Chapter 5
Chapter 5
Large and Fast: Exploiting Memory Hierarchy
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Chapter 5 — Large and Fast: Exploiting Memory Hierarchy
Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — *
Principle of Locality
Programs access a small proportion of their address space at any time
Temporal locality
Items accessed recently are likely to be accessed again soon
e.g., instructions in a loop, induction variables
Spatial locality
Items near those accessed recently are likely to be accessed soon
E.g., sequential instruction access, array data
§5.1 Introduction
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Taking Advantage of Locality
Memory hierarchy
Store everything on disk
Copy recently accessed (and nearby) items from disk to smaller DRAM memory
Main memory
Copy more recently accessed (and nearby) items from DRAM to smaller SRAM memory
Cache memory attached to CPU
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Chapter 5 — Large and Fast: Exploiting Memory Hierarchy
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Memory Hierarchy Levels
Block (aka line): unit of copying
May be multiple words
If accessed data is present in upper level
Hit: access satisfied by upper level
Hit ratio: hits/accesses
If accessed data is absent
Miss: block copied from lower level
Time taken: miss penalty
Miss ratio: misses/accesses
= 1 – hit ratio
Then accessed data supplied from upper level
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Memory Technology
Static RAM (SRAM)
0.5ns – 2.5ns, $2000 – $5000 per GB
Dynamic RAM (DRAM)
50ns – 70ns, $20 – $75 per GB
Magnetic disk
5ms – 20ms, $0.20 – $2 per GB
Ideal memory
Access time of SRAM
Capacity and cost/GB of disk
§5.2 Memory Technologies
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DRAM Technology
Data stored as a charge in a capacitor
Single transistor used to access the charge
Must periodically be refreshed
Read contents and write back
Performed on a DRAM “row”
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Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — *
Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — *
Advanced DRAM Organization
Bits in a DRAM are organized as a rectangular array
DRAM accesses an entire row
Burst mode: supply successive words from a row with reduced latency
Double data rate (DDR) DRAM
Transfer on rising and falling clock edges
Quad data rate (QDR) DRAM
Separate DDR inputs and outputs
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DRAM Generations
Year Capacity $/GB
1980 64Kbit $1500000
1983 256Kbit $500000
1985 1Mbit $200000
1989 4Mbit $50000
1992 16Mbit $15000
1996 64Mbit $10000
1998 128Mbit $4000
2000 256Mbit $1000
2004 512Mbit $250
2007 1Gbit $50
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DRAM Performance Factors
Row buffer
Allows several words to be read and refreshed in parallel
Synchronous DRAM
Allows for consecutive accesses in bursts without needing to send each address
Improves bandwidth
DRAM banking
Allows simultaneous access to multiple DRAMs
Improves bandwidth
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Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — *
Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — *
Increasing Memory Bandwidth
4-word wide memory
Miss penalty = 1 + 15 + 1 = 17 bus cycles
Bandwidth = 16 bytes / 17 cycles = 0.94 B/cycle
4-bank interleaved memory
Miss penalty = 1 + 15 + 4×1 = 20 bus cycles
Bandwidth = 16 bytes / 20 cycles = 0.8 B/cycle
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Chapter 6 — Storage and Other I/O Topics — *
Flash Storage
Nonvolatile semiconductor storage
100× – 1000× faster than disk
Smaller, lower power, more robust
But more $/GB (between disk and DRAM)
§6.4 Flash Storage
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Flash Types
NOR flash: bit cell like a NOR gate
Random read/write access
Used for instruction memory in embedded systems
NAND flash: bit cell like a NAND gate
Denser (bits/area), but block-at-a-time access
Cheaper per GB
Used for USB keys, media storage, …
Flash bits wears out after 1000’s of accesses
Not suitable for direct RAM or disk replacement
