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Memory Hierarchy Design. Outline. Introduction Reviews of the ABCs of caches Cache Performance Reducing Cache Miss Penalty Reducing Miss Rate Reducing Cache Miss Penalty or Miss Rate Via Parallelism Reducing Hit Time Main Memory and Organizations for Improving Performance - PowerPoint PPT Presentation

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Page 1: Memory Hierarchy Design


Memory Hierarchy Design

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• Introduction• Reviews of the ABCs of caches• Cache Performance• Reducing Cache Miss Penalty• Reducing Miss Rate• Reducing Cache Miss Penalty or Miss Rate Via Parallelism• Reducing Hit Time• Main Memory and Organizations for Improving Performance• Memory Technology• Virtual Memory• Protection and Examples of Virtual Memory• Assignment Questions

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5.1 Introduction

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Memory Hierarchy Design

• Motivated by the principle of locality - A 90/10 type of rule– Take advantage of 2 forms of locality

• Spatial - nearby references are likely• Temporal - same reference is likely soon

• Also motivated by cost/performance structures– Smaller hardware is faster: SRAM, DRAM, Disk, Tape– Access vs. bandwidth variations– Fast memory is more expensive

• Goal – Provide a memory system with cost almost as low as the cheapest level and speed almost as fast as the fastest level

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• CPU performance improves at 55%/year– In 1996 it was a phenomenal 18% per month

• DRAM - has improved at 7% per year

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Levels in A Typical Memory Hierarchy

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Sample Memory Hierarchy

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5.2 Review of the ABCs of Caches

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36 Basic Terms on Caches

Cache Full associative Write allocate

Virtual memory dirty bit unified cache

memory stall cycles block offset misses per instruction

directed mapped write back block

valid bit data cache locality

block address hit time address trace

write through cache miss set

instruction cache page fault random placement

average memory access time miss rate index field

cache hit n-way set associative no-write allocate

page least-recently used write buffer

miss penalty tag field write stall

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• The first level of the memory hierarchy encountered once the address leaves the CPU– Persistent mismatch between CPU and main-memory speeds– Exploit the principle of locality by providing a small, fast memory

between CPU and main memory -- the cache memory

• Cache is now applied whenever buffering is employed to reuse commonly occurring terms (ex. file caches)

• Caching – copying information into faster storage system– Main memory can be viewed as a cache for secondary storage

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General Hierarchy Concepts

• At each level - block concept is present (block is the caching unit)– Block size may vary depending on level

• Amortize longer access by bringing in larger chunk• Works if locality principle is true

– Hit - access where block is present - hit rate is the probability– Miss - access where block is absent (in lower levels) - miss rate

• Mirroring and consistency– Data residing in higher level is subset of data in lower level– Changes at higher level must be reflected down - sometime

• Policy of sometime is the consistency mechanism

• Addressing– Whatever the organization you have to know how to get at it!– Address checking and protection

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Physical Address Structure

• Key is that you want different block sizes at different levels

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Latency and Bandwidth

• The time required for the cache miss depends on both latency and bandwidth of the memory (or lower level)

• Latency determines the time to retrieve the first word of the block

• Bandwidth determines the time to retrieve the rest of this block

• A cache miss is handled by hardware and causes processors following in-order execution to pause or stall until the data are available

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Predicting Memory Access Times

• On a hit: simple access time to the cache• On a miss: access time + miss penalty

– Miss penalty = access time of lower + block transfer time– Block transfer time depends on

• Block size - bigger blocks mean longer transfers• Bandwidth between the two levels of memory

– Bandwidth usually dominated by the slower memory and the bus protocol

• Performance – Average-Memory-Access-Time = Hit-Access-Time + Miss-Rate *

Miss-Penalty– Memory-stall-cycles = IC * Memory-reference-per-instruction * Miss-

Rate * Miss-Penalty

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Block Sizes, Miss Rates & Penalties, Accesses

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Typical Memory Hierarchy Parameters for WS or SS

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Typical Parameters in Modern CPU

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Headaches of Memory Hierarchies

• CPU never knows for sure if an access will hit• How deep will a miss be - i. e. miss penalty

– If short then the CPU just waits– If long then probably best to work on something else – task switch

• Implies that the amount can be predicted with reasonable accuracy

• Task switch better be fast or productivity/efficiency will suffer

• Implies some new needs– More hardware accounting– Software readable accounting information (address trace)

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Four Standard Questions

• Block Placement– Where can a block be placed in the upper level?

• Block Identification– How is a block found if it is in the upper level?

• Block Replacement– Which block should be replaced on a miss?

• Write Strategy– What happens on a write?

Answer the four questions for the first level of the memory hierarchy

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Block Placement Options

• Direct Mapped– (Block address) MOD (# of cache blocks)

• Fully Associative– Can be placed anywhere

• Set Associative– Set is a group of n blocks -- each block is called a way– Block first mapped into a set (Block address) MOD (# of cache

sets)– Placed anywhere in the set

• Most caches are direct mapped, 2- or 4-way set associative

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Block Placement Options (Cont.)

Continuum of levels of set associativity

(m=0) (m=3) (m=2)

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Block Identification

• Each cache block carries tags• Address Tags: which block am I?

– Physical address now: address tag## set index## block offset– Note relationship of block size, cache size, and tag size– The smaller the set tag the cheaper it is to find

• Status Tags: what state is the block in?– valid, dirty, etc.

r (address tag)

m (set index)

n(block offset)

2n bytesper block

2m addressable sets in the cache

Physical address =r + m + n bits

Many memory blocks may map to the same cache block

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Block Identification (Cont.)

r (address tag) m n

Physical address = r + m + n bits

2n bytesper block

2m addressable sets in the cache

• Caches have an address tag on each block frame that gives the block address.

• A valid bit to say whether or not this entry contains a valid address.

• The block frame address can be divided into the tag filed and the index field.

