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Cache Design

Who Cares about Memory Hierarchy?
 Processor vs Memory Performance
CPU-DRAM Gap
1980: no cache in microprocessor;
1995 2-level cache
Memory Cache
cpu
memory
cache
Memory Locality
 Memory hierarchies take advantage of memory locality.
 Memory locality is the principle that future memory
accesses are near past accesses.
 Memories take advantage of two types of locality
– Temporal locality -- near in time
• we will often access the same data again very soon
– Spatial locality -- near in space/distance
• our next access is often very close to our last access (or recent
accesses).
1,2,3,4,5,6,7,8,8,47,8,9,8,10,8,8...
Locality and Caching
 Memory hierarchies exploit locality by caching (keeping
close to the processor) data likely to be used again.
 This is done because we can build large, slow memories
and small, fast memories, but we can’t build large, fast
memories.
 If it works, we get the illusion of SRAM access time with
disk capacity
SRAM (static RAM) – 1-5 ns access time
DRAM (dynamic RAM) – 40-60 ns
disk -- access time measured in milliseconds, very cheap
CPU
A typical memory hierarchy
memory
memory
memory
memory
on-chip cache
on-chip L2 L3
cache
main memory
disk
small expensive $/bit
cheap $/bit
big
• so then where is my program and data??
Cache Fundamentals
 cache hit -- an access where the data
is found in the cache.
 cache miss -- an access which isn’t
 hit time -- time to access the higher cache
 miss penalty -- time to move data from
lower level to upper, then to cpu
 hit ratio -- percentage of time the data is found in the
higher cache
 miss ratio -- (1 - hit ratio)
cpu
highest-level
cache
lower-level
memory/cache
Cache Fundamentals, cont.
 cache block size or cache line size-- the
amount of data that gets transferred on a
cache miss.
 instruction cache -- cache that only holds
instructions.
 data cache -- cache that only caches data.
 unified cache -- cache that holds both.
(L1 is unified  “princeton architecture”)
cpu
lowest-level
cache
next-level
memory/cache
Cache Characteristics
 Cache Organization
 Cache Access
 Cache Replacement
 Write Policy
Cache Organization: Where can a block
be placed in the cache?
 Block 12 placed in 8 block cache:
– Fully associative, direct mapped, 2-
way set associative
– S.A. Mapping = Block Number
Modulo Number Sets
(associativity = degree of freedom in
placing a particular block of
memory)
(set = a collection of blocks cache
blocks with the same cache index)
Cache Access: How Is a Block Found
In the Cache?
 Tag on each block
– No need to check index or block offset
 Increasing associativity shrinks index, expands tag
Fully Assoc: No index
Directe Mapped: Large index
Block Address
Cache
tags data
Cache Organization
 A typical cache has three dimensions
tagt a g d a tdaa t a tagt a g d a tdaa t a
tagt a g d a tdaa t a tagt a g d a tdaa t a
tagt a g d a tdaa t a tagt a g d a tdaa t a
tagt a g d a tdaa t a tagt a g d a tdaa t a
.
.
.
Bytes/block (block size)
Blocks/set (associativity)
Number of sets (cache size)
tag index block offset
“ways”
Which Block Should be Replaced on a Miss?
 Direct Mapped is Easy
 Set associative or fully associative:
– “Random” (large associativities)
– LRU (smaller associativities)
– Pseudo Associative
Associativity: 2-way 4-way 8-way
Size LRU Random LRU Random LRU Random
16 KB 5.18% 5.69% 4.67% 5.29% 4.39% 4.96%
64 KB 1.88% 2.01% 1.54% 1.66% 1.39% 1.53%
256 KB 1.15% 1.17% 1.13% 1.13% 1.12% 1.12%
Numbers are averages across a set of benchmarks. Performance improvements vary
greatly by individual benchmarks.
Accessing a Direct Mapped Cache
 64 KB cache, direct-mapped, 32-byte cache block size
31 30 29 28 27 ........... 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 0
tag index
valid tag data
64 KB / 32 bytes =
2 K cache blocks/sets
11
=
256
32
16
hit/miss
0
1
2
...