Wear leveling: remap data to less used blocks
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Disk Storage
Nonvolatile, rotating magnetic storage
§6.3 Disk Storage
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Disk Sectors and Access
Each sector records
Sector ID
Data (512 bytes, 4096 bytes proposed)
Error correcting code (ECC)
Used to hide defects and recording errors
Synchronization fields and gaps
Access to a sector involves
Queuing delay if other accesses are pending
Seek: move the heads
Rotational latency
Data transfer
Controller overhead
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Disk Access Example
Given
512B sector, 15,000rpm, 4ms average seek time, 100MB/s transfer rate, 0.2ms controller overhead, idle disk
Average read time
4ms seek time
+ ½ / (15,000/60) = 2ms rotational latency
+ 512 / 100MB/s = 0.005ms transfer time
+ 0.2ms controller delay
= 6.2ms
If actual average seek time is 1ms
Average read time = 3.2ms
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Chapter 6 — Storage and Other I/O Topics — *
Disk Performance Issues
Manufacturers quote average seek time
Based on all possible seeks
Locality and OS scheduling lead to smaller actual average seek times
Smart disk controller allocate physical sectors on disk
Present logical sector interface to host
SCSI, ATA, SATA
Disk drives include caches
Prefetch sectors in anticipation of access
Avoid seek and rotational delay
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Chapter 5 — Large and Fast: Exploiting Memory Hierarchy
Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — *
Cache Memory
Cache memory
The level of the memory hierarchy closest to the CPU
Given accesses X1, …, Xn–1, Xn
§5.3 The Basics of Caches
How do we know if the data is present?
Where do we look?
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Direct Mapped Cache
Location determined by address
Direct mapped: only one choice
(Block address) modulo (#Blocks in cache)
#Blocks is a power of 2
Use low-order address bits
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Tags and Valid Bits
How do we know which particular block is stored in a cache location?
Store block address as well as the data
Actually, only need the high-order bits
Called the tag
What if there is no data in a location?
Valid bit: 1 = present, 0 = not present
Initially 0
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Cache Example
8-blocks, 1 word/block, direct mapped
Initial state
Index V Tag Data
000 N
001 N
010 N
011 N
100 N
101 N
110 N
111 N
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Cache Example
Index V Tag Data
000 N
001 N
010 N
011 N
100 N
101 N
110 Y 10 Mem[10110]
111 N
Word addr Binary addr Hit/miss Cache block
22 10 110 Miss 110
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Cache Example
Index V Tag Data
000 N
001 N
010 Y 11 Mem[11010]
011 N
100 N
101 N
110 Y 10 Mem[10110]
111 N
Word addr Binary addr Hit/miss Cache block
26 11 010 Miss 010
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Cache Example
Index V Tag Data
000 N
001 N
010 Y 11 Mem[11010]
011 N
100 N
101 N
110 Y 10 Mem[10110]
111 N
Word addr Binary addr Hit/miss Cache block
22 10 110 Hit 110
26 11 010 Hit 010
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Chapter 5 — Large and Fast: Exploiting Memory Hierarchy
Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — *
Cache Example
Index V Tag Data
000 Y 10 Mem[10000]
001 N
010 Y 11 Mem[11010]
011 Y 00 Mem[00011]
100 N
101 N
110 Y 10 Mem[10110]
111 N
Word addr Binary addr Hit/miss Cache block
16 10 000 Miss 000
3 00 011 Miss 011
16 10 000 Hit 000
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Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — *
Cache Example
Index V Tag Data
000 Y 10 Mem[10000]
001 N
010 Y 10 Mem[10010]
011 Y 00 Mem[00011]
100 N
101 N
110 Y 10 Mem[10110]
111 N
Word addr Binary addr Hit/miss Cache block
18 10 010 Miss 010
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Address Subdivision
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Example: Larger Block Size
64 blocks, 16 bytes/block
To what block number does address 1200 map?