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Block Replacement

• Random: just pick one and chuck it– Simple hash game played on target block frame address– Some use truly random

• But lack of reproducibility is a problem at debug time

• LRU - least recently used– Need to keep time since each block was last accessed

• Expensive if number of blocks is large due to global compare• Hence approximation is often used = Use bit tag and LFU

• FIFOOnly one choice for direct-mappedplacement

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Data Cache Misses Per 1000 Instructions

64 byte blocks on a Alpha using 10 SPEC2000

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Short Summaries from the Previous Figure

• More-way associative is better for small cache• 2- or 4-way associative perform similar to 8-way associative

for larger caches• Larger cache size is better• LRU is the best for small block sizes• Random works fine for large caches• FIFO outperforms random in smaller caches• Little difference between LRU and random for larger caches

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Improving Cache Performance

• MIPS mix is 10% stores and 37% loads– Writes are about 10%/(100%+10%+37%) = 7% of overall memory

traffic, and 10%/(10%+37%)=21% of data cache traffic

• Make the common case fast– Implies optimizing caches for reads

• Read optimizations– Block can be read concurrent with tag comparison– On a hit the read information is passed on– On a miss the - nuke the block and start the miss access

• Write optimizations– Can’t modify until after tag check - hence take longer

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Write Options

• Write through: write posted to cache line and through to next lower level– Incurs write stall (use an intermediate write buffer to reduce the stall)

• Write back– Only write to cache not to lower level– Implies that cache and main memory are now inconsistent

• Mark the line with a dirty bit• If this block is replaced and dirty then write it back

• Pro’s and Con’s both are useful– Write through

• No write on read miss, simpler to implement, no inconsistency with main memory

– Write back• Uses less main memory bandwidth, write times independent of main

memory speeds• Multiple writes within a block require only one write to the main memory

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Write Miss Options

• Two choices for implementation– Write allocate – or fetch on write

• Load the block into cache, and then do the write in cache• Usually the choice for write-back caches

– No-write allocate – or write around• Modify the block where it is, but do not load the block in the

cache• Usually the choice for write-through caches• Danger - goes against the locality principle grain• But other delayed completion games are possible

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• Fully associative write-back cache with many cache entries that start empty

• Read/Write sequence– Write Mem[100];– Write Mem[100];– Read Mem[200];– Write Mem[200];– Write Mem[100]

• Four misses and one hit for no-write allocate; two misses and three hits for write allocate

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Different Memory-Hierarchy Consideration for Desktop, Server, Embedded System

• Servers– More context switches increase compulsory miss rates

• Desktops are concerned more with average latency, whereas servers are also concerned about memory bandwidth

– The importance of protection escalates – Have greater bandwidth demands

• Embedded systems– Worry about worst-case performance: caches improve average-case

performance– Power and battery life less HW less HW-intensive optimization– Protection role is diminished– Often no disk storage– Write-back is more attractive

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The Alpha AXP 21264 Data Cache

• The cache contains 65,536 bytes of data in 64-byte blocks with two-way set associative placement (total 512 sets in the cache), write back, and write allocate on a write miss

• The 44-bit physical address is divided into three fields: the 29-bit Tag, 9-bit Index, and 6-bit block offset

• Although each block is 64 bytes, 8 bytes within a block is accessed per time– 3 bits from the block offset are used to index the proper 8 bytes

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The Alpha AXP 21264 Data Cache (Cont.)

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The Alpha AXP 21264 Data Cache (Cont.)

• Read hit: three clock cycles for 4 steps instructions in the following two 2 clock cycles would wait if they tried to use the load result

• Read miss: 64 bytes are read from the next level– Block replacement: FIFO with a round-robin bit

• Update data, address tag, valid bit, and the round-robin bit– Write back with one dirty bit per block– 8 victim buffers (or write buffers)

• If the victim buffer is full, the cache must wait

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The Alpha AXP 21264 Data Cache (Cont.)

• Write hit: the first three steps are the same as read. Since 21264 executes out-of-order, only after it signals the instruction has committed and the cache tag comparison indicates a hit are the data written to the cache

• Write miss: similar to read miss (write allocate)• Separate instruction and data caches

– Each has 64KB

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Unified vs. Split Cache

• Instruction cache and data cache• Unified cache

– structural hazards for load and store operations

• Split cache– Most recent processors choose split cache– Separate ports for instruction and data caches – double bandwidth– Opportunity of optimizing each cache separately – different capacity,

block sizes, and associativity

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Unified vs. Split Cache

Miss per 1000 instructions for instruction, data, and unified caches.Instruction reference is about 74%. The data are for 2-way associative caches with 64-byte blocks

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5.3 Cache Performance

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Cache Performance

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Cache Performance Example

• Each instruction takes 2 clock cycle (ignore memory stalls)• Cache miss penalty – 50 clock cycles• Miss rate = 2%• Average 1.33 memory reference per instructions

• Ideal – IC * 2 * cycle-time• With cache – IC*(2+1.33*2%*50)*cycle-time = IC * 3.33 * cycle-time• No cache – IC * (2+1.33*100%*50)*cycle-time• The importance of cache for CPUs with lower CPI and higher clock

rates is greater – Amdahl’s Law

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Average Memory Access Time VS CPU Time

• Compare two different cache organizations– Miss rate – direct-mapped (1.4%), 2-way associative (1.0%)– Clock-cycle-time – direct-mapped (2.0ns), 2-way associative (2.2ns)

• CPI with a perfect cache – 2.0, average memory reference per instruction – 1.3; miss-penalty – 70ns; hit-time – 1 CC

• Average Memory Access Time (Hit time + Miss_rate * Miss_penalty)• AMAT(Direct) = 1 * 2 + (1.4% * 70) = 2.98ns• AMAT(2-way) = 1 * 2.2 + (1.0% * 70) = 2.90ns

• CPU Time• CPU(Direct) = IC * (2 * 2 + 1.3 * 1.4% * 70) = 5.27 * IC• CPU(2-way) = IC * (2 * 2.2 + 1.3 * 1.0% * 70) = 5.31 * IC

Since CPU time is our bottom-line evaluation, and since direct mapped is simpler to build, the preferred cache is direct mapped in this example

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Unified and Split Cache

• Unified – 32KB cache, Split – 16KB IC and 16KB DC• Hit time – 1 clock cycle, miss penalty – 100 clock cycles• Load/Store hit takes 1 extra clock cycle for unified cache• 36% load/store – reference to cache: 74% instruction, 26% data

