MemoryHierarchyReview - Emerging Technologies of Emerging...

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Unformatted text preview: Emerging Technologies of Emerging Technologies of Computation Montek Singh COMP790­084 Oct 25, 2011 REVIEW: Memory Hierarchy REVIEW: Memory Hierarchy Today: Review Memory Flavors Principle of Locality Program Traces Memory Hierarchies Associativity What Do We Want in a Memory? What Do We Want in a Memory? PC ADDR INST DOUT miniMIPS MADDR MDATA Wr MEMORY ADDR DATA R/W Capacity Late ncy Cos t 1000’s of bits 10 ps $$$$ SRAM 1-4 Mbytes 0.2 ns $$$ DRAM 1-4 Gbytes 5 ns $ Hard disk* 100’s Gbytes 10 ms ¢ *non-volatile Want? 2-10 Gbytes 0.2ns cheap! Register Best of Both Worlds Best of Both Worlds What we REALLY want: A BIG, FAST memory! Keep everything within instant access We’d like to have a memory system that Performs like 2­10 GB of fast SRAM typical SRAM sizes are in MB, not GB Costs like 1­4 GB of DRAM (slower) typical DRAMs are order of magnitude slower than SRAM SURPRISE: We can (nearly) get our wish! Key Idea: Use a hierarchy of memory technologies: S RAM CPU MAIN MEM DIS K Key Idea Key Idea Key Idea: Exploit “Principle of Locality” Keep data used often in a small fast SRAM called “CACHE”, often on the same chip as the CPU Keep all data in a bigger but slower DRAM called “main memory”, usually separate chip Access Main Memory only rarely, for remaining data The reason this strategy works: LOCALITY if you access something now, you will likely access it again (or its neighbors) soon Locality of Reference: Locality of Reference: Reference to location X atttime tt implies Reference to location X a time implies tthatreference to location X +∆ X atttime hat reference to location X +∆ X a time tt+∆tt is likely for small ∆ X and ∆ tt. +∆ is likely for small ∆ X and ∆ . Cache Cache cache (kash) n. A hiding place used especially for storing provisions. A place for concealment and safekeeping, as of valuables. The store of goods or valuables concealed in a hiding place. Computer Science. A fast storage buffer in the central processing unit of a computer. In this sense, also called cache memory. v. tr. cached, cach∙ing, cach∙es. To hide or store in a cache. Cache Analogy Cache Analogy You are writing a term paper for your history class at a table in the library As you work you realize you need a book You stop writing, fetch the reference, continue writing You don’t immediately return the book, maybe you’ll need it again Soon you have a few books at your table, and you can work smoothly without needing to fetch more books from the shelves The table is a CACHE for the rest of the library Now you switch to doing your biology homework You need to fetch your biology textbook from the shelf If your table is full, you need to return one of the history books back to the shelf to make room for the biology book Typical Memory Reference Patterns Typical Memory Reference Patterns Memory Trace A temporal sequence of memory references (addresses) from a real program. Temporal Locality address stack If an item is referenced, it will tend to be referenced again soon data Spatial Locality If an item is referenced, nearby items will tend to be referenced soon. program time Exploiting the Memory Hierarchy Exploiting the Memory Hierarchy Approach 1 (Cray, others): Expose Hierarchy Registers, Main Memory, Disk each available as explicit storage alternatives MAIN S RAM MEM Tell programmers: “Use them cleverly” CPU Approach 2: Hide Hierarchy Programming model: SINGLE kind of memory, single address space. Transparent to programmer: Machine AUTOMATICALLY assigns locations, depending on runtime usage patterns. CPU S mall S RAM Dynamic RAM “CACHE” “MAIN MEMORY” HARD DIS K Exploiting the Memory Hierarchy Exploiting the Memory Hierarchy CPU speed is dominated by memory performance More significant than: ISA, circuit optimization, pipelining, etc . CPU S mall S RAM Dynamic RAM HARD DIS K “CACHE” “MAIN MEMORY” “VIRTUAL MEMORY” “S WAP S PACE” TRICK #1: Make slow MAIN MEMORY appear faster Technique: CACHING TRICK #2: Make small MAIN MEMORY appear bigger Technique: VIRTUAL MEMORY The Cache Idea The Cache Idea Program­Transparent Memory Hierarchy: Cache contains TEMPORARY COPIES of selected main memory locations… e.g. Mem[100] = 37 (1.0-α) 1.0 CPU 100 37 Two Goals: Cache Improve the average memory access time HIT RATIO (α) : Fraction of refs found in cache MISS RATIO (1­α): Remaining references average total access time depends on these parameters t ave = αt c + (1 − α )(t c + t m ) = t c + (1 − α ) t m Transparency (compatibility, programming ease) DYNAMIC RAM Main Memory Challenge: To make the hit ratio as high as possible. How High of a Hit Ratio? How High of a Hit Ratio? Suppose we can easily build an on­chip SRAM with a 0.8 ns access time, but the fastest DRAM we can buy for main memory has access time of 10 ns. How high of a hit rate do we need to sustain an average total access time of 1 ns? t ave − tc 1 − 0.8 α = 1− = 1− = 98% tm 10 Wow, a cache really needs to be good! Cache Cache Sits between CPU and main memory Very fast table that stores a TAG and DATA TAG is the memory address Main Memory DATA is a copy of memory contents at the address given by TAG Tag Data 1000 17 1004 23 1008 11 1000 17 1012 5 1040 1 1016 29 1032 97 1020 38 1008 11 1024 44 Cache 1028 99 1032 97 1036 25 1040 1 1044 4 Cache Access Cache Access On load (lw) we look in the TAG entries for the address we’re loading Found a HIT, return the DATA Not Found a MISS, go to memory for the data and put it and the address (TAG) in the cache Tag Data Main Memory 1000 17 1004 23 1008 11 1000 17 1012 5 1040 1 1016 29 1032 97 1020 