen elements and compute their integer sum, keeping the accumulator outside the modelled memory cache. Only the sixteen array reads count as memory references. The toy fully associative cache starts empty, holds exactly one 16-byte cache line, fetches the requested cache block on every cache miss, and uses LRU replacement. No cache block is fetched ahead of a request, and no writeback costs are counted because the modelled accesses are reads.

Every element is read once in both traversals. The row traversal exploits spatial locality between distinct elements and temporal locality of their shared cache block. The column traversal returns to each cache block after three other distinct cache blocks, too late for the one-line memory cache. Increase the fully associative cache to four cache lines and both traversals give four compulsory misses and twelve cache hits: no loaded cache block needs eviction. The difference therefore depends on capacity as well as access order and layout.

This makes spatial locality useful and beautiful: the same logical operation on the same elements can use a fetched cache line much more fully simply by changing traversal order. The toy counts do not predict a universal speed ratio on real hardware. Actual cache line sizes, capacities, placement rules, fetching ahead of requests and overlapping work affect runtime; measure a concrete implementation with statistical program profiling. Ulrich Drepper's November 2007 authored paper explains cache line transfers and gives array and matrix access examples connecting layout, temporal locality and spatial locality. Its hardware measurements are historical examples, rather than specifications for every present-day CPU. ^[ref]

// Destination: computer-science.bigb

Temporal locality is the tendency for the same item to be accessed again soon after an earlier access. For computer memory, the item might be an array element, a cache line or a memory page; always specify the unit. Temporal locality concerns reuse over time, whereas spatial locality concerns accesses to nearby addresses. Both are properties of a workload, rather than guarantees about its next memory reference. ^[ref]

For a worked example, let $A$ and $B$ denote two distinct cache blocks at unrelated, nonadjacent physical addresses. An initially empty fully associative cache holding two cache lines, with LRU replacement, receives the sequence $A,B,A$. The first $A$ causes a cache miss and leaves $A$ resident; $B$ causes another cache miss and leaves both $A$ and $B$ resident; the last $A$ is a cache hit. LRU replacement now regards $B$ as older than $A$. The benefit comes from temporal locality of $A$, irrespective of how far apart the cache blocks lie. With capacity one, the same sequence gives three cache misses: reuse helps only if the item survives until it is needed.

The appealing idea is that a small memory cache can serve a large computation when its currently useful data stay small. Repeatedly reading a counter or executing the same instructions can exploit temporal locality. LRU replacement and recency-based approximations try to retain recently used items. A working set records recent demand; if the active working set cannot fit in resident memory pages, memory thrashing can defeat this benefit. At cache line granularity, even accesses to different array elements can reuse the same cache block, as the spatial locality example shows.

Peter J. Denning's May 1968 paper connected recent memory page use to near-future demand through the working set. Section 3 explicitly discusses the clustering of references to the same memory page in time. This is a documented early treatment of the reasoning behind temporal locality, rather than a claim about who first coined the term. ^[ref]