• Miss rate(16KB instruction) = 3.82/1000/1.0 = 0.004Miss rate (16KB data) = 40.9/1000/0.36 = 0.114

• Miss rate for split cache – (74%*0.004) + (26%*0.114) = 0.0324Miss rate for unified cache – 43.3/1000/(1+0.36) = 0.0318

• Average-memory-access-time = % inst * (hit-time + inst-miss-rate * miss-penalty) + % data * (hit-time + data-miss-rate * miss-penalty)

• AMAT(Split) = 74% * (1 + 0.004 * 100) + 26% * (1 + 0.114 * 100) = 4.24• AMAT(Unified) = 74% * (1 + 0.0318 * 100) + 26% * (1 + 1 + 0.0318*

100) = 4.44

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Improving Cache Performance

• Average-memory-access-time = Hit-time + Miss-rate * Miss-penalty

• Strategies for improving cache performance– Reducing the miss penalty– Reducing the miss rate– Reducing the miss penalty or miss rate via parallelism– Reducing the time to hit in the cache

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5.4 Reducing Cache Miss Penalty

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Techniques for Reducing Miss Penalty

• Multilevel Caches (the most important)• Critical Word First and Early Restart• Giving Priority to Read Misses over Writes• Merging Write Buffer• Victim Caches

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Multi-Level Caches

• Probably the best miss-penalty reduction• Performance measurement for 2-level caches

– AMAT = Hit-time-L1 + Miss-rate-L1* Miss-penalty-L1– Miss-penalty-L1 = Hit-time-L2 + Miss-rate-L2 * Miss-penalty-L2– AMAT = Hit-time-L1 + Miss-rate-L1 * (Hit-time-L2 + Miss-rate-L2 *


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Multi-Level Caches (Cont.)

• Definitions:– Local miss rate: misses in this cache divided by the total number of

memory accesses to this cache (Miss-rate-L2)– Global miss rate: misses in this cache divided by the total number of

memory accesses generated by CPU (Miss-rate-L1 x Miss-rate-L2) – Global Miss Rate is what matters

• Advantages:– Capacity misses in L1 end up with a significant penalty reduction

since they likely will get supplied from L2• No need to go to main memory

– Conflict misses in L1 similarly will get supplied by L2

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Miss Rate Example

• Suppose that in 1000 memory references there are 40 misses in the first-level cache and 20 misses in the second-level cache– Miss rate for the first-level cache = 40/1000 (4%)– Local miss rate for the second-level cache = 20/40 (50%)– Global miss rate for the second-level cache = 20/1000 (2%)

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Miss Rate Example (Cont.)

• Assume miss-penalty-L2 is 100 CC, hit-time-L2 is 10 CC, hit-time-L1 is 1 CC, and 1.5 memory reference per instruction. What is average memory access time and average stall cycles per instructions? Ignore writes impact.– AMAT = Hit-time-L1 + Miss-rate-L1 * (Hit-time-L2 + Miss-rate-L2 *

Miss-penalty-L2) = 1 + 4% * (10 + 50% * 100) = 3.4 CC– Average memory stalls per instruction = Misses-per-instruction-L1 *

Hit-time-L2 + Misses-per-instructions-L2*Miss-penalty-L2= (40*1.5/1000) * 10 + (20*1.5/1000) * 100 = 3.6 CC

• Or (3.4 – 1.0) * 1.5 = 3.6 CC

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Comparing Local and Global Miss Rates

32KB L1 cache

More assumptions are shown inthe legend of Figure 5.10

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Relative Execution Time by L2-Cache Size

Cache size iswhat matters

Reference execution timeof 1.0 is for 8192KB L2cache with 1 CC latencyon a L2 hit

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Comparing Local and Global Miss Rates

• Huge 2nd level caches• Global miss rate close to single level cache rate provided L2

>> L1• Global cache miss rate should be used when evaluating

second-level caches (or 3rd, 4th,… levels of hierarchy)• Many fewer hits than L1, target reduce misses

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Impact of L2 Cache Associativity

• Hit-time-L2 – Direct mapped = 10 CC; 2-way set associativity = 10.1 CC (usually

round up to integral number of CC, 10 or 11 CC)

• Local-miss-rate-L2– Direct mapped = 25%; 2-way set associativity = 20%

• Miss-penalty-L2 = 100CC• Miss-penalty-L2

– Direct mapped = 10 + 25% * 100 = 35 CC– 2-way (10 CC) = 10 + 20% * 100 = 30 CC– 2-way (11 CC) = 11 + 20% * 100 = 31 CC

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Critical Word First and Early Restart

• Do not wait for full block to be loaded before restarting CPU– 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

– Early restart -- as soon as the requested word of the block arrives, send it to the CPU and let the CPU continue execution

• Benefits of critical word first and early restart depend on– Block size: generally useful only in large blocks – Likelihood of another access to the portion of the block that has not

yet been fetched• Spatial locality problem: tend to want next sequential word, so

not clear if benefit


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Giving Priority to Read Misses Over Writes

• In write through, write buffers complicate memory access in that they might hold the updated value of location needed on a read miss– RAW conflicts with main memory reads on cache misses

• Read miss waits until the write buffer empty increase read miss penalty (old MIPS 1000 with 4-word buffer by 50% )

• Check write buffer contents before read, and if no conflicts, let the memory access continue

• Write Back?– Read miss replacing 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 stall less since restarts as soon as do read

SW R3, 512(R0)

LW R1, 1024(R0)

LW R2, 512(R0)

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Merging Write Buffer

• An entry of write buffer often contain multi-words. However, a write often involves single word– A single-word write occupies the whole entry if no write-merging

• Write merging: check to see if the address of a new data matches the address of a valid write buffer entry. If so, the new data are combined with that entry

• Advantage– Multi-word writes are usually faster than single-word writes– Reduce the stalls due to the write buffer being full

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Write-Merging Illustration

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Victim Caches

• Remember what was just discarded in case it is need again• Add small fully associative cache (called victim cache)

between the cache and the refill path– Contain only blocks discarded from a cache because of a miss– Are checked on a miss to see if they have the desired data before

going to the next lower-level of memory• If yes, swap the victim block and cache block

