Parallelism, Multicore, and Synchronization

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  • 1.Parallelism, Multicore, and Synchronization Hakim Weatherspoon CS 3410 Computer Science Cornell University [Weatherspoon, Bala, Bracy, McKee, and Sirer, Roth, Martin]
  • 2.Announcements P4-Buffer Overflow is due today Due Tuesday, April 16th C practice assignment Due Friday, April 19th P5-Cache project Due Friday, April 26th Prelim2 Thursday, May 2nd, 7:30pm
  • 3.xkcd/619 3
  • 4.4 Big Picture: Multicore and Parallelism
  • 5.5 Big Picture: Multicore and Parallelism Why do I need four computing cores on my phone?!
  • 6.6 Big Picture: Multicore and Parallelism Why do I need eight computing cores on my phone?!
  • 7.7 Big Picture: Multicore and Parallelism Why do I need sixteeen computing cores on my phone?!
  • 8.8 Pitfall: Amdahl’s Law affected execution time amount of improvement + execution time unaffected Execution time after improvement = Timproved = Taffected improvement factor + Tunaffected
  • 9.9 Pitfall: Amdahl’s Law Improving an aspect of a computer and expecting a proportional improvement in overall performance Example: multiply accounts for 80s out of 100s Multiply can be parallelized How much improvement do we need in the multiply performance to get 5× overall improvement? (a) 2x (b) 10x (c) 100x (d) 1000x (e) not possible Timproved = Taffected improvement factor + Tunaffected
  • 10.10 Pitfall: Amdahl’s Law Improving an aspect of a computer and expecting a proportional improvement in overall performance Example: multiply accounts for 80s out of 100s Multiply can be parallelized How much improvement do we need in the multiply performance to get 5× overall improvement? 20 = 80 𝑛 + 20 Timproved = Taffected improvement factor + Tunaffected Can’t be done!
  • 11.Workload: sum of 10 scalars, and 10 × 10 matrix sum Speed up from 10 to 100 processors? Single processor: Time = (10 + 100) × tadd 10 processors Time = 100/10 × tadd + 10 × tadd = 20 × tadd Speedup = 110/20 = 5.5 100 processors Time = 100/100 × tadd + 10 × tadd = 11 × tadd Speedup = 110/11 = 10 Assumes load can be balanced across processors Scaling Example 11
  • 12.What if matrix size is 100 × 100? Single processor: Time = (10 + 10000) × tadd 10 processors Time = 10 × tadd + 10000/10 × tadd = 1010 × tadd Speedup = 10010/1010 = 9.9 100 processors Time = 10 × tadd + 10000/100 × tadd = 110 × tadd Speedup = 10010/110 = 91 Assuming load balanced Scaling Example 12
  • 13.Takeaway 13 Unfortunately, we cannot not obtain unlimited scaling (speedup) by adding unlimited parallel resources, eventual performance is dominated by a component needing to be executed sequentially. Amdahl's Law is a caution about this diminishing return
  • 14.Performance Improvement 101 14 seconds instructions cycles seconds program program instruction cycle 2 Classic Goals of Architects: ⬇ Clock period (⬆ Clock frequency) ⬇ Cycles per Instruction (⬆ IPC) = x x
  • 15.Darling of performance improvement for decades Why is this no longer the strategy? Hitting Limits: Pipeline depth Clock frequency Moore’s Law & Technology Scaling Power Clock frequencies have stalled 15
  • 16.Exploiting Intra-instruction parallelism: Pipelining (decode A while fetching B) Exploiting Instruction Level Parallelism (ILP): Multiple issue pipeline (2-wide, 4-wide, etc.) Statically detected by compiler (VLIW) Dynamically detected by HW Dynamically Scheduled (OoO) Improving IPC via ILP 16