Block address = 1200/16 = 75
Block number = 75 modulo 64 = 11
Tag
Index
Offset
0
3
4
9
10
31
4 bits
6 bits
22 bits
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Block Size Considerations
Larger blocks should reduce miss rate
Due to spatial locality
But in a fixed-sized cache
Larger blocks fewer of them
More competition increased miss rate
Larger blocks pollution
Larger miss penalty
Can override benefit of reduced miss rate
Early restart and critical-word-first can help
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Chapter 5 — Large and Fast: Exploiting Memory Hierarchy
Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — *
Cache Misses
On cache hit, CPU proceeds normally
On cache miss
Stall the CPU pipeline
Fetch block from next level of hierarchy
Instruction cache miss
Restart instruction fetch
Data cache miss
Complete data access
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Write-Through
On data-write hit, could just update the block in cache
But then cache and memory would be inconsistent
Write through: also update memory
But makes writes take longer
e.g., if base CPI = 1, 10% of instructions are stores, write to memory takes 100 cycles
Effective CPI = 1 + 0.1×100 = 11
Solution: write buffer
Holds data waiting to be written to memory
CPU continues immediately
Only stalls on write if write buffer is already full
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Write-Back
Alternative: On data-write hit, just update the block in cache
Keep track of whether each block is dirty
When a dirty block is replaced
Write it back to memory
Can use a write buffer to allow replacing block to be read first
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Write Allocation
What should happen on a write miss?
Alternatives for write-through
Allocate on miss: fetch the block
Write around: don’t fetch the block
Since programs often write a whole block before reading it (e.g., initialization)
For write-back
Usually fetch the block
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Example: Intrinsity FastMATH
Embedded MIPS processor
12-stage pipeline
Instruction and data access on each cycle
Split cache: separate I-cache and D-cache
Each 16KB: 256 blocks × 16 words/block
D-cache: write-through or write-back
SPEC2000 miss rates
I-cache: 0.4%
D-cache: 11.4%
Weighted average: 3.2%
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Example: Intrinsity FastMATH
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Main Memory Supporting Caches
Use DRAMs for main memory
Fixed width (e.g., 1 word)
Connected by fixed-width clocked bus
Bus clock is typically slower than CPU clock
Example cache block read
1 bus cycle for address transfer
15 bus cycles per DRAM access
1 bus cycle per data transfer
For 4-word block, 1-word-wide DRAM
Miss penalty = 1 + 4×15 + 4×1 = 65 bus cycles
Bandwidth = 16 bytes / 65 cycles = 0.25 B/cycle
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Measuring Cache Performance
Components of CPU time
Program execution cycles
Includes cache hit time
Memory stall cycles
Mainly from cache misses
With simplifying assumptions:
§5.4 Measuring and Improving Cache Performance
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Cache Performance Example
Given
I-cache miss rate = 2%
D-cache miss rate = 4%
Miss penalty = 100 cycles
Base CPI (ideal cache) = 2
Load & stores are 36% of instructions
Miss cycles per instruction
I-cache: 0.02 × 100 = 2
D-cache: 0.36 × 0.04 × 100 = 1.44
Actual CPI = 2 + 2 + 1.44 = 5.44
Ideal CPU is 5.44/2 =2.72 times faster
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Average Access Time
Hit time is also important for performance
Average memory access time (AMAT)
AMAT = Hit time + Miss rate × Miss penalty
Example
CPU with 1ns clock, hit time = 1 cycle, miss penalty = 20 cycles, I-cache miss rate = 5%
AMAT = 1 + 0.05 × 20 = 2ns
2 cycles per instruction
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Performance Summary
When CPU performance increased
Miss penalty becomes more significant
Decreasing base CPI
Greater proportion of time spent on memory stalls
Increasing clock rate
Memory stalls account for more CPU cycles
Can’t neglect cache behavior when evaluating system performance
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Associative Caches
Fully associative
Allow a given block to go in any cache entry
Requires all entries to be searched at once
Comparator per entry (expensive)
n-way set associative
Each set contains n entries
Block number determines which set
(Block number) modulo (#Sets in cache)
Search all entries in a given set at once
n comparators (less expensive)
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Associative Cache Example
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Spectrum of Associativity
For a cache with 8 entries
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Associativity Example
Compare 4-block caches
Direct mapped, 2-way set associative,
fully associative
Block access sequence: 0, 8, 0, 6, 8
Direct mapped
Block address Cache index Hit/miss Cache content after access
0 1 2 3
0 0 miss Mem[0]