– Addressing both victim and regular cache at the same time• The penalty will not increase

• Jouppi (DEC SRC) shows miss reduction of 20 - 95%– For a 4KB direct mapped cache with 1-5 victim blocks

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Victim Cache Organization

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5.5 Reducing Miss Rate

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Classify Cache Misses - 3 C’s

• Compulsory independent of cache size– First access to a block no choice but to load it– Also called cold-start or first-reference misses– Measured by a infinite cache (ideal)

• Capacity decrease as cache size increases– Cache cannot contain all the blocks needed during execution, then

blocks being discarded will be later retrieved– Measured by a fully associative cache

• Conflict (Collision) decrease as associativity increases– Side effect of set associative or direct mapping– A block may be discarded and later retrieved if too many blocks map

to the same cache block

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Miss Distributions vs. the 3 C’s (Total Miss Rate)

independent of cache sizes

decrease ascapacity increases

decrease as associativity increases

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Miss Distributions

Normalized to direct-mapped organization

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Techniques for Reducing Miss Rate

• Larger Block Size• Larger Caches• Higher Associativity• Way Prediction and Pseudo-associative Caches• Compiler optimizations

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Larger Block Sizes

• Obvious advantages: reduce compulsory misses– Reason is due to spatial locality

• Obvious disadvantage– Higher miss penalty: larger block takes longer to move– May increase conflict misses and capacity miss if cache is small

Don’t let increase in miss penalty outweigh the decrease in miss rate

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Miss Rate VS Block SizeLarger block may increase conflict and capacity miss

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Actual Miss Rate VS. Block Size

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Miss Rate VS. Miss Penalty

• Assume memory system takes 80 CC of overhead and then deliver 16 bytes every 2 CC. Hit time = 1 CC

• Miss penalty– Block size 16 = 80 + 2 = 82– Block size 32 = 80 + 2 * 2 = 84– Block size 256 = 80 + 16 * 2 = 112

• AMAT = hit_time + miss_rate*miss_penalty– 256-byte in a 256 KB cache = 1 + 0.49% * 112 = 1.549 CC

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AMAT VS. Block Size for Different-Size Caches

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Large Caches

• Help with both conflict and capacity misses• May need longer hit time AND/OR higher HW cost• Popular in off-chip caches

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Higher Associativity

• 8-way set associative is for practical purposes as effective in reducing misses as fully associative

• 2: 1 Rule of thumb– 2 way set associative of size N/ 2 is about the same as a direct

mapped cache of size N (held for cache size < 128 KB)

• Greater associativity comes at the cost of increased hit time– Lengthen the clock cycle– Hill [1988] suggested hit time for 2-way vs. 1-way: external cache

+10%, internal + 2%

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Effect of Higher Associativity for AMAT

Clock-cycle-time (2-way) = 1.10 * Clock-cycle-time (1-way)Clock-cycle-time (4-way) = 1.12 * Clock-cycle-time (1-way)Clock-cycle-time (8-way) = 1.14 * Clock-cycle-time (1-way)

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Way Prediction

• Extra bits are kept in cache to predict the way, or block within the set of the next cache access

• Multiplexor is set early to select the desired block, and only a single tag comparison is performed that clock cycle

• A miss results in checking the other blocks for matches in subsequent clock cycles

• Alpha 21264 uses way prediction in its 2-way set-associative instruction cache. Simulation using SPEC95 suggested way prediction accuracy is in excess of 85%

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Pseudo-Associative Caches

• Attempt to get the miss rate of set-associative caches and the hit speed of direct-mapped cache

• Idea– Start with a direct mapped cache– On a miss check another entry– Usual method is to invert the high order index bit to get the next try

• 010111 110111

• Problem - fast hit and slow hit– May have the problem that you mostly need the slow hit– In this case it is better to swap the blocks

• Drawback: CPU pipeline is hard if hit takes 1 or 2 cycles– Better for caches not tied directly to processor (L2)– Used in MIPS R1000 L2 cache, similar in UltraSPARC

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Relationship Between a Regular Hit Time, Pseudo Hit Time and Miss Penalty

Hit Time

Pseudo Hit Time Miss Penalty

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Effect of Pseudo-Associative Caches

• Assume that it takes two extra cycles to find the entry in the alternative location (1 to check and 1 to swap)

• AMAT = Hit-time+ Miss-rate * Miss-penalty– Miss-penalty is 1 cycle more than a normal miss penalty (why??)– Miss-rate * Miss-penalty = Miss-rate(2-way) * Miss-penalty(1-way)– Hit-time = Hit-time(1-way) + Alternate_hit_rate * 2– Alternate-hit-rate = Hit-rate(2-way) – Hit-rate(1-way) = Miss-rate(1-

way) – Miss-rate(2-way)

• AMAT(pseudo) = 4.950 (2K), 1.371 (128K)• AMAT(1-way) = 5.90 (2K), 1.50 (128K)• AMAT(2-way) = 4.90 (2K), 1.45 (128K)

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Compiler Optimization for Code

• Code can easily be arranged without affecting correctness• Reordering the procedures of a program might reduce

instruction miss rates by reducing conflict misses• McFarling's observation using profiling information [1988]

– Reduce miss by 50% for a 2KB direct-mapped instruction cache with 4-byte blocks, and by 75% in an 8KB cache

– Optimized programs on a direct-mapped cache missed less than unoptimized ones on an 8-way set-associative cache of same size

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Compiler Optimization for Data

• Idea – improve the spatial and temporal locality of the data• Lots of options

– Array merging – Allocate arrays so that paired operands show up in same cache block

– Loop interchange – Exchange inner and outer loop order to improve cache performance

– Loop fusion – For independent loops accessing the same data, fuse these loops into a single aggregate loop

– Blocking – Do as much as possible on a sub- block before moving on

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Merging Arrays Example

/* Before: 2 sequential arrays */

int val[SIZE];

int key[SIZE];

/* After: 1 array of stuctures */

struct merge {

int val;

int key;


struct merge merged_array[SIZE];

Reducing conflicts between val & key; improve spatial locality

val key

val key val key val key

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Loop Interchange Example

/* Before */

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 (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; improve spatial locality

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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;Improve temporal locality

Perform different computations on the common data in two loops fuse the two loops

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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;};

Improve temporal locality + spatial locality

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Snapshot of x, y, z when i=1(Figure 5.21)

White: not yet touchedLight: older accessDark: newer access

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Blocking Example (Cont.)