  • 17.Pipelining: execute multiple instructions in parallel Q: How to get more instruction level parallelism? A: Deeper pipeline E.g. 250MHz 1-stage; 500Mhz 2-stage; 1GHz 4-stage; 4GHz 16-stage Pipeline depth limited by… max clock speed (less work per stage  shorter clock cycle) min unit of work dependencies, hazards / forwarding logic Instruction-Level Parallelism (ILP) 17
  • 18.Pipelining: execute multiple instructions in parallel Q: How to get more instruction level parallelism? A: Multiple issue pipeline Start multiple instructions per clock cycle in duplicate stages Instruction-Level Parallelism (ILP) 18 ALU/Br LW/SW
  • 19.Static multiple issue aka Very Long Instruction Word Decisions made by compiler Dynamic multiple issue Decisions made on the fly Cost: More execute hardware Reading/writing register files: more ports Multiple issue pipeline 19
  • 20.a.k.a. Very Long Instruction Word (VLIW) Compiler groups instructions to be issued together Packages them into “issue slots” How does HW detect and resolve hazards? It doesn’t.  Compiler must avoid hazards Example: Static Dual-Issue 32-bit RISC-V Instructions come in pairs (64-bit aligned) One ALU/branch instruction (or nop) One load/store instruction (or nop) Static Multiple Issue 20
  • 21.Two-issue packets One ALU/branch instruction One load/store instruction 64-bit aligned ALU/branch, then load/store Pad an unused instruction with nop RISC-V with Static Dual Issue 21
  • 22.Loop: lw t0, s1, 0 # $t0=array element add t0, t0, s2 # add scalar in $s2 sw t0, s1, 0 # store result addi s1, s1,–4 # decrement pointer bne s1, zero, Loop # branch $s1!=0 Scheduling Example 22 Loop: lw t0, s1, 0 # $t0=array element add t0, t0, s2 # add scalar in $s2 sw t0, s1, 0 # store result addi s1, s1,–4 # decrement pointer bne s1, zero, Loop # branch $s1!=0 Schedule this for dual-issue RISC-V Clicker Question: What is the IPC of this machine? (A) 0.8 (B) 1.0 (C) 1.25 (D) 1.5 (E) 2.0
  • 23.Scheduling Example 23 Loop: lw t0, s1, 0 # $t0=array element add t0, t0, s2 # add scalar in $s2 sw t0, s1, 0 # store result addi s1, s1,–4 # decrement pointer bne s1, zero, Loop # branch $s1!=0 Schedule this for dual-issue RISC-V 5 instructions 4 cycles = IPC = 1.25 4 cycles 5 instructions = CPI = 0.8
  • 24.Goal: larger instruction windows (to play with) Predication Loop unrolling Function in-lining Basic block modifications (superblocks, etc.) Roadblocks Memory dependences (aliasing) Control dependences Techniques and Limits of Static Scheduling 24
  • 25.Reorder instructions To fill the issue slot with useful work Complicated: exceptions may occur Speculation 25
  • 26. Move instructions to fill in nops Need to track hazards and dependencies Loop unrolling Optimizations to make it work 26
  • 27.Loop: lw t0, s1, 0 # t0 = A[i] lw t1, s1, 4 # t1 = A[i+1] add t0, t0, s2 # add s2 add t1, t1, s2 # add s2 sw t0, s1, 0 # store A[i] sw t1, s1, 4 # store A[i+1] addi s1, s1, +8 # increment pointer bne s1, s3, Loop # continue if s1!=end ALU/branch slot Load/store slot cycle Loop: nop lw t0, s1, 0 1 nop lw t1, s1, 4 2 add t0, t0, s2 nop 3 add t1, t1, s2 sw t0, s1, 0 4 addi s1, s1, +8 sw t1, s1, 4 5 bne s1, s3, Loop nop 6 Scheduling Example 27 Compiler scheduling for dual-issue RISC-V… 8 cycles 6 cycles 6 cycles 8 instructions = CPI = 0.75 delay slot -4 -8