...
...
...
2045
2046
2047
word offset
Accessing a 2-way Assoc Cache – Hit Logic
 32 KB cache, 2-way set-associative, 16-byte block size
31 30 29 28 27 ........... 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 0
tag index
valid tag data
32 KB / 16 bytes / 2 =
1 K cache sets
10
=
18
hit/miss
0
1
2
...
...
...
...
1021
1022
1023
word offset
valid tag data
=
v. direct
mapped
21264 L1 Cache
 64 KB, 64-byte blocks, (SA (set assoc) = 2) v. direct
mapped –
must know
correct line
that contains
data to control
mux –
direct mapped
cache can
operate on
data without
waiting for
tag
set assoc needs
to know which
set to operate on!
 line predictor
Cache Organization
Intel Core 2 AMD Opteron
Duo
90 nm, 64-byte clock, 1 bank
.00346 miss rate
Spec00
.00366 miss rate
Spec00
(From Mark Hill’s Spec Data)
Evaluation of Cache Access Time via. Cacti + Simulation –
Intel wins by a hair
Cache Access
 Cache Size = #sets * block size * associativity
 What is the Cache Size, if we have direct mapped, 128 set
cache with a 32-byte block?
 What is the Associativity, if we have 128 KByte cache,
with 512 sets and a block size of 64-bytes?
Cache Access
 16 KB, 4-way set-associative cache, 32-bit address, byteaddressable
memory, 32-byte cache blocks/lines
 how many tag bits?
 Where would you find the word at address 0x200356A4?
tag data tag data tag data tag data
index
What Happens on a Write?
 Write through: The information is written to both the block in
the cache and to the block in the lower-level memory.
 Write back: The information is written only to the block in the
cache. The modified cache block is written to main memory
only when it is replaced.
– is block clean or dirty?
 Pros and Cons of each:
– WT: read misses do not need to write back evicted line contents
– WB: no writes of repeated writes
 WT always combined with write buffers so that don’t wait for
lower level memory
What About Write Miss?
 Write allocate: The block is loaded into cache on a write miss
 No-Write allocate: The block is modified in the lower levels
of memory but not in cache
 Write buffer allows merging of writes
100
104
108
112
Write address
1
1
1
1
V
0
0
0
0
V
0
0
0
0
V
0
0
0
0
V
100
Write address
1
0
0
0
V
1
0
0
0
V
1
0
0
0
V
1
0
0
0
V
Unified versus Separate I+D L1 Cache
(Princeton vs. Harvard Arch)
Separate Instruction Cache and Data Cache?
Size Instruction Cache Data Cache Unified Cache
1 KB 3.06% 24.61% 13.34%
2 KB 2.26% 20.57% 9.78%
4 KB 1.78% 15.94% 7.24%
8 KB 1.10% 10.19% 4.57%
16 KB 0.64% 6.47% 2.87%
32 KB 0.39% 4.82% 1.99%
64 KB 0.15% 3.77% 1.35%
128 KB 0.02% 2.88% 0.95%
Area-limited designs may consider unified caches
Generally, the benefits of separating the caches are
overwhelming… (what are the benefits?)
Cache Performance
 CPU time = (CPU execution clock cycles + Memory stall
clock cycles) x clock cycle time
 Memory stall clock cycles = (Reads x Read miss rate x
Read miss penalty + Writes x Write miss rate x Write miss
penalty)
 Memory stall clock cycles = Memory accesses x Miss rate
x Miss penalty
Cache Performance
CPUtime = IC x (CPIexecution + Memory stalls per instruction)
x Clock cycle time
CPUtime = IC x (CPIexecution + Mem accesses per instruction x
Miss rate x Miss penalty) x Clock cycle time
(includes hit time as part of CPI)
Average memory-access time = Hit time + Miss rate x Miss
penalty (ns or clocks)
Improving Cache Performance
Average memory-access time = Hit time + Miss rate x Miss
penalty (ns or clocks)
How are we going to improve cache performance??