• Dealing with multiple arrays, with some arrays accessed by rows and some by columns– Row-major or column-major-order no help loop interchange no


• Idea: compute on BxB submatrix that fits• 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:– 3 NxNx4 no capacity misses; otherwise ...

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Blocking Example (Cont.)

/* 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,N); j = j+1){r = 0; for (k = kk; k < min(kk+B,N); k = k+1) {

r = r + y[i][k]*z[k][j];}; x[i][j] = x[i][j] + r;};

• B called Blocking Factor• Worst-case capacity Misses from 2N3 + N2 to 2N3/B +N2

• Help register allocation

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The Age of Accesses to x, y, z (Figure 5.22)

Note in contrast to Figure 5.21, the smaller number of elements accessed

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Performance Improvement

1 1.5 2 2.5 3




mxm (nasa7)

btrix (nasa7)


gmty (nasa7)

vpenta (nasa7)



loop fusion blocking

Summary of Compiler Optimizations to Reduce Cache Misses

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5.6 Reducing Cache Miss Penalty or Miss Rate Via Parallelism

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• Overlap the execution of instructions with activity in the memory hierarchy

• Techniques– Non-blocking caches to reduce stalls on cache misses – help with

out-of-order processors– Hardware prefetch of instructions and data– Compiler-controlled prefetching

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Non-blocking Caches

• Non-blocking cache or lockup-free cache allow data cache to continue to supply cache hits during a miss– Requires out-of-order execution CPU, like scoreboard or Tomasulo

• Hit-under-miss: reduces the effective miss penalty by working during miss vs. ignoring CPU requests

• Hit-under-multiple-miss or miss-under-miss: may further lower the effective penalty by overlapping multiple misses– Significantly increases the complexity of the cache controller as

there can be multiple outstanding memory accesses– Requires multiple memory banks (otherwise cannot support)– Pentium Pro allows 4 outstanding memory misses

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Effect of Non-blocking CacheRatio of the average memory stall time (Compare with blocking cache)

FP Avg.:1: 76%2: 51%64: 39%

Int Avg.:1: 81%2: 78%64: 78%

8K DM with 32-byte blocks and 16 CC penalty

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• Compare 2-way set-associativity or hit-under-one-miss under 8KB data caches– FP miss rate: 11.4% (direct-mapped), 10.7% for (2-way)– INT miss rate: 7.4% (direct-mapped), 6.0% for (2-way)

• FP (Miss_rate * Miss_penalty)– Direct-mapped: 11.4% * 16 = 1.84– 2-way: 10.7% * 16 = 1.71– 1.71/1.84 = 93% hit-under-one-miss is better

• Integer (Miss_rate * Miss_penalty)– Direct-mapped: 7.4% * 16 = 1.18– 2-way: 6.0% * 16 = 0.96– 0.96/1.18 = 81% Almost the same

Hit-under-miss does not increase hit time

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Non-Blocking Cache (Cont.)

• Difficult to evaluate performance of non-blocking caches– A cache miss does not necessarily stall the CPU– Effective miss penalty is the nonoverlapped time that CPU is stalled– Difficult to judge the impact of any single miss– Difficult to calculate AMAT

• Out-of-order CPUs are capable of hiding the miss penalty of L1 data cache that hits in L2, but cannot hide a significant fraction of an L2 cache miss

• Possible to be more than one miss requests to same block– Must check on misses to be sure it is not to a block already being

requested to avoid possible inconsistency and to save time

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Hardware Prefetching of I&D

• Use hardware other than the cache to prefetch what you expect to need ahead of time

• AXP 21064 I-fetches 2 blocks on a miss– Target block goes to the I-cache– Next block goes to the instruction stream buffer (ISB)– If requested block is in the ISB then it moves to the Icache and next block

only is promoted from the next lower level.– 1, 4, 16 block ISB catches 15-25%, 50%, 72% of the misses

• Works with data blocks too:– Jouppi: 1 DSB 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 having extra memory bandwidth that can be used without penalty (otherwise would be unused)

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Effect of HW Prefetching

• AMAT(prefetch) = Hit-time + Miss-rate * Prefetch-hit-rate * prefetch-hit-time + Miss-rate * (1-Prefetch-hit-rate) * Miss-penalty

• Parameters– Prefetch-hit-time: 1 clock cycle; prefetch hit rate: 25%– Miss rate: 1.10%(8KB cache); Hit time: 2 clock cycle; Miss penalty:

50 clock cycle

• AMAT(prefetch) = 2.41525• The miss rate of a cache without prefetching has to be

0.83%(8(1.10%) 16(0.64%)) to achieve the equivalent AMAT

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Compiler-Controlled Prefetching

• Data Prefetch– Register Prefetch: load data into register (HP PA-RISC loads)– Cache Prefetch: load into cache (MIPS IV, PowerPC, SPARC v. 9)– Perfetch instruction example: prefetch(b[j+7][0])– Special prefetching instructions cannot cause faults; a form of

speculative execution– Best candidates are loops– Issuing Prefetch Instructions takes time

• Is cost of prefetch issues < savings in reduced misses?

