Transcript Direct Mapped Cache

Chapter Seven
2004 Morgan Kaufmann Publishers
1
Memories: Review
•
SRAM:
– value is stored on a pair of inverting gates
– very fast but takes up more space than DRAM (4 to 6 transistors)
•
DRAM:
– value is stored as a charge on capacitor (must be refreshed)
– very small but slower than SRAM (factor of 5 to 10)
Word line
Pass transistor
Capacitor
Bit line
2004 Morgan Kaufmann Publishers
2
Exploiting Memory Hierarchy
•
Users want large and fast memories!
SRAM access times are .5 – 5ns at cost of $4000 to $10,000 per GB.
DRAM access times are 50-70ns at cost of $100 to $200 per GB.
Disk access times are 5 to 20 million ns at cost of $.50 to $2 per GB.
•
2004
Try and give it to them anyway
– build a memory hierarchy
CPU
Level 1
Increasing distance
from the CPU in
access time
Levels in the
Level 2
memory hierarchy
Level n
Size of the memory at each level
2004 Morgan Kaufmann Publishers
3
Locality
•
A principle that makes having a memory hierarchy a good idea
•
If an item is referenced,
temporal locality: it will tend to be referenced again soon
spatial locality: nearby items will tend to be referenced soon.
Why does code have locality?
•
Our initial focus: two levels (upper, lower)
– block: minimum unit of data
– hit: data requested is in the upper level
– miss: data requested is not in the upper level
2004 Morgan Kaufmann Publishers
4
Cache
•
•
Two issues:
– How do we know if a data item is in the cache?
– If it is, how do we find it?
Our first example:
– block size is one word of data
– "direct mapped"
For each item of data at the lower level,
there is exactly one location in the cache where it might be.
e.g., lots of items at the lower level share locations in the upper level
2004 Morgan Kaufmann Publishers
5
Direct Mapped Cache
Mapping: address is modulo the number of blocks in the cache
Cache
000
001
010
011
100
101
110
111
•
00001
00101
01001
01101
10001
10101
11001
11101
Memory
2004 Morgan Kaufmann Publishers
6
Direct Mapped Cache
•
For MIPS:
Address (showing bit positions)
31 30
Hit
13 12 11
20
2 10
Byte
offset
10
Tag
Data
Index
Index
0
1
2
Valid Tag
Data
1021
1022
1023
20
32
=
What kind of locality are we taking advantage of?
2004 Morgan Kaufmann Publishers
7
Direct Mapped Cache
•
Accessing a Cache:
Decimal address
of reference
Assigned cache block
(where found or placed)
10110
miss
10110two mod 8 =110two
26
11010
miss
11010two mod 8 =010two
22
10110
hit
10110two mod 8 =110two
26
11010
hit
11010two mod 8 =010two
16
10000
miss
10000two mod 8 =000two
3
00011
miss
00110two mod 8 =110two
16
10000
hit
10000two mod 8 =000two
18
10010
miss
10010two mod 8 =010two
V
000
N
001
N
010
N
Y
011
N
Y
Tag
Y
100
N
101
N
111
Hit or miss
in cache
22
Index
110
Binary address
of reference
N Y
N
Data
10two
11two
Memory(10000two)
10two
Memory(11010two)
Memory(10010two)
00two
Memory(00011two)
10two
Memory(10110two)
2004 Morgan Kaufmann Publishers
8
Direct Mapped Cache
•
Taking advantage of spatial locality (the Intrinsity FastMATH
processor):
Address (showing bit positions)
31
14 13
18
Hit
65
8
210
4
Tag
Byte
offset
Data
Block offset
Index
18 bits
V
512 bits
Tag
Data
256
entries
16
32
32
32
=
Mux
32
2004 Morgan Kaufmann Publishers
9
Hits vs. Misses
•
Read hits
– this is what we want!