8 0 miss Mem[8]
0 0 miss Mem[0]
6 2 miss Mem[0] Mem[6]
8 0 miss Mem[8] Mem[6]
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Associativity Example
2-way set associative
Fully associative
Block address Cache index Hit/miss Cache content after access
Set 0 Set 1
0 0 miss Mem[0]
8 0 miss Mem[0] Mem[8]
0 0 hit Mem[0] Mem[8]
6 0 miss Mem[0] Mem[6]
8 0 miss Mem[8] Mem[6]
Block address Hit/miss Cache content after access
0 miss Mem[0]
8 miss Mem[0] Mem[8]
0 hit Mem[0] Mem[8]
6 miss Mem[0] Mem[8] Mem[6]
8 hit Mem[0] Mem[8] Mem[6]
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How Much Associativity
Increased associativity decreases miss rate
But with diminishing returns
Simulation of a system with 64KB
D-cache, 16-word blocks, SPEC2000
1-way: 10.3%
2-way: 8.6%
4-way: 8.3%
8-way: 8.1%
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Set Associative Cache Organization
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Replacement Policy
Direct mapped: no choice
Set associative
Prefer non-valid entry, if there is one
Otherwise, choose among entries in the set
Least-recently used (LRU)
Choose the one unused for the longest time
Simple for 2-way, manageable for 4-way, too hard beyond that
Random
Gives approximately the same performance as LRU for high associativity
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Multilevel Caches
Primary cache attached to CPU
Small, but fast
Level-2 cache services misses from primary cache
Larger, slower, but still faster than main memory
Main memory services L-2 cache misses
Some high-end systems include L-3 cache
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Multilevel Cache Example
Given
CPU base CPI = 1, clock rate = 4GHz
Miss rate/instruction = 2%
Main memory access time = 100ns
With just primary cache
Miss penalty = 100ns/0.25ns = 400 cycles
Effective CPI = 1 + 0.02 × 400 = 9
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Example (cont.)
Now add L-2 cache
Access time = 5ns
Global miss rate to main memory = 0.5%
Primary miss with L-2 hit
Penalty = 5ns/0.25ns = 20 cycles
Primary miss with L-2 miss
Extra penalty = 500 cycles
CPI = 1 + 0.02 × 20 + 0.005 × 400 = 3.4
Performance ratio = 9/3.4 = 2.6
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Multilevel Cache Considerations
Primary cache
Focus on minimal hit time
L-2 cache
Focus on low miss rate to avoid main memory access
Hit time has less overall impact
Results
L-1 cache usually smaller than a single cache
L-1 block size smaller than L-2 block size
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Interactions with Advanced CPUs
Out-of-order CPUs can execute instructions during cache miss
Pending store stays in load/store unit
Dependent instructions wait in reservation stations
Independent instructions continue
Effect of miss depends on program data flow
Much harder to analyse
Use system simulation
Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — *
Morgan Kaufmann Publishers
Morgan Kaufmann Publishers
*
Chapter 5 — Large and Fast: Exploiting Memory Hierarchy
*
Chapter 5 — Large and Fast: Exploiting Memory Hierarchy
Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — *
Interactions with Software
Misses depend on memory access patterns
Algorithm behavior
Compiler optimization for memory access
Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — *
Morgan Kaufmann Publishers
Morgan Kaufmann Publishers
*
Chapter 5 — Large and Fast: Exploiting Memory Hierarchy
*
Chapter 5 — Large and Fast: Exploiting Memory Hierarchy
Software Optimization via Blocking
Goal: maximize accesses to data before it is replaced
Consider inner loops of DGEMM:
for (int j = 0; j < n; ++j) { double cij = C[i+j*n]; for( int k = 0; k < n; k++ ) cij += A[i+k*n] * B[k+j*n]; C[i+j*n] = cij; } Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * DGEMM Access Pattern C, A, and B arrays Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * older accesses new accesses Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Cache Blocked DGEMM 1 #define BLOCKSIZE 32 2 void do_block (int n, int si, int sj, int sk, double *A, double 3 *B, double *C) 4 { 5 for (int i = si; i < si+BLOCKSIZE; ++i) 6 for (int j = sj; j < sj+BLOCKSIZE; ++j) 7 { 8 double cij = C[i+j*n];/* cij = C[i][j] */ 9 for( int k = sk; k < sk+BLOCKSIZE; k++ ) 10 cij += A[i+k*n] * B[k+j*n];/* cij+=A[i][k]*B[k][j] */ 11 C[i+j*n] = cij;/* C[i][j] = cij */ 12 } 13 } 14 void dgemm (int n, double* A, double* B, double* C) 15 { 16 for ( int sj = 0; sj < n; sj += BLOCKSIZE ) 17 for ( int si = 0; si < n; si += BLOCKSIZE ) 18 for ( int sk = 0; sk < n; sk += BLOCKSIZE ) 19 do_block(n, si, sj, sk, A, B, C); 20 } Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Blocked DGEMM Access Pattern Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Unoptimized Blocked Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Chapter 6 — Storage and Other I/O Topics — * Dependability Fault: failure of a component May or may not lead to system failure Service accomplishment Service delivered as specified Service interruption Deviation from specified service Failure