• Also works for instruction prefetch

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5.7 Reducing Hit Time

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Reducing Hit Time

• Hit time is critical because it affects the clock cycle time– On many machines, cache access time limits the clock cycle rate

• A fast hit time is multiplied in importance beyond the average memory access time formula because it helps everything– Average-Memory-Access-Time = Hit-Access-Time + Miss-Rate

* Miss-Penalty• Miss-penalty is clock-cycle dependent

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Techniques for Reducing Hit Time

• Small and Simple Caches• Avoid Address Translation during Indexing of the Cache• Pipelined Cache Access• Trace Caches

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Small and Simple Caches

• A time-consuming portion of a cache hit: use the index portion to read the tag and then compare it to the address

• Small caches – smaller hardware is faster– Keep the L1 cache small enough to fit on the same chip as CPU– Keep the tags on-chip, and the data off-chip for L2 caches

• Simple caches – direct-Mapped cache– Trading hit time for increased miss-rate

• Small direct mapped misses more often than small associative caches

• But simpler structure makes the hit go faster

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Access Time as Size and Associativity Vary in a CMOS Cache

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Virtual Addressed Caches

• Parallel rather than sequential access– Physical addressed caches access the TLB to generate the physical

address, then do the cache access

• Avoid address translation during cache index– Implies virtual addressed cache– Address translation proceeds in parallel with cache index

• If translation indicates that the page is not mapped - then the result of the index is not a hit

• Or if a protection violation occurs - then an exception results• All is well when neither happen

• Too good to be true?

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Virtually Addressed Caches
















Virtually Addressed CacheTranslate only on miss

Synonym (Alias) Problem


$ means cache






PAL2 $

Overlap $ access with VA translation: requires $

index to remain invariantacross translation

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Paging Hardware with TLB

Cacheis here

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Problems with Virtual Caches

• Protection – necessary part of the virtual to physical address translation– Copy protection information on a miss, add a field to hold it, and

check it on every access to virtually addressed cache.

• Task switch causes the same virtual address to refer to different physical address– Hence cache must be flushed

• Creating huge task switch overhead• Also creates huge compulsory miss rates for new process

– Use PID’s as part of the tag to aid discrimination

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Miss Rate of Virtual CachesPIDs increases Uniprocess – 0.3% to 0.5%PIDs saves 0.6% to 4.3% over purging

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Problems with Virtual Caches (Cont.)

• Synonyms or Alias– OS and User code have different virtual addresses which map to the

same physical address (facilitates copy-free sharing)– Two copies of the same data in a virtual cache consistency issue– Anti-aliasing (HW) mechanisms guarantee single copy

• On a miss, check to make sure none match PA of the data being fetched (must VA PA); otherwise, invalidate

– SW can help - e.g. SUN’s version of UNIX• Page coloring - aliases must have same low-order 18 bits

• I/O – use PA– Require mapping to VA to interact with a virtual cache

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Pipelining Writes for Fast Write Hits – Pipelined Cache

• Write hits usually take longer than read hits– Tag must be checked before writing the data

• Pipelines the write– 2 stages – Tag Check and Update Cache (can be more in practice)

– Current write tag check & previous write cache update

• Result– Looks like a write happens on every cycle

– Cycle-time can stay short since real write is spread over

– Mostly works if CPU is not dependent on data from a write

• Spot any problems if read and write ordering is not preserved by the memory system?

• Reads play no part in this pipeline since they already operate in parallel with the tag check

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Trace Caches

• Conventional caches limit the instructions in a static cache block to spatial locality

• Conventional caches may be entered from and exited by a taken branch first and last portion of a block are unused– Taken branches or jumps are 1 in 5 to 10 instructions

• A 64-byte block has 16 instructions space utilization problem

• A trace cache stores instructions only from the branch entry point to the exit of the trace avoid header and trailer overhead

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Trace Cache

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Trace Caches (Cont.)

• Complicated address mapping mechanism, as addresses are no longer aligned to power of 2 multiples of word size

• May store the same instructions multiple time in I-cache– Conditional branches making different choices result in the same

instructions being part of separate traces, which each occupy space in the cache

• Intel NetBurst (foundation of Pentium 4)

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Cache Optimization Summary

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5.9 Main Memory

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Main Memory -- 3 important issues

• Capacity• Latency

– Access time: time between a read is requested and the word arrives– Cycle time: min time between requests to memory (> access time)

• Memory needs the address lines to be stable between accesses– By addressing big chunks - like an entire cache block (amortize the

latency)– Critical to cache performance when the miss is to main

• Bandwidth -- # of bytes read or written per unit time– Affects the time it takes to transfer the block

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Example of Memory Latency and Bandwidth

• Consider– 4 cycle to send the address– 56 cycles per word of access– 4 cycle to transmit the data

• Hence if main memory is organized by word– 64 cycles has to be spent for every word we want to access

• Given a cache line of 4 words (8 bytes per word)– 256 cycles is the miss penalty– Memory bandwidth = 1/8 byte per clock cycle (4 * 8 /256)

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Improving Main Memory Performance

• Simple: – CPU, Cache, Bus, Memory same width (32 or 64 bits)

• Wide: – CPU/Mux 1 word; Mux/Cache, Bus, Memory N words (Alpha: 64 bits

& 256 bits; UtraSPARC 512)

• Interleaved: – CPU, Cache, Bus 1 word: Memory N Modules

(4 Modules); example is word interleaved

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3 Examples of Bus Width, Memory Width, and Memory Interleaving to Achieve Memory Bandwidth

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Wider Main Memory

• Doubling or quadrupling the width of the cache or memory will doubling or quadrupling the memory bandwidth– Miss penalty is reduced correspondingly

• Cost and Drawback– More cost on memory bus– Multiplexer between the cache and the CPU may be on the critical

path (CPU is still access the cache one word at a time)• Multiplexors can be put between L1 and L2

– The design of error correction become more complicated• If only a portion of the block is updated, all other portions must be

read for calculating the new error correction code– Since main memory is traditionally expandable by the customer, the

minimum increment is doubled or quadrupled

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Simple Interleaved Memory

• Memory chips are organized into banks to read or write multiple words at a time, rather than a single word– Share address lines with a memory controller– Keep the memory bus the same but make it run faster– Take advantage of potential memory bandwidth of all DRAMs banks– The banks are often one word wide– Good for accessing consecutive memory location

• Miss penalty of 4 + 56 + 4 * 4 or 76 CC (0.4 bytes per CC)

Organization of Four-way Interleaved Memory

Interleaving factor = #_of_banks (usually power of 2)

Bank_# = address MOD #_of_banksAddress_within_bank = Floor(Address / #_of_banks)

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What Can Interleaving and a Wide Memory Buy?