38 1008 11 1024 44 Cache 1028 99 1032 97 1036 25 1040 1 1044 4 Cache Lines Cache Lines Usually get more data than requested a LINE is the unit of memory stored in the cache usually much bigger than 1 word, 32 bytes per line is common bigger LINE means fewer misses because of spatial locality but bigger LINE means longer time on miss Main Memory 1000 17 1004 23 1008 11 1012 5 Tag Data 1016 29 1020 38 1000 17 23 1040 1 4 1032 97 25 1032 97 1008 11 5 1036 25 Cache 1024 44 1028 99 1040 1 1044 4 Finding the TAG in the Cache Finding the TAG in the Cache Suppose requested data could be anywhere in cache: This is called a Fully Associative cache A 1MB cache may have 32k different lines each of 32 bytes We can’t afford to sequentially search the 32k different tags Fully Associative cache uses hardware to compare the address to the tags in parallel but it is expensive Incoming Address TAG Data =? TAG Data HIT =? TAG =? Data Data Out Finding the TAG in the Cache Finding the TAG in the Cache Fully Associative: Requested data could be anywhere in the cache Fully Associative cache uses hardware to compare the address to the tags in parallel but it is expensive 1 MB is thus unlikely, typically smaller Direct Mapped Cache: Directly computes the cache entry from the address multiple addresses will map to the same cache line use TAG to determine if right Choose some bits from the address to determine cache entry low 5 bits determine which byte within the line of 32 bytes we need 15 bits to determine which of the 32k different lines has the data which of the 32 – 5 = 27 remaining bits should we use? Direct­Mapping Example Direct­Mapping Example Suppose: 2 words/line, 4 lines, bytes are being read With 8 byte lines, bottom 3 bits determine byte within line With 4 cache lines, next 2 bits determine which line to use 1024d = 10000000000b line = 00b = 0d 1000d = 01111101000b line = 01b = 1d 1040d = 10000010000b line = 10b = 2d Main memory 1000 17 1004 23 1008 11 1012 5 Tag Data 1024 44 99 1000 17 23 1040 1 4 1016 29 38 1016 29 1020 38 1024 44 1028 99 1032 97 1036 25 1040 1 Cache 1044 4 Direct Mapping Miss Direct Mapping Miss What happens when we now ask for address 1008? 1008d = 01111110000b line = 10b = 2d 000b …but earlier we put 1040d there 1040d = 10000010000b line = 10b = 2d 000b …so evict 1040d, put 1008d in that entry Tag Data Memory 1000 17 1004 23 1008 11 1012 5 1016 29 1024 44 99 1000 17 23 1024 44 1008 1040 11 1 5 4 1028 99 1016 29 38 1032 97 1020 38 1036 25 Cache 1040 1 1044 4 Miss Penalty and Rate Miss Penalty and Rate How much time do you lose on a Miss? MISS PENALTY is the time it takes to read the main memory if data was not found in the cache 50 to 100 clock cycles is common MISS RATE is the fraction of accesses which MISS HIT RATE is the fraction of accesses which HIT MISS RATE + HIT RATE = 1 Example Suppose a particular cache has a MISS PENALTY of 100 cycles and a HIT RATE of 95%. The CPI for load on HIT is 5 but on a MISS it is 105. What is the average CPI for load? Average CPI = 5 * 0.95 + 105 * 0.05 = 10 What if MISS PENALTY = 120 cycles? Average CPI = 5 * 0.95 + 120 * 0.05 = 11 Continuum of Associativity Fully associative address Compares addr with ALL tags simultaneously location A can be stored in any cache line. N-way set associative address Direct-mapped address N Compares addr with N tags simultaneously. Data can be stored in any of the N cache lines belonging to a “set” like N direct-mapped caches. Compare addr with only ONE tag. Location A can be stored in exactly one cache line. What happens on a MISS? Finds one entry out of entire cache to Finds one entry out of N in a particular row to There is only one place it can go Three Replacement Strategies Three Replacement Strategies When an entry has to be evicted, how to pick the victim? LRU (Least­recently used) replaces the item that has gone UNACCESSED the LONGEST favors the most recently accessed data FIFO/LRR (first­in, first­out/least­recently replaced) replaces the OLDEST item in cache favors recently loaded items over older STALE items Random replace some item at RANDOM no favoritism – uniform distribution no “pathological” reference streams causing worst­case results use pseudo­random generator to get reproducible behavior Handling WRITES Handling WRITES Observation: Most (80+%) of memory accesses are reads, but writes are essential. How should we handle writes? Two different policies: WRITE­THROUGH: CPU writes are cached, but also written to main memory (stalling the CPU until write is completed). Memory always holds the latest values. WRITE­BACK: CPU writes are cached, but not immediately written to main memory. Main memory contents can become “stale”. Only when a value has to be evicted from the cache, and only if it had been modified (i.e., is “dirty”), only then it is written to main memory. Pros and Cons? WRITE­BACK typically has higher performance WRITE­THROUGH typically causes fewer consistency problems Memory Hierarchy Summary Memory Hierarchy Summary Give the illusion of fast, big memory small fast cache makes the entire memory appear fast large main memory provides ample storage even larger hard drive provides huge virtual memory (TB) Various design decisions affect caching total cache size, line size, replacement strategy, write policy Performance Put your money on bigger caches, them bigger main memory. Don’t put your money on CPU clock speed (GHz) because memory is usually the culprit! ...
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This note was uploaded on 11/28/2011 for the course COMP 790 taught by Professor Staff during the Fall '08 term at UNC.

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