Restoration §5.5 Dependable Memory Hierarchy Chapter 6 — Storage and Other I/O Topics — * Morgan Kaufmann Publishers Morgan Kaufmann Publishers * Chapter 6 — Storage and Other I/O Topics * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy Chapter 6 — Storage and Other I/O Topics — * Dependability Measures Reliability: mean time to failure (MTTF) Service interruption: mean time to repair (MTTR) Mean time between failures MTBF = MTTF + MTTR Availability = MTTF / (MTTF + MTTR) Improving Availability Increase MTTF: fault avoidance, fault tolerance, fault forecasting Reduce MTTR: improved tools and processes for diagnosis and repair Chapter 6 — Storage and Other I/O Topics — * Morgan Kaufmann Publishers Morgan Kaufmann Publishers * Chapter 6 — Storage and Other I/O Topics * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy The Hamming SEC Code Hamming distance Number of bits that are different between two bit patterns Minimum distance = 2 provides single bit error detection E.g. parity code Minimum distance = 3 provides single error correction, 2 bit error detection Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Encoding SEC To calculate Hamming code: Number bits from 1 on the left All bit positions that are a power 2 are parity bits Each parity bit checks certain data bits: Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Decoding SEC Value of parity bits indicates which bits are in error Use numbering from encoding procedure E.g. Parity bits = 0000 indicates no error Parity bits = 1010 indicates bit 10 was flipped Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * SEC/DEC Code Add an additional parity bit for the whole word (pn) Make Hamming distance = 4 Decoding: Let H = SEC parity bits H even, pn even, no error H odd, pn odd, correctable single bit error H even, pn odd, error in pn bit H odd, pn even, double error occurred Note: ECC DRAM uses SEC/DEC with 8 bits protecting each 64 bits Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Virtual Machines Host computer emulates guest operating system and machine resources Improved isolation of multiple guests Avoids security and reliability problems Aids sharing of resources Virtualization has some performance impact Feasible with modern high-performance comptuers Examples IBM VM/370 (1970s technology!) VMWare Microsoft Virtual PC §5.6 Virtual Machines Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Morgan Kaufmann Publishers Morgan Kaufmann Publishers * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Virtual Machine Monitor Maps virtual resources to physical resources Memory, I/O devices, CPUs Guest code runs on native machine in user mode Traps to VMM on privileged instructions and access to protected resources Guest OS may be different from host OS VMM handles real I/O devices Emulates generic virtual I/O devices for guest Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Morgan Kaufmann Publishers Morgan Kaufmann Publishers * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Example: Timer Virtualization In native machine, on timer interrupt OS suspends current process, handles interrupt, selects and resumes next process With Virtual Machine Monitor VMM suspends current VM, handles interrupt, selects and resumes next VM If a VM requires timer interrupts VMM emulates a virtual timer Emulates interrupt for VM when physical timer interrupt occurs Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Morgan Kaufmann Publishers Morgan Kaufmann Publishers * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Instruction Set Support User and System modes Privileged instructions only available in system mode Trap to system if executed in user mode All physical resources only accessible using privileged instructions Including page tables, interrupt controls, I/O registers Renaissance of virtualization support Current ISAs (e.g., x86) adapting Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Morgan Kaufmann Publishers Morgan Kaufmann Publishers * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Virtual Memory Use main memory as a “cache” for secondary (disk) storage Managed jointly by CPU hardware and the operating system (OS) Programs share main memory Each gets a private virtual address space holding its frequently used code and data Protected from other programs CPU and OS translate virtual addresses to physical addresses VM “block” is called a page VM translation “miss” is called a page fault §5.7 Virtual Memory Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Morgan Kaufmann Publishers Morgan Kaufmann Publishers * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Address Translation Fixed-size pages (e.g., 4K) Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Morgan Kaufmann Publishers Morgan Kaufmann Publishers * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Page Fault Penalty On page fault, the page must be fetched from disk Takes millions of clock cycles Handled by OS code Try to minimize page fault rate Fully associative placement Smart replacement algorithms Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Morgan