• Block size = 1, 2, 4 words. Miss rate = 3%, 2% 1.2% correspondingly• Memory Bus width = 1 word, memory access per instruction = 1.2• Cache miss penalty = 64 cycles (as above)• Average cycles per instruction (ignore cache misses) = 2• CPI = 2 + (1.2 * 3% *64) = 4.3 (1-word block)

• Block size = 2 words• 64-bit bus and memory, no interleaving = 2 + (1.2 * 2% * 2 * 64) = 5.07• 64-bit bus and memory, interleaving = 2 + (1.2 * 2% * (4+56+2*4)) = 3.63• 128-bit bus and memory, no interleaving = 2 + (1.2 * 2% * 1* 64) = 3.54

• Block size = 4 words• 64-bit bus and memory, no interleaving = 2 + (1.2 * 1.2% * 4 *64) = 5.69• 64-bit bus and memory, interleaving = 2 + (1.2 * 1.2% * (4+56+4*4)) = 3.09• 128-bit bus and memory, no interleaving = 2 + (1.2 * 1.2% * 2 *64) = 3.84

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Simple Interleaved Memory (Cont.)

• Interleaved memory is logically a wide memory, except that accesses to bank are staged over time to share bus

• How many banks should be included?– More than # of CC to access word in bank

• To achieve the goal that delivering information from a new bank each clock for sequential accesses avoid waiting

• Disadvantages– Making multiple banks are expensive larger chip, few chips

• 512MB RAM– 256 chips of 4M*4 bits 16 banks of 16 chips– 16 chips of 64M*4 bit only 1 bank

– More difficulty in main memory expansion (like wider memory)

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Independent Memory Banks

• Memory banks for independent accesses vs. faster sequential accesses (like wider or interleaved memory)– Multiple memory controller

• Good for…– Multiprocessor I/O– CPU with Hit under n Misses, Non-blocking Cache

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5.9 Memory Technology

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DRAM Technology

• Semiconductor Dynamic Random Access Memory• Emphasize on cost per bit and capacity• Multiplex address lines cutting # of address pins in half

– Row access strobe (RAS) first, then column access strobe (CAS)– Memory as a 2D matrix – rows go to a buffer– Subsequent CAS selects subrow

• Use only a single transistor to store a bit– Reading that bit can destroy the information– Refresh each bit periodically (ex. 8 milliseconds) by writing back

• Keep refreshing time less than 5% of the total time

• DRAM capacity is 4 to 8 times that of SRAM

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DRAM Technology (Cont.)

• DIMM: Dual inline memory module– DRAM chips are commonly sold on small boards called DIMMs– DIMMs typically contain 4 to 16 DRAMs

• Slowing down in DRAM capacity growth– Four times the capacity every three years, for more than 20 years– New chips only double capacity every two year, since 1998

• DRAM performance is growing at a slower rate– RAS (related to latency): 5% per year– CAS (related to bandwidth): 10%+ per year

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RAS improvement

A performance improvement in RAS of about 5% per year

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SRAM Technology

• Cache uses SRAM: Static Random Access Memory• SRAM uses six transistors per bit to prevent the information

from being disturbed when read no need to refresh– SRAM needs only minimal power to retain the charge in the standby

modegood for embedded applications – No difference between access time and cycle time for SRAM

• Emphasize on speed and capacity– SRAM address lines are not multiplexed

• SRAM speed is 8 to 16x that of DRAM

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ROM and Flash

• Embedded processor memory• Read-only memory (ROM)

– Programmed at the time of manufacture– Only a single transistor per bit to represent 1 or 0– Used for the embedded program and for constant– Nonvolatile and indestructible

• Flash memory: – Nonvolatile but allow the memory to be modified– Reads at almost DRAM speeds, but writes 10 to 100 times slower– DRAM capacity per chip and MB per dollar is about 4 to 8 times

greater than flash

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Improving Memory Performance in a Standard DRAM Chip

• Fast page mode: time signals that allow repeated accesses to buffer without another row access time

• Synchronous RAM (SDRAM): add a clock signal to DRAM interface, so that the repeated transfer would not bear overhead to synchronize with the controller– Asynchronous DRAM involves overhead to sync with controller– Peak speed per memory module 800—1200MB/sec in 2001

• Double data rate (DDR): transfer data on both the rising edge and falling edge of DRAM clock signal – Peak speed per memory module 1600—2400MB/sec in 2001

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• RAMBUS optimizes the interface between DRAM and CPU• RAMBUS makes a single chip act more like a memory

system than a memory component– Each chip has interleaved memory and high-speed interface

• 1st generation RAMBUS: RDAM– Replace RAS/CAS with a bus that allows other accesses over it

between the sending of the address and return of the data– Each chip has four banks, each with their own row buffer– A chip can return a variable amount of data from a single request,

and even perform its refresh– Clock signal and transfer on both edges of its clock– 300 MHz clock

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RAMBUS (Cont.)

• 2nd generation RAMBUS: direct RDRAM (DRDRAM)– Offer up to 1.6GB/sec of bandwidth– Separate row- and column-command buses– 18-bit data bus; 16 internal banks; 8 row buffers; 400 MHz

• RAMBUS are sold in RIMMs: one RAMBUS chip per RIMM• RAMBUS vs. DDR SDRAM

– DIMM bandwidth (multiple DRAM chips) is closer to RAMBUS– RDRAM and DRDRAM have a price premium over traditional DRAM

• Larger chips• In 2001, it is factor of 2• Section 5.16 has a detailed price-performance evaluation

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5.10 Virtual Memory

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Virtual Memory

• Virtual memory divides physical memory into blocks (called page or segment) and allocates them to different processes

• With virtual memory, the CPU produces virtual addresses that are translated by a combination of HW and SW to physical addresses, which accesses main memory. The process is called memory mapping or address translation

• Today, the two memory-hierarchy levels controlled by virtual memory are DRAMs and magnetic disks

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Example of Virtual to Physical Address Mapping

Mapping by apage table

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Address Translation Hardware for Paging

frame number frame offset

f (l-n) d (n)

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Page table when some pages are not in main memory…

illegal access

OS puts the process in the backing store when it starts executing.

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Virtual Memory (Cont.)

