Lecture 19: Cache Basics Today’s topics: Out-of-order execution

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Presentation transcript:

Lecture 19: Cache Basics Today’s topics: Out-of-order execution Cache hierarchies Reminder: Assignment 7 due on Thursday

Multicycle Instructions Multiple parallel pipelines – each pipeline can have a different number of stages Instructions can now complete out of order – must make sure that writes to a register happen in the correct order

An Out-of-Order Processor Implementation Reorder Buffer (ROB) Branch prediction and instr fetch Instr 1 Instr 2 Instr 3 Instr 4 Instr 5 Instr 6 T1 T2 T3 T4 T5 T6 Register File R1-R32 R1  R1+R2 R2  R1+R3 BEQZ R2 R3  R1+R2 R1  R3+R2 Decode & Rename T1  R1+R2 T2  T1+R3 BEQZ T2 T4  T1+T2 T5  T4+T2 ALU ALU ALU Instr Fetch Queue Results written to ROB and tags broadcast to IQ Issue Queue (IQ)

Cache Hierarchies Data and instructions are stored on DRAM chips – DRAM is a technology that has high bit density, but relatively poor latency – an access to data in memory can take as many as 300 cycles today! Hence, some data is stored on the processor in a structure called the cache – caches employ SRAM technology, which is faster, but has lower bit density Internet browsers also cache web pages – same concept

Memory Hierarchy As you go further, capacity and latency increase Disk 80 GB 10M cycles Memory 1GB 300 cycles L2 cache 2MB 15 cycles L1 data or instruction Cache 32KB 2 cycles Registers 1KB 1 cycle

Locality Why do caches work? Temporal locality: if you used some data recently, you will likely use it again Spatial locality: if you used some data recently, you will likely access its neighbors No hierarchy: average access time for data = 300 cycles 32KB 1-cycle L1 cache that has a hit rate of 95%: average access time = 0.95 x 1 + 0.05 x (301) = 16 cycles

Accessing the Cache Byte address 101000 Offset 8-byte words 8 words: 3 index bits Direct-mapped cache: each address maps to a unique address Sets Data array

The Tag Array Byte address 101000 Tag 8-byte words Compare Direct-mapped cache: each address maps to a unique address Tag array Data array

Example Access Pattern Byte address Assume that addresses are 8 bits long How many of the following address requests are hits/misses? 4, 7, 10, 13, 16, 68, 73, 78, 83, 88, 4, 7, 10… 101000 Tag 8-byte words Compare Direct-mapped cache: each address maps to a unique address Tag array Data array

Increasing Line Size Byte address A large cache line size  smaller tag array, fewer misses because of spatial locality 10100000 32-byte cache line size or block size Tag Offset Tag array Data array

Associativity Byte address Set associativity  fewer conflicts; wasted power because multiple data and tags are read 10100000 Tag Way-1 Way-2 Tag array Data array Compare

How many offset/index/tag bits if the cache has Associativity How many offset/index/tag bits if the cache has 64 sets, each set has 64 bytes, 4 ways Byte address 10100000 Tag Way-1 Way-2 Tag array Data array Compare

Example 32 KB 4-way set-associative data cache array with 32 byte line sizes How many sets? How many index bits, offset bits, tag bits? How large is the tag array?

Cache Misses On a write miss, you may either choose to bring the block into the cache (write-allocate) or not (write-no-allocate) On a read miss, you always bring the block in (spatial and temporal locality) – but which block do you replace? no choice for a direct-mapped cache randomly pick one of the ways to replace replace the way that was least-recently used (LRU) FIFO replacement (round-robin)

Writes When you write into a block, do you also update the copy in L2? write-through: every write to L1  write to L2 write-back: mark the block as dirty, when the block gets replaced from L1, write it to L2 Writeback coalesces multiple writes to an L1 block into one L2 write Writethrough simplifies coherency protocols in a multiprocessor system as the L2 always has a current copy of data

Types of Cache Misses Compulsory misses: happens the first time a memory word is accessed – the misses for an infinite cache Capacity misses: happens because the program touched many other words before re-touching the same word – the misses for a fully-associative cache Conflict misses: happens because two words map to the same location in the cache – the misses generated while moving from a fully-associative to a direct-mapped cache

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