•
Read misses
– stall the CPU, fetch block from memory, deliver to cache, restart
•
Write hits:
– can replace data in cache and memory (write-through)
– write the data only into the cache (write-back the cache later)
•
Write misses:
– read the entire block into the cache, then write the word
2004 Morgan Kaufmann Publishers
10
Hardware Issues
•
Make reading multiple words easier by using banks of memory
CPU
CPU
CPU
Multiplexor
Cache
Cache
Cache
Bus
Bus
Memory
b. Wide memory organization
Bus
Memory
Memory
Memory
Memory
bank 0
bank 1
bank 2
bank 3
c. Interleaved memory organization
Memory
a. One-word-wide
memory organization
•
It can get a lot more complicated...
2004 Morgan Kaufmann Publishers
11
Performance
•
Increasing the block size tends to decrease miss rate:
40%
35%
Miss rate
30%
25%
20%
15%
10%
5%
0%
4
16
64
Block size (bytes)
256
1 KB
8 KB
16 KB
64 KB
256 KB
•
Use split caches because there is more spatial locality in code:
Program
gcc
spice
Block size in
words
1
4
1
4
Instruction
miss rate
6.1%
2.0%
1.2%
0.3%
Data miss
rate
2.1%
1.7%
1.3%
0.6%
Effective combined
miss rate
5.4%
1.9%
1.2%
0.4%
2004 Morgan Kaufmann Publishers
12
Performance
•
Simplified model:
execution time = (execution cycles + stall cycles)  cycle time
stall cycles = # of instructions  miss ratio  miss penalty
•
Two ways of improving performance:
– decreasing the miss ratio
– decreasing the miss penalty
What happens if we increase block size?
2004 Morgan Kaufmann Publishers
13
Decreasing miss ratio with associativity
2004 Morgan Kaufmann Publishers
14
Decreasing miss ratio with associativity
One-way set associative
(direct mapped)
Block
Tag
Data
0
Two-way set associative
1
Set
2
Tag
Data
Tag
Data
0
3
1
4
2
5
3
6
7
Four-way set associative
Set
Tag
Data
Tag
Data
Tag
Data
Tag
Data
0
1
Eight-way set associative (fully associative)
Tag
Data
Tag
Data
Tag
Data
Tag
Data Tag
Data
Tag
Data
Tag
Data
Tag
Data
Compared to direct mapped, give a series of references that:
– results in a lower miss ratio using a 2-way set associative cache
– results in a higher miss ratio using a 2-way set associative cache
assuming we use the “least recently used” replacement strategy
2004 Morgan Kaufmann Publishers
15
An implementation
Address
31 30
12 11 10 9 8
8
22
Index
0
1
2
V
Tag
Data
V
3210
Tag
Data
V
Tag
Data
V
Tag
Data
253
254
255
22
32
4-to-1 multiplexor
Hit
Data
2004 Morgan Kaufmann Publishers
16
Performance
15%
1 KB
12%
2 KB
9%
4 KB
6%
8 KB
16 KB
32 KB
3%
64 KB
128 KB
0
One-way
Two-way
Four-way
Eight-way
Associativity
2004 Morgan Kaufmann Publishers
17
Decreasing miss penalty with multilevel caches
•
Add a second level cache:
– often primary cache is on the same chip as the processor
– use SRAMs to add another cache above primary memory (DRAM)
– miss penalty goes down if data is in 2nd level cache
•
Example:
– CPI of 1.0 on a 5 Ghz machine with a 5% miss rate, 100ns DRAM access
– Adding 2nd level cache with 5ns access time decreases miss rate to .5%
•
Using multilevel caches:
– try and optimize the hit time on the 1st level cache
– try and optimize the miss rate on the 2nd level cache
2004 Morgan Kaufmann Publishers
18
Cache Complexities
•
Not always easy to understand implications of caches:
1200
2000
Radix sort
1000
Radix sort
1600
800
1200
600
800
400
200
Quicksort
400
0
Quicksort
0
4
8
16
32
64
128
256
512 1024 2048 4096
Size (K items to sort)
Theoretical behavior of
Radix sort vs. Quicksort
4
8
16
32
64
128
256
512 1024 2048 4096
Size (K items to sort)
Observed behavior of
Radix sort vs. Quicksort
2004 Morgan Kaufmann Publishers
19
Cache Complexities
•
Here is why:
5
Radix sort
4
3
2
1
Quicksort
0
4
8
16
32
64
128
256
512 1024 2048 4096
Size (K items to sort)
•
Memory system performance is often critical factor
– multilevel caches, pipelined processors, make it harder to predict outcomes
– Compiler optimizations to increase locality sometimes hurt ILP
•
Difficult to predict best algorithm: need experimental data
2004 Morgan Kaufmann Publishers
20
Virtual Memory
•
Main memory can act as a cache for the secondary storage (disk)
Virtual addresses
Physical addresses
Address translation
Disk addresses
•
Advantages:
– illusion of having more physical memory
– program relocation
– Sharing of both the physical memory and CPU among multiple
programs
– Protection
2004 Morgan Kaufmann Publishers
21
Pages: virtual memory blocks
•
Page faults: the data is not in memory, retrieve it from disk
– huge miss penalty, thus pages should be fairly large (e.g., 4KB)
– reducing page faults is important (LRU is worth the price)
– can handle the faults in software instead of hardware
– using write-through is too expensive so we use writeback
Virtual address
31 30 29 28 27
15 14 13 12 11 10 9 8
3210
Page offset
Virtual page number
Translation
29 28 27
15 14 13 12 11 10 9 8
Physical page number
3210
Page offset
Physical address
2004 Morgan Kaufmann Publishers
22
Page Tables
Virtual page
number
Page table
Physical page or
Valid disk address
1
1
1
1
0
1
1
0
1
1
0
1
Physical memory
Disk storage
2004 Morgan Kaufmann Publishers
23
Page Tables
Page table register
Virtual address
31 30 29 28 27
1 5 1 4 1 3 1 2 11 1 0 9 8
Virtual page number
Page offset
12
20
Valid
3 2 1 0
Physical page number
Page table
18
If 0 then page is not
present in memory
29 28 27
1 5 1 4 1 3 1 2 11 1 0 9 8
Physical page number
3 2 1 0
Page offset
Physical address
2004 Morgan Kaufmann Publishers
24
Making Address Translation Fast
•
A cache for address translations: translation lookaside buffer
TLB
Virtual page
number Valid Dirty Ref
1
1
1
1
0
1
0
1
1
0
0
0
Tag
Physical page
address
1
1
1
1
0
1
Physical memory
Page table
Physical page
Valid Dirty Ref or disk address
1
1
1
1
0
1
1
0
1
1
0
1
Typical values:
1
0
0
0
0
0
0
0
1
1
0
1
1
0
0
1
0
1
1
0
1
1
0
1
Disk storage
16-512 entries,
miss-rate: .01% - 1%
miss-penalty: 10 – 100 cycles
2004 Morgan Kaufmann Publishers
25
TLBs and caches
Virtual address
TLB access
TLB miss
exception
No
Yes
TLB hit?
Physical address
No
Try to read data
from cache
Cache miss stall
while read block
No
Cache hit?
Yes
Write?
No
Yes
Write access
bit on?
Write protection
exception
Yes
Try to write data
to cache
Deliver data
to the CPU
Cache miss stall
while read block
No
Cache hit?
Yes
Write data into cache,
update the dirty bit, and
put the data and the
address into the write buffer
2004 Morgan Kaufmann Publishers
26
TLBs and Caches
Virtual address
31 30 29
14 13 12 11 10 9
Virtual page number
3 2 1 0
Page offset
12
20
Valid Dirty
Tag
Physical page number
=
=
=
=
=
=
TLB
TLB hit
20
Page offset
Physical page number
Physical address
Block
Cache index
Physical address tag
offset
18
8
4
Byte
offset
2
8
12
Valid
Data
Tag
Cache
=
Cache hit
32
Data
2004 Morgan Kaufmann Publishers
27
Modern Systems
•
2004 Morgan Kaufmann Publishers
28
Modern Systems
•
Things are getting complicated!
2004 Morgan Kaufmann Publishers
29
Some Issues
•
Processor speeds continue to increase very fast
— much faster than either DRAM or disk access times
100,000
10,000
1,000
Performance
CPU
100
10
Memory
1
Year
•
Design challenge: dealing with this growing disparity
– Prefetching? 3rd level caches and more? Memory design?
2004 Morgan Kaufmann Publishers
30