1.
2.
3.
Cache Overview Key Points
 CPU-Memory gap is a major performance obstacle
 Caches take advantage of program behavior: locality
 Designer has lots of choices -> cache size, block size,
associativity, replacement policy, write policy, ...
 Time of program still only reliable performance measure
Improving Cache Performance
1. Reduce the miss rate,
2. Reduce the miss penalty, or
3. Reduce the time to hit in the cache.
Reducing Misses
 Classifying Misses: 3 Cs
– Compulsory—The first access to a block is not in the
cache, so the block must be brought into the cache. These
are also called cold start misses or first reference misses.
– Capacity—If the cache cannot contain all the blocks
needed during execution of a program, capacity misses
will occur due to blocks being discarded and later
retrieved.
– Conflict—If the block-placement strategy is set
associative or direct mapped, conflict misses (in addition
to compulsory and capacity misses) will occur because a
block can be discarded and later retrieved if too many
blocks map to its set. These are also called collision
misses or interference misses.
How To Measure
Misses in infinite
cache
Non-compulsory
misses in size X
fully associative
cache
Non-compulsory,
non-capacity
misses
3Cs Absolute Miss Rate
(bad color tones)
How To Reduce Misses?
 Compulsory Misses?
 Capacity Misses?
 Conflict Misses?
 What can the compiler do?
1. Reduce Misses via Larger Block Size
 16K cache, miss penalty for 16-byte block = 42, 32-byte is
44, 64-byte is 48. Miss rates are 3.94, 2.87, and 2.64%?
2. Reduce Misses via Higher Associativity
 2:1 Cache Rule:
– Miss Rate DM cache size N ==
– Miss Rate 2-way associative cache size N/2
 Beware: Execution time is only final measure!
– Will Clock Cycle time increase?
3. Reducing Misses via
Victim Cache
 How to get fast hit time of
Direct Mapped yet still avoid
conflict misses?
 Add buffer to place data
discarded from cache
 Jouppi [1990]: 4-entry victim
cache removed 20% to 95%
of conflicts for a 4 KB direct
mapped data cache
4. Reducing Misses via Pseudo-Associativity
 Combines fast hit time of Direct Mapped and the lower conflict
misses of a 2-way SA cache.
 Divide cache: on a miss, check other half of cache to see if there, if
so have a pseudo-hit (slow hit)
 Drawback: CPU pipeline is hard if hit takes 1 or 2 cycles
– Better for caches not tied directly to processor
Hit Time
Pseudo Hit Time Miss Penalty
Time
5. Reducing Misses by HW Prefetching of
Instruction & Data
 E.g., Instruction Prefetching
– Alpha 21064 fetches 2 blocks on a miss
– Extra block placed in stream buffer
– On miss check stream buffer
 Works with data blocks too:
– Jouppi [1990] 1 data stream buffer got 25% misses from 4KB
cache; 4 streams got 43%
– Palacharla & Kessler [1994] for scientific programs for 8 streams
got 50% to 70% of misses from 2 64KB, 4-way set associative
caches
 Prefetching relies on extra memory bandwidth that can be
used without penalty
Stream Buffers
 Allocate a Stream Buffer on a cache miss
 Run ahead of the execution stream prefetching N blocks into stream
buffer
 Search stream buffer in parallel with cache access
 If hit, then move block to cache, and prefetch another block
Predictor-Directed Stream Buffers
 Effective for next-line and stride prefetching, what about pointers?
 Allow stream buffer to follow any prediction stream.
 Provide confidence techniques for stream allocation
– Reduce stream thrashing
Impact of hardware prefetching on Pentium 4 – pretty large
(15 missing benchmarks benefited less than 15%)
6. Reducing Misses by
Software Prefetching Data
 Data Prefetch
– Load data into register (HP PA-RISC loads)
– Cache Prefetch: load into cache (MIPS IV, PowerPC, SPARC v. 9)
– Special prefetching instructions cannot cause faults
• can be used speculatively
• load into a r0
 Issuing Prefetch Instructions takes time
– Is cost of prefetch issues < savings in reduced misses?