  • 28.Loop: lw t0, s1, 0 # t0 = A[i] lw t1, s1, 4 # t1 = A[i+1] add t0, t0, s2 # add s2 add t1, t1, s2 # add s2 sw t0, s1, 0 # store A[i] sw t1, s1, 4 # store A[i+1] addi s1, s1, +8 # increment pointer bne s1, s3, Loop # continue if s1!=end ALU/branch slot Load/store slot cycle Loop: nop lw t0, s1, 0 1 addi s1, s1, +8 lw t1, s1, 4 2 add t0, t0, s2 nop 3 add t1, t1, s2 sw t0, s1, -8 4 bne s1, s3, Loop sw t1, s1, -4 5 Scheduling Example 28 Compiler scheduling for dual-issue RISC-V… 8 cycles 5 cycles -4 -8 5 cycles 8 instructions = CPI = 0.625
  • 29. lw t0, s1, 0 # load A addi t0, t0, +1 # increment A sw t0, s1, 0 # store A lw t0, s2, 0 # load B addi t0, t0, +1 # increment B sw t0, s2, 0 # store B ALU/branch slot Load/store slot cycle nop lw t0, s1, 0 1 nop nop 2 addi t0, t0, +1 nop 3 nop sw t0, s1, 0 4 nop lw t0, s2, 0 5 nop nop 6 addi t0, t0, +1 nop 7 nop sw t0, s2, 0 8 Limits of Static Scheduling 29 Compiler scheduling for dual-issue RISC-V…
  • 30. lw t0, s1, 0 # load A addi t0, t0, +1 # increment A sw t0, s1, 0 # store A lw t1, s2, 0 # load B addi t1, t1, +1 # increment B sw t1, s2, 0 # store B ALU/branch slot Load/store slot cycle nop lw t0, s1, 0 1 nop nop 2 addi t0, t0, +1 nop 3 nop sw t0, s1, 0 4 nop lw t1, s2, 0 5 nop nop 6 addi t1, t1, +1 nop 7 nop sw t1, s2, 0 8 Limits of Static Scheduling 30 Compiler scheduling for dual-issue RISC-V…
  • 31. lw t0, s1, 0 # load A addi t0, t0, +1 # increment A sw t0, s1, 0 # store A lw t1, s2, 0 # load B addi t1, t1, +1 # increment B sw t1, s2, 0 # store B ALU/branch slot Load/store slot cycle nop lw t0, s1, 0 1 nop lw t1, s2, 0 2 addi t0, t0, +1 nop 3 addi t1, t1, +1 sw t0, s1, 0 4 nop sw t1, s2, 0 5 Limits of Static Scheduling 31 Compiler scheduling for dual-issue RISC-V… Problem: What if $s1 and $s2 are equal (aliasing)? Won’t work
  • 32.Exploiting Intra-instruction parallelism: Pipelining (decode A while fetching B) Exploiting Instruction Level Parallelism (ILP): Multiple issue pipeline (2-wide, 4-wide, etc.) Statically detected by compiler (VLIW) Dynamically detected by HW Dynamically Scheduled (OoO) Improving IPC via ILP 32
  • 33.aka SuperScalar Processor (c.f. Intel) CPU chooses multiple instructions to issue each cycle Compiler can help, by reordering instructions…. … but CPU resolves hazards Even better: Speculation/Out-of-order Execution Execute instructions as early as possible Aggressive register renaming (indirection to the rescue!) Guess results of branches, loads, etc. Roll back if guesses were wrong Don’t commit results until all previous insns committed Dynamic Multiple Issue 33
  • 34.Dynamic Multiple Issue 34
  • 35.It was awesome, but then it stopped improving Limiting factors? Programs dependencies Memory dependence detection  be conservative e.g. Pointer Aliasing: A[0] += 1; B[0] *= 2; Hard to expose parallelism Still limited by the fetch stream of the static program Structural limits Memory delays and limited bandwidth Hard to keep pipelines full, especially with branches Effectiveness of OoO Superscalar 35
  • 36.Power Efficiency 36 Q: Does multiple issue / ILP cost much? A: Yes.  Dynamic issue and speculation requires power Those simpler cores did something very right.