• Permits applications to grow bigger than main memory size• Helps with multiple process management

– Each process gets its own chunk of memory– Permits protection of 1 process’ chunks from another– Mapping of multiple chunks onto shared physical memory– Mapping also facilitates relocation (a program can run in any

memory location, and can be moved during execution)– Application and CPU run in virtual space (logical memory, 0 – max)– Mapping onto physical space is invisible to the application

• Cache VS. VM– Block becomes a page or segment– Miss becomes a page or address fault

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Typical Page Parameters

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Cache vs. VM Differences

• Replacement– Cache miss handled by hardware– Page fault usually handled by OS

• Addresses– VM space is determined by the address size of the CPU– Cache space is independent of the CPU address size

• Lower level memory– For caches - the main memory is not shared by something else– For VM - most of the disk contains the file system

• File system addressed differently - usually in I/ O space• VM lower level is usually called SWAP space

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2 VM Styles - Paged or Segmented?

• Virtual systems can be categorized into two classes: pages (fixed-size blocks), and segments (variable-size blocks)

Page Segment

Words per address One Two (segment and offset)

Programmer visible? Invisible to application programmer

May be visible to application programmer

Replacing a block Trivial (all blocks are the same size)

Hard (must find contiguous, variable-size, unused portion of main memory)

Memory use inefficiency

Internal fragmentation (unused portion of page)

External fragmentation (unused pieces of main memory)

Efficient disk traffic Yes (adjust page size to balance access time and transfer time)

Not always (small segments may transfer just a few bytes)

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Virtual Memory – The Same 4 Questions

• Block Placement– Choice: lower miss rates and complex placement or vice versa

• Miss penalty is huge, so choose low miss rate place anywhere• Similar to fully associative cache model

• Block Identification - both use additional data structure– Fixed size pages - use a page table– Variable sized segments - segment table

frame number frame offset

f (l-n) d (n)

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Address Translation Hardware for Paging

frame number frame offset

f (l-n) d (n)

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Block Identification Example

11 0113

2 * 4 + 1 = 9 Physical space = 25

Logical space = 24

Page size = 22

PT Size = 24/22= 22

Each PT entry needs 5-2 bits

010 019

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Virtual Memory – The Same 4 Questions (Cont.)

• Block Replacement -- LRU is the best– However true LRU is a bit complex – so use approximation

• Page table contains a use tag, and on access the use tag is set• OS checks them every so often - records what it sees in a data

structure - then clears them all• On a miss the OS decides who has been used the least and

replace that one

• Write Strategy -- always write back– Due to the access time to the disk, write through is silly– Use a dirty bit to only write back pages that have been modified

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Techniques for Fast Address Translation

• Page table is kept in main memory (kernel memory)– Each process has a page table

• Every data/instruction access requires two memory accesses– One for the page table and one for the data/instruction– Can be solved by the use of a special fast-lookup hardware cache

called associative registers or translation look-aside buffers (TLBs)

• If locality applies then cache the recent translation– TLB = translation look-aside buffer– TLB entry: virtual page no, physical page no, protection bit, use bit,

dirty bit

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TLB = Translation Look-aside Buffer

• The TLB must be on chip; otherwise it is worthless– Fully associative – parallel search

• Typical TLB’s– Hit time - 1 cycle– Miss penalty - 10 to 30 cycles– Miss rate - .1% to 2%– TLB size - 32 B to 8 KB

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Paging Hardware with TLB

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TLB of Alpha 21264

Address Space Number: process ID to prevent context switch

A total of 128 TLB entries

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Effective Access Time

• Associative Lookup = time unit (hit time)• Assume memory cycle time is x time unit (miss penalty)• Hit ratio = • Effective Access Time (EAT)

EAT = (x + ) + (2x + )(1 – )

= (2–) * x +

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Effective Access Time (Cont.)

• Example 1– Associate lookup = 20

– Memory access = 100

– Hit ratio = 0.8

– EAT = (100 + 20) * 0.8 + (200 + 20) * 0.2= 1.2 * 100 + 20 = 140

• Example 2– Associate lookup = 20

– Memory access = 100

– Hit ratio = 0.98

– EAT = (100 + 20) * 0.98 + (200 + 20) * 0.02= 1.02 * 100 + 20 = 122

40% slow in memory access time 22% slow in memory access time

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Page Size – An Architectural Choice

• Large pages are good:– Reduces page table size– Amortizes the long disk access– If spatial locality is good then hit rate will improve– Reduce the number of TLB miss

• Large pages are bad:– More internal fragmentation

• If everything is random each structure’s last page is only half full – Process start up time takes longer

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5.11 Protection and Examples of VM

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• Multiprogramming forces us to worry about it• Hence lots of processes

– Hence task switch overhead– HW must provide savable state– OS must promise to save and restore properly– Most machines task switch every few milliseconds– A task switch typically takes several microseconds

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Protection Options

• Simplest - base and bound (valid if Base Address Bound)– 2 registers - check each address falls between the values

• These registers must be changed by the OS but not the application

– Need for 2 modes: regular & privileged• Hence need to privilege-trap and provide mode switch ability

• VM provides another option– Check as part of the VA PA translation process– Memory protection implemented by associating protection bit with

each page• The protection bits reside in the page table & TLB• Read-only, read-write, execute-only

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Hardware Support for Relocation and Limit Registers

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Memory Protection with Valid-Invalid bit

•A process with length 10,468

•10468 – 12287 are also invalid


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Protection Options (Cont.)

• Rings - ala MULTIC’s and now Pentium– Inner is most privileged - outer is least

• Capabilities (i432) - similar to a key or password model– OS hands them out - so they’re difficult to forge– In some cases they can be passed between app’s

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VM Example

• Alpha 21264• Intel Pentium

Paged segmentation & Multi-level Paging

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1. (a) Give the types of “Conflict misses”.

(b) Which principle of locality does the first miss rate reduction technique

address? Explain why? [8+8]

2. (a) Give the three categories of cache organizations based on the block


(b) Describe the block, when it is found in the cache? [8+8]

3. (a) What are fully associative caches? Explain how they are used in calculating

capacity misses?

(b) Define pseudoset .How does the pseudo associative cache works. [8+8]

Assignment Questions