7. Reducing Misses by Various
Compiler Optimizations
 Instructions
– Reorder procedures in memory so as to reduce misses
– Profiling to look at conflicts
– results in a 30% reduction in miss rate for an 8K I-cache
 Data
– Reordering
• results in a 40% reduction in cache miss rate for an 8K D-cache
– Merging Arrays: improve spatial locality by single array of compound
elements vs. 2 arrays
– Loop Interchange: change nesting of loops to access data in order
stored in memory
– Loop Fusion: Combine 2 independent loops that have same looping and
some variables overlap
– Blocking: Improve temporal locality by accessing “blocks” of data
repeatedly vs. going down whole columns or rows
Merging Arrays Example
/* Before */
int val[SIZE];
int key[SIZE];
/* After */
struct merge {
int val;
int key;
};
struct merge merged_array[SIZE];
Reducing conflicts between val & key, create spatial locality.
Loop Interchange Example
/* Before */
for (k = 0; k < 100; k = k+1)
for (j = 0; j < 100; j = j+1)
for (i = 0; i < 5000; i = i+1)
x[i][j] = 2 * x[i][j];
/* After */
for (k = 0; k < 100; k = k+1)
for (i = 0; i < 5000; i = i+1)
for (j = 0; j < 100; j = j+1)
x[i][j] = 2 * x[i][j];
Sequential accesses instead of striding through memory
every 100 words
Loop Fusion Example
/* Before */
for (i = 0; i < N; i = i+1)
for (j = 0; j < N; j = j+1)
a[i][j] = 1/b[i][j] * c[i][j];
for (i = 0; i < N; i = i+1)
for (j = 0; j < N; j = j+1)
d[i][j] = a[i][j] + c[i][j];
/* After */
for (i = 0; i < N; i = i+1)
for (j = 0; j < N; j = j+1)
{ a[i][j] = 1/b[i][j] * c[i][j];
d[i][j] = a[i][j] + c[i][j];}
2 misses per access to a & c vs. one miss per access
Blocking Example
/* Before */
for (i = 0; i < N; i = i+1)
for (j = 0; j < N; j = j+1)
{r = 0;
for (k = 0; k < N; k = k+1){
r = r + y[i][k]*z[k][j];};
x[i][j] = r;
};
 Two Inner Loops:
– Read all NxN elements of z[]
– Read N elements of 1 row of y[] repeatedly
– Write N elements of 1 row of x[]
 Capacity Misses a function of N & Cache Size:
– worst case => 2N3 + N2.
 Idea: compute on BxB submatrix that fits in cache
Blocking Example
/* After */
for (jj = 0; jj < N; jj = jj+B)
for (kk = 0; kk < N; kk = kk+B)
for (i = 0; i < N; i = i+1)
for (j = jj; j < min(jj+B-1,N); j = j+1)
{r = 0;
for (k = kk; k < min(kk+B-1,N); k = k+1) {
r = r + y[i][k]*z[k][j];};
x[i][j] = x[i][j] + r;
};
 Capacity Misses from 2N3 + N2 to 2N3/B +N2
 B called Blocking Factor
 Conflict Misses Are Not As Easy...
Key Points
 3 Cs: Compulsory, Capacity, Conflict
– 1. Reduce Misses via Larger Block Size
– 2. Reduce Misses via Higher Associativity
– 3. Reducing Misses via Victim Cache
– 4. Reducing Misses via Pseudo-Associativity
– 5. Reducing Misses by HW Prefetching Instr, Data
– 6. Reducing Misses by SW Prefetching Data
– 7. Reducing Misses by Compiler Optimizations
 Remember danger of concentrating on just one parameter when
evaluating performance
 Next: reducing Miss penalty
Improving Cache Performance
1. Reduce the miss rate,
2. Reduce the miss penalty, or
3. Reduce the time to hit in the cache.
1. Reducing Miss Penalty: Read Priority
over Write on Miss
 Write buffers offer RAW conflicts with main memory reads
on cache misses
 If simply wait for write buffer to empty might increase read
miss penalty by 50%
 Check write buffer contents before read;
if no conflicts, let the memory access continue
 Write Back?