Kaufmann Publishers Morgan Kaufmann Publishers * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Page Tables Stores placement information Array of page table entries, indexed by virtual page number Page table register in CPU points to page table in physical memory If page is present in memory PTE stores the physical page number Plus other status bits (referenced, dirty, …) If page is not present PTE can refer to location in swap space on disk Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Morgan Kaufmann Publishers Morgan Kaufmann Publishers * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Translation Using a Page Table Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Morgan Kaufmann Publishers Morgan Kaufmann Publishers * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Mapping Pages to Storage Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Morgan Kaufmann Publishers Morgan Kaufmann Publishers * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Replacement and Writes To reduce page fault rate, prefer least-recently used (LRU) replacement Reference bit (aka use bit) in PTE set to 1 on access to page Periodically cleared to 0 by OS A page with reference bit = 0 has not been used recently Disk writes take millions of cycles Block at once, not individual locations Write through is impractical Use write-back Dirty bit in PTE set when page is written Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Morgan Kaufmann Publishers Morgan Kaufmann Publishers * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Fast Translation Using a TLB Address translation would appear to require extra memory references One to access the PTE Then the actual memory access But access to page tables has good locality So use a fast cache of PTEs within the CPU Called a Translation Look-aside Buffer (TLB) Typical: 16–512 PTEs, 0.5–1 cycle for hit, 10–100 cycles for miss, 0.01%–1% miss rate Misses could be handled by hardware or software Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Morgan Kaufmann Publishers Morgan Kaufmann Publishers * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Fast Translation Using a TLB Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Morgan Kaufmann Publishers Morgan Kaufmann Publishers * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * TLB Misses If page is in memory Load the PTE from memory and retry Could be handled in hardware Can get complex for more complicated page table structures Or in software Raise a special exception, with optimized handler If page is not in memory (page fault) OS handles fetching the page and updating the page table Then restart the faulting instruction Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Morgan Kaufmann Publishers Morgan Kaufmann Publishers * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * TLB Miss Handler TLB miss indicates Page present, but PTE not in TLB Page not preset Must recognize TLB miss before destination register overwritten Raise exception Handler copies PTE from memory to TLB Then restarts instruction If page not present, page fault will occur Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Morgan Kaufmann Publishers Morgan Kaufmann Publishers * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Page Fault Handler Use faulting virtual address to find PTE Locate page on disk Choose page to replace If dirty, write to disk first Read page into memory and update page table Make process runnable again Restart from faulting instruction Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Morgan Kaufmann Publishers Morgan Kaufmann Publishers * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * TLB and Cache Interaction If cache tag uses physical address Need to translate before cache lookup Alternative: use virtual address tag Complications due to aliasing Different virtual addresses for shared physical address Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Morgan Kaufmann Publishers Morgan Kaufmann Publishers * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Memory Protection Different tasks can share parts of their virtual address spaces But need to protect against errant access Requires OS assistance Hardware support for OS protection Privileged supervisor mode (aka kernel mode) Privileged instructions Page tables and other state information only accessible in supervisor mode System call exception (e.g., syscall in MIPS) Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Morgan Kaufmann Publishers Morgan Kaufmann Publishers * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * The Memory Hierarchy Common principles apply at all levels of the memory hierarchy Based on notions of caching At each level in the hierarchy Block placement Finding a block Replacement on a miss Write policy §5.8 A Common Framework for Memory Hierarchies The BIG Picture Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Morgan Kaufmann Publishers Morgan Kaufmann Publishers * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Block Placement Determined by associativity Direct mapped (1-way