Chapter 5: Large and Fast: Exploiting Memory Hierarchy PDF

COMPUTER ORGANIZATION AND DESIGN 6 Edition th The Hardware/Software Interface Chapter 5 Large and Fast: Exploiting Memory Hierarchy ...

COMPUTER ORGANIZATION AND DESIGN 6 Edition th The Hardware/Software Interface Chapter 5 Large and Fast: Exploiting Memory Hierarchy  §5.1 Introduction 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 Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — 2 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 Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — 3 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 Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — 4  §5.2 Memory Technologies Memory Technology Static RAM (SRAM) 0.5ns – 2.5ns, $500 – $1000 per GB Dynamic RAM (DRAM) 50ns – 70ns, $3 – $6 per GB Magnetic disk 5ms – 20ms, $0.01 – $0.02 per GB Ideal memory Access time of SRAM Capacity and cost/GB of disk Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — 5 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 Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — 6 Flash Storage Nonvolatile semiconductor storage 100× – 1000× faster than disk Smaller, lower power, more robust But more $/GB (between disk and DRAM) Chapter 6 — Storage and Other I/O Topics — 7 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 Chapter 6 — Storage and Other I/O Topics — 8 Disk Storage Nonvolatile, rotating magnetic storage Chapter 6 — Storage and Other I/O Topics — 9 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 Chapter 6 — Storage and Other I/O Topics — 10 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 Chapter 6 — Storage and Other I/O Topics — 11 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 Chapter 6 — Storage and Other I/O Topics — 12  §5.3 The Basics of Caches Cache Memory Cache memory The level of the memory hierarchy closest to the CPU Given accesses X1, …, Xn–1, Xn How do we know if the data is present? Where do we look? Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — 13 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 Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — 14 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 Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — 15 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 Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — 16 Cache Example Word addr Binary addr Hit/miss Cache block 22 10 110 Miss 110 Index V Tag Data 000 N 001 N 010 N 011 N 100 N 101 N 110 Y 10 Mem 111 N Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — 17 Cache Example Word addr Binary addr Hit/miss Cache block 26 11 010 Miss 010 Index V Tag Data 000 N 001 N 010 Y 11 Mem 011 N 100 N 101 N 110 Y 10 Mem 111 N Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — 18 Cache Example Word addr Binary addr Hit/miss Cache block 22 10 110 Hit 110 26 11 010 Hit 010 Index V Tag Data 000 N 001 N 010 Y 11 Mem 011 N 100 N 101 N 110 Y 10 Mem 111 N Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — 19 Cache Example Word addr Binary addr Hit/miss Cache block 16 10 000 Miss 000 3 00 011 Miss 011 16 10 000 Hit 000 Index V Tag Data 000 Y 10 Mem 001 N 010 Y 11 Mem 011 Y 00 Mem 100 N 101 N 110 Y 10 Mem 111 N Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — 20 Cache Example Word addr Binary addr Hit/miss Cache block 18 10 010 Miss 010 Index V Tag Data 000 Y 10 Mem 001 N 010 Y 10 Mem 011 Y 00 Mem 100 N 101 N 110 Y 10 Mem 111 N Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — 21 Address Subdivision Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — 22 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 31 10 9 4 3 0 Tag Index Offset 22 bits 6 bits 4 bits Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — 23 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 Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — 24 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 Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — 25 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 Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — 26 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 Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — 27 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 Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — 28 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% Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — 29 Example: Intrinsity FastMATH Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — 30 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 Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — 31  §5.4 Measuring and Improving Cache Performance Measuring Cache Performance Components of CPU time Program execution cycles Includes cache hit time Memory stall cycles Mainly from cache misses With simplifying assumptions: Memory stall cycles Memory accesses  Miss rate Miss penalty Program Instructio ns Misses   Miss penalty Program Instructio n Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — 32 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 Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — 33 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 Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — 34 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 Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — 35 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) Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — 36 Associative Cache Example Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — 37 Spectrum of Associativity For a cache with 8 entries Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — 38 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 Cache Hit/miss Cache content after access address index 0 1 2 3 0 0 miss Mem 8 0 miss Mem 0 0 miss Mem 6 2 miss Mem Mem 8 0 miss Mem Mem Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — 39 Associativity Example 2-way set associative Block Cache Hit/miss Cache content after access address index Set 0 Set 1 0 0 miss Mem 8 0 miss Mem Mem 0 0 hit Mem Mem 6 0 miss Mem Mem 8 0 miss Mem Mem Fully associative Block Hit/miss Cache content after access address 0 miss Mem 8 miss Mem Mem 0 hit Mem Mem 6 miss Mem Mem Mem 8 hit Mem Mem Mem Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — 40 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 Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — 41 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% Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — 42 Set Associative Cache Organization Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — 43 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 Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — 44 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 Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — 45 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 Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — 46 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 Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — 47 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 Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — 48 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 — 49 Interactions with Software Misses depend on memory access patterns Algorithm behavior Compiler optimization for memory access Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — 50 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 — 51 DGEMM Access Pattern C, A, and B arrays older accesses new accesses Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — 52 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]; 9 for( int k = sk; k < sk+BLOCKSIZE; k++ ) 10 cij += A[i+k*n] * B[k+j*n]; 11 C[i+j*n] = 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 — 53 Blocked DGEMM Access Pattern Unoptimized Blocked Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — 54  §5.5 Dependable Memory Hierarchy Dependability Service accomplishment Service delivered as specified Fault: failure of a component Restoration Failure May or may not lead to system failure Service interruption Deviation from specified service Chapter 6 — Storage and Other I/O Topics — 55 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 — 56 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 — 57 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 — 58 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 — 59 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 — 60  §5.6 Virtual Machines 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 Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — 61 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 — 62 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 — 63 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 — 64  §5.7 Virtual Memory 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 Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — 65 Address Translation Fixed-size pages (e.g., 4K) Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — 66 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 — 67 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 — 68 Translation Using a Page Table Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — 69 Mapping Pages to Storage Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — 70 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 — 71 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 — 72 Fast Translation Using a TLB Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — 73 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 — 74 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 — 75 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 — 76 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 — 77 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 — 78  §5.8 A Common Framework for Memory Hierarchies The Memory Hierarchy The BIG Picture 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 Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — 79 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 — 80 Finding a Block Associativity Location method Tag comparisons Direct mapped Index 1 n-way set Set index, then search n associative entries within the set Fully associative Search all entries #entries Full lookup table 0 Hardware caches Reduce comparisons to reduce cost Virtual memory Full table lookup makes full associativity feasible Benefit in reduced miss rate Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — 81 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 — 82 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 — 83 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 — 84 Cache Design Trade-offs Design change Effect on miss rate Negative performance effect Increase cache size Decrease capacity May increase access misses time Increase associativity Decrease conflict May increase access misses time Increase block size Decrease compulsory Increases miss misses penalty. For very large block size, may increase miss rate due to pollution. Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — 85  §5.9 Using a Finite-State Machine to Control A Simple Cache 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 31 10 9 4 3 0 Tag Index Offset 18 bits 10 bits 4 bits Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — 86 Interface Signals Read/Write Read/Write Valid Valid 32 32 Address Address CPU Write Data 32 Cache Write Data 128 Memory 32 128 Read Data Read Data Ready Ready Multiple cycles per access Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — 87 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 — 88 Cache Controller FSM Could partition into separate states to reduce clock cycle time Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — 89  §5.10 Parallelism and Memory Hierarchies: Cache Coherence Cache Coherence Problem Suppose two CPU cores share a physical address space Write-through caches Time Event CPU A’s CPU B’s Memory step cache cache 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 — 90 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 — 91 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 — 92 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 CPU B’s Memory cache cache 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 — 93 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 — 94  §5.13 The ARM Cortex-A8 and Intel Core i7 Memory Hierarchies Multilevel On-Chip Caches Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — 95 2-Level TLB Organization Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — 96 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 — 97  §5.14 Going Faster: Cache Blocking and Matrix Multiply DGEMM Combine cache blocking and subword parallelism Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — 98  §5.15 Fallacies and Pitfalls 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 Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — 99 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 — 100 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 — 101  §5.16 Concluding Remarks 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 Chapter 5 — Large and Fast: Exploiting Memory Hierarchy — 102