– Read miss may require write of dirty block
– Normal: Write dirty block to memory, and then do the read
– Instead copy the dirty block to a write buffer, then do the read, and
then do the write
– CPU stalls less since it can restart as soon as read completes
2. Subblock Placement to Reduce Miss
Penalty (also known as sectoring)
 Don’t have to load full block on a miss
 Have bits per subblock to indicate valid
 (Originally invented to reduce tag storage)
Valid Bits
100
300
200
204
1
1
0
0
1
1
1
0
1
0
0
0
Sub-blocks
1
0
1
0
some PPC machines do this.
3. Early Restart and Critical Word First
 Don’t wait for full block to be loaded before restarting CPU
– Early restart—As soon as the requested word of the block arrives, send
it to the CPU and let the CPU continue execution
– Critical Word First—Request the missed word first from memory and
send it to the CPU as soon as it arrives; let the CPU continue execution
while filling the rest of the words in the block. Also called wrapped fetch
and requested word first
 Most useful with large blocks,
 Spatial locality a problem; often we next want the next
sequential word soon, so not always a benefit (early restart).
4. Non-blocking Caches to reduce stalls
on misses
 Non-blocking cache or lockup-free cache allowing the data
cache to continue to supply cache hits during a miss
 “hit under miss” reduces the effective miss penalty by being
helpful during a miss instead of ignoring the requests of the
CPU
 “hit under multiple miss” or “miss under miss” may further
lower the effective miss penalty by overlapping multiple misses
– Significantly increases the complexity of the cache controller as there
can be multiple outstanding memory accesses
 assumes “stall on use” not “stall on miss” which works naturally
with dynamic scheduling, but can also work with static.
Value of Hit Under Miss for SPEC
- 8 KB cache, 16 cycle miss, 32-byte blocks
- old data; good model for misses to L2, not a good model for misses to main memory (~ 300 cycles)
5. Use Multi-level caches
 L2 Equations
AMAT = Hit TimeL1 + Miss RateL1 x Miss PenaltyL1
Miss PenaltyL1 = Hit TimeL2 + Miss RateL2 x Miss PenaltyL2
AMAT = Hit TimeL1 + Miss RateL1
x (Hit TimeL2 + Miss RateL2 x Miss PenaltyL2)
 Definitions:
– Local miss rate— misses in this cache divided by the total
number of memory accesses to this cache (Miss rateL2)
– Global miss rate—misses in this cache divided by the total
number of memory accesses generated by the CPU
(Miss RateL1 x Miss RateL2)
cpu
lowest-level
cache
next-level
memory/cache
L2 local miss rates pretty high
L2 Size More Important Than Latency: Why?
Experiment is on 21264:
OoO hides L1 misses
well but not L2 misses
Hit TimeL1 + Miss RateL1
x (Hit TimeL2
+ Miss RateL2 x Miss PenaltyL2)
8 or 16 .5 200
=100
Multi-level Caches, cont.
 L1 cache local miss rate 10%, L2 local miss rate 40%. What are
the global miss rates?
 L1 highest priority is fast hit time. L2 typically low miss rate.
 Design L1 and L2 caches in concert.
 Property of inclusion -- if it is in L2 cache, it is guaranteed to be
in one of the L1 caches -- simplifies design of consistent caches.
 L2 cache can have different associativity (good idea?) or block
size (good idea?) than L1 cache.
Reducing Miss Penalty Summary
 Five techniques
– Read priority over write on miss
– Subblock placement
– Early Restart and Critical Word First on miss
– Non-blocking Caches (Hit Under Miss)
– Multi-levels of Cache
Improving Cache Performance
1. Reduce the miss rate,
2. Reduce the miss penalty, or
3. Reduce the time to hit in the cache.
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