associative) One choice for placement n-way set associative n choices within a set Fully associative Any location Higher associativity reduces miss rate Increases complexity, cost, and access time Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Morgan Kaufmann Publishers Morgan Kaufmann Publishers * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Finding a Block Hardware caches Reduce comparisons to reduce cost Virtual memory Full table lookup makes full associativity feasible Benefit in reduced miss rate Associativity Location method Tag comparisons Direct mapped Index 1 n-way set associative Set index, then search entries within the set n Fully associative Search all entries #entries Full lookup table 0 Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Morgan Kaufmann Publishers Morgan Kaufmann Publishers * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Replacement Choice of entry to replace on a miss Least recently used (LRU) Complex and costly hardware for high associativity Random Close to LRU, easier to implement Virtual memory LRU approximation with hardware support Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Morgan Kaufmann Publishers Morgan Kaufmann Publishers * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Write Policy Write-through Update both upper and lower levels Simplifies replacement, but may require write buffer Write-back Update upper level only Update lower level when block is replaced Need to keep more state Virtual memory Only write-back is feasible, given disk write latency Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Morgan Kaufmann Publishers Morgan Kaufmann Publishers * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Sources of Misses Compulsory misses (aka cold start misses) First access to a block Capacity misses Due to finite cache size A replaced block is later accessed again Conflict misses (aka collision misses) In a non-fully associative cache Due to competition for entries in a set Would not occur in a fully associative cache of the same total size Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Morgan Kaufmann Publishers Morgan Kaufmann Publishers * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Cache Design Trade-offs Design change Effect on miss rate Negative performance effect Increase cache size Decrease capacity misses May increase access time Increase associativity Decrease conflict misses May increase access time Increase block size Decrease compulsory misses Increases miss penalty. For very large block size, may increase miss rate due to pollution. Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Morgan Kaufmann Publishers Morgan Kaufmann Publishers * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Cache Control Example cache characteristics Direct-mapped, write-back, write allocate Block size: 4 words (16 bytes) Cache size: 16 KB (1024 blocks) 32-bit byte addresses Valid bit and dirty bit per block Blocking cache CPU waits until access is complete §5.9 Using a Finite State Machine to Control A Simple Cache Tag Index Offset 0 3 4 9 10 31 4 bits 10 bits 18 bits Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Morgan Kaufmann Publishers Morgan Kaufmann Publishers * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Interface Signals Cache CPU Memory Read/Write Valid Address Write Data Read Data Ready 32 32 32 Read/Write Valid Address Write Data Read Data Ready 32 128 128 Multiple cycles per access Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Morgan Kaufmann Publishers Morgan Kaufmann Publishers * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Finite State Machines Use an FSM to sequence control steps Set of states, transition on each clock edge State values are binary encoded Current state stored in a register Next state = fn (current state, current inputs) Control output signals = fo (current state) Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Morgan Kaufmann Publishers Morgan Kaufmann Publishers * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Cache Controller FSM Could partition into separate states to reduce clock cycle time Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Morgan Kaufmann Publishers Morgan Kaufmann Publishers * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Cache Coherence Problem Suppose two CPU cores share a physical address space Write-through caches §5.10 Parallelism and Memory Hierarchies: Cache Coherence Time step Event CPU A’s cache CPU B’s cache Memory 0 0 1 CPU A reads X 0 0 2 CPU B reads X 0 0 0 3 CPU A writes 1 to X 1 0 1 Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Morgan Kaufmann Publishers Morgan Kaufmann Publishers * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Coherence Defined Informally: Reads return most recently written value Formally: P writes X; P reads X (no intervening writes) read returns written value P1 writes X; P2 reads X (sufficiently later) read returns written value c.f. CPU B reading X after step 3 in example P1 writes X, P2 writes X all processors see writes in the same order End up with the same final