  • 37.37 Moore’s Law 486 286 8088 8080 8008 4004 386 Pentium Atom P4 Itanium 2 K8 K10 Dual-core Itanium 2
  • 38.Moore’s law A law about transistors Smaller means more transistors per die And smaller means faster too But: Power consumption growing too… Why Multicore? 38
  • 39.Power Limits 39 Hot Plate Rocket Nozzle Nuclear Reactor Surface of Sun Xeon 180nm 32nm
  • 40.Power = capacitance * voltage2 * frequency In practice: Power ~ voltage3 Reducing voltage helps (a lot) ... so does reducing clock speed Better cooling helps The power wall We can’t reduce voltage further We can’t remove more heat Power Wall 40 Lower Frequency
  • 41.Why Multicore? 41 Power 1.0x 1.0x Performance Single-Core Power 1.2x 1.7x Performance Single-CoreOverclocked +20% Power 0.8x 0.51x Performance Single-Core Underclocked -20% 1.6x 1.02x Dual-Core Underclocked -20%
  • 42.Power Efficiency 42 Q: Does multiple issue / ILP cost much? A: Yes.  Dynamic issue and speculation requires power Those simpler cores did something very right.
  • 43.AMD Barcelona Quad-Core: 4 processor cores Inside the Processor 43
  • 44.Inside the Processor 44 Intel Nehalem Hex-Core 4-wide pipeline
  • 45.Exploiting Thread-Level parallelism Hardware multithreading to improve utilization: Multiplexing multiple threads on single CPU Sacrifices latency for throughput Single thread cannot fully utilize CPU? Try more! Three types: Course-grain (has preferred thread) Fine-grain (round robin between threads) Simultaneous (hyperthreading) Improving IPC via ILP TLP 45
  • 46.Process: multiple threads, code, data and OS state Threads: share code, data, files, not regs or stack What is a thread? 46
  • 47.Standard Multithreading Picture 47 Time evolution of issue slots Color = thread, white = no instruction CGMT FGMT SMT 4-wide Superscalar time Switch to thread B on thread A L2 miss Switch threads every cycle Insns from multiple threads coexist
  • 48. Multi-Core vs. Multi-Issue Programs: Num. Pipelines: Pipeline Width: Hyperthreads HT = MultiIssue + extra PCs and registers – dependency logic HT = MultiCore – redundant functional units + hazard avoidance Hyperthreads (Intel) Illusion of multiple cores on a single core Easy to keep HT pipelines full + share functional units Hyperthreading 48 vs. HT
  • 49.Example: All of the above 49 8 die (aka 8 sockets) 4 core per socket 2 HT per core Note: a socket is a processor, where each processor may have multiple processing cores, so this is an example of a multiprocessor multicore hyperthreaded system
  • 50.Q: So lets just all use multicore from now on! A: Software must be written as parallel program Multicore difficulties Partitioning work Coordination & synchronization Communications overhead How do you write parallel programs? ... without knowing exact underlying architecture? Parallel Programming 50
  • 51.Partition work so all cores have something to do Work Partitioning 51
  • 52.Load Balancing Need to partition so all cores are actually working Load Balancing 52
  • 53.If tasks have a serial part and a parallel part… Example: step 1: divide input data into n pieces step 2: do work on each piece step 3: combine all results Recall: Amdahl’s Law As number of cores increases … time to execute parallel part? time to execute serial part? Serial part eventually dominates Amdahl’s Law 53 goes to zero Remains the same
  • 54.Amdahl’s Law 54
  • 55.Necessity, not luxury Power wall Not easy to get performance out of Many solutions Pipelining Multi-issue Hyperthreading Multicore Parallelism is a necessity 55