value for X Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Morgan Kaufmann Publishers Morgan Kaufmann Publishers * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Cache Coherence Protocols Operations performed by caches in multiprocessors to ensure coherence Migration of data to local caches Reduces bandwidth for shared memory Replication of read-shared data Reduces contention for access Snooping protocols Each cache monitors bus reads/writes Directory-based protocols Caches and memory record sharing status of blocks in a directory Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Morgan Kaufmann Publishers Morgan Kaufmann Publishers * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Invalidating Snooping Protocols Cache gets exclusive access to a block when it is to be written Broadcasts an invalidate message on the bus Subsequent read in another cache misses Owning cache supplies updated value CPU activity Bus activity CPU A’s cache CPU B’s cache Memory 0 CPU A reads X Cache miss for X 0 0 CPU B reads X Cache miss for X 0 0 0 CPU A writes 1 to X Invalidate for X 1 0 CPU B read X Cache miss for X 1 1 1 Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Morgan Kaufmann Publishers Morgan Kaufmann Publishers * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Memory Consistency When are writes seen by other processors “Seen” means a read returns the written value Can’t be instantaneously Assumptions A write completes only when all processors have seen it A processor does not reorder writes with other accesses Consequence P writes X then writes Y all processors that see new Y also see new X Processors can reorder reads, but not writes Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Morgan Kaufmann Publishers Morgan Kaufmann Publishers * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Multilevel On-Chip Caches §5.13 The ARM Cortex-A8 and Intel Core i7 Memory Hierarchies Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Morgan Kaufmann Publishers Morgan Kaufmann Publishers * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * 2-Level TLB Organization Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Morgan Kaufmann Publishers Morgan Kaufmann Publishers * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Supporting Multiple Issue Both have multi-banked caches that allow multiple accesses per cycle assuming no bank conflicts Core i7 cache optimizations Return requested word first Non-blocking cache Hit under miss Miss under miss Data prefetching Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Morgan Kaufmann Publishers Morgan Kaufmann Publishers * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy DGEMM Combine cache blocking and subword parallelism Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * §5.14 Going Faster: Cache Blocking and Matrix Multiply Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Pitfalls Byte vs. word addressing Example: 32-byte direct-mapped cache, 4-byte blocks Byte 36 maps to block 1 Word 36 maps to block 4 Ignoring memory system effects when writing or generating code Example: iterating over rows vs. columns of arrays Large strides result in poor locality §5.15 Fallacies and Pitfalls Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Morgan Kaufmann Publishers Morgan Kaufmann Publishers * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Pitfalls In multiprocessor with shared L2 or L3 cache Less associativity than cores results in conflict misses More cores need to increase associativity Using AMAT to evaluate performance of out-of-order processors Ignores effect of non-blocked accesses Instead, evaluate performance by simulation Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Morgan Kaufmann Publishers Morgan Kaufmann Publishers * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Pitfalls Extending address range using segments E.g., Intel 80286 But a segment is not always big enough Makes address arithmetic complicated Implementing a VMM on an ISA not designed for virtualization E.g., non-privileged instructions accessing hardware resources Either extend ISA, or require guest OS not to use problematic instructions Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Morgan Kaufmann Publishers Morgan Kaufmann Publishers * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Concluding Remarks Fast memories are small, large memories are slow We really want fast, large memories Caching gives this illusion Principle of locality Programs use a small part of their memory space frequently Memory hierarchy L1 cache L2 cache … DRAM memory disk Memory system design is critical for multiprocessors §5.16 Concluding Remarks Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — * Morgan Kaufmann Publishers Morgan Kaufmann Publishers * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy * Chapter 5 — Large and Fast: Exploiting Memory Hierarchy 0 50 100 150 200 250 300 '80'83'85'89'92'96'98'00'04'07 Trac Tcac penalty Miss n Instructio Misses Program ns Instructio penalty Miss rate Miss Program accesses Memory cycles stall Memory ´ ´ = ´ ´ =