  • 56.Q: So lets just all use multicore from now on! A: Software must be written as parallel program Multicore difficulties Partitioning work Coordination & synchronization Communications overhead How do you write parallel programs? ... without knowing exact underlying architecture? Parallel Programming 56 HW SW Your career…
  • 57.How do I take advantage of parallelism? How do I write (correct) parallel programs? What primitives do I need to implement correct parallel programs? Big Picture: Parallelism and Synchronization 57
  • 58.Cache Coherency Processors cache shared data  they see different (incoherent) values for the same memory location Synchronizing parallel programs Atomic Instructions HW support for synchronization How to write parallel programs Threads and processes Critical sections, race conditions, and mutexes Parallelism & Synchronization 58
  • 59.Cache Coherency Problem: What happens when to two or more processors cache shared data? Parallelism and Synchronization 59
  • 60.Cache Coherency Problem: What happens when to two or more processors cache shared data? i.e. the view of memory held by two different processors is through their individual caches. As a result, processors can see different (incoherent) values to the same memory location. Parallelism and Synchronization 60
  • 61.Parallelism and Synchronization 61
  • 62.Each processor core has its own L1 cache Parallelism and Synchronization 62
  • 63.Each processor core has its own L1 cache Parallelism and Synchronization 63
  • 64.Each processor core has its own L1 cache Parallelism and Synchronization 64 Core0 Cache Memory I/O Interconnect Core1 Cache Core3 Cache Core2 Cache
  • 65.Shared Memory Multiprocessor (SMP) Typical (today): 2 – 4 processor dies, 2 – 8 cores each HW provides single physical address space for all processors Shared Memory Multiprocessors 65 Core0 Cache Memory I/O Interconnect Core1 Cache Core3 Cache Core2 Cache
  • 66.Shared Memory Multiprocessor (SMP) Typical (today): 2 – 4 processor dies, 2 – 8 cores each HW provides single physical address space for all processors Shared Memory Multiprocessors 66 Core0 Cache Memory I/O Interconnect Core1 Cache ... CoreN Cache ... ...
  • 67.Thread A (on Core0) Thread B (on Core1)for(int i = 0, i < 5; i++) { for(int j = 0; j < 5; j++) { x = x + 1; x = x + 1;} } What will the value of x be after both loops finish? Cache Coherency Problem 67 Core0 Cache Memory I/O Interconnect Core1 Cache ... CoreN Cache ... ...
  • 68.Thread A (on Core0) Thread B (on Core1)for(int i = 0, i < 5; i++) { for(int j = 0; j < 5; j++) { x = x + 1; x = x + 1;} } What will the value of x be after both loops finish? (x starts as 0) 6 8 10 Could be any of the above Couldn’t be any of the above Cache Coherency Problem 68 Clicker Question
  • 69.Thread A (on Core0) Thread B (on Core1)for(int i = 0, i < 5; i++) { for(int j = 0; j < 5; j++) { x = x + 1; x = x + 1;} } What will the value of x be after both loops finish? (x starts as 0) 6 8 10 Could be any of the above Couldn’t be any of the above Cache Coherency Problem 69 Clicker Question
  • 70.Thread A (on Core0) Thread B (on Core1)for(int i = 0, i < 5; i++) { for(int j = 0; j < 5; j++) { LW t0, addr(x) LW t0, addr(x) ADDIU t0, t0, 1 ADDIU t0, t0, 1 SW t0, addr(x) SW t0, addr(x)} } Cache Coherency Problem, WB $ 70 t0=0 t0=1 x=1 t0=0 t0=1 x=1 Problem! Core0 Cache Memory I/O Interconnect Core1 Cache ... CoreN Cache ... ... X 0 X X 1 1
  • 71.Not just a problem for Write-Back Caches 71 Executing on a write-thru cache Core0 Cache Memory I/O Interconnect Core1 Cache ... CoreN Cache ... ...
  • 72.Coherence What values can be returned by a read Need a globally uniform (consistent) view of a single memory location Solution: Cache Coherence Protocols Consistency When a written value will be returned by a read Need a globally uniform (consistent) view of all memory locations relative to each other Solution: Memory Consistency Models Two issues 72
  • 73.Informal: Reads return most recently written value Formal: For concurrent processes P1 and P2 P writes X before P reads X (with no intervening writes) read returns written value (preserve program order) P1 writes X before P2 reads X  read returns written value (coherent memory view, can’t read old value forever) P1 writes X and P2 writes X all processors see writes in the same order all see the same final value for X Aka write serialization (else PA can see P2’s write before P1’s and PB can see the opposite; their final understanding of state is wrong) Coherence Defined 73
  • 74.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 Cache Coherence Protocols 74
  • 75.Snooping for Hardware Cache Coherence All caches monitor bus and all other caches Bus read: respond if you have dirty data Bus write: update/invalidate your copy of data Snooping 75 ... Core0 Cache Memory I/O Interconnect ... ... Snoop Core1 Cache Snoop CoreN Cache Snoop
  • 76.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 Invalidating Snooping Protocols 76
  • 77.Write-back policies for bandwidth Write-invalidate coherence policy First invalidate all other copies of data Then write it in cache line Anybody else can read it Permits one writer, multiple readers In reality: many coherence protocols Snooping doesn’t scale Directory-based protocols Caches and memory record sharing status of blocks in a directory Writing 77
  • 78.Hardware Cache Coherence 78 Coherence all copies have same data at all times Coherence controller: Examines bus traffic (addresses and data) Executes coherence protocol What to do with local copy when you see different things happening on bus Three processor-initiated events Ld: load St: store WB: write-back Two remote-initiated events LdMiss: read miss from another processor StMiss: write miss from another processor CPU D$ data D$ tags CC bus
  • 79.VI Coherence Protocol 79 VI (valid-invalid) protocol: Two states (per block in cache) V (valid): have block I (invalid): don’t have block Can implement with valid bit Protocol diagram (left) If you load/store a block: transition to V If anyone else wants to read/write block: Give it up: transition to I state Write-back if your own copy is dirty I V Load, Store LdMiss, StMiss, WB Load, Store LdMiss/ StMiss
  • 80.VI Protocol (Write-Back Cache) 80 lw by Thread B generates an “other load miss” event (LdMiss) Thread A responds by sending its dirty copy, transitioning to I 0 V:0 0 V:1 0 I: 1 V:1 1 V:2 CPU0 Mem CPU1 Thread A lw t0, r3, 0 ADDIU t0, t0, 1 sw t0, r3, 0 Thread B lw t0, r3, 0 ADDIU t0, t0, 1 sw t0, r3, 0
  • 81.VI Coherence Question 81 I V Load, Store LdMiss, StMiss, WB Load, Store LdMiss/ StMiss Clicker Question: Core A loads x into a register Core B wants to load x into a register What happens? they can both have a copy of X in their cache A keeps the copy B steals the copy from A, and this is an efficient thing to do B steals the copy from A, and this is a sad shame B waits until A kicks X out of its cache, then it can complete the load
  • 82.VI  MSI 82 LdMiss I M Store StMiss, WB Load, Store S Store Load, LdMiss StMiss Load LdMiss/ StMiss VI protocol is inefficient Only one cached copy allowed in entire system Multiple copies can’t exist even if read-only Not a problem in example Big problem in reality MSI (modified-shared-invalid) Fixes problem: splits “V” state into two states M (modified): local dirty copy S (shared): local clean copy Allows either Multiple read-only copies (S-state) --OR-- Single read/write copy (M-state)
  • 83.MSI Protocol (Write-Back Cache) 83 lw by Thread B generates a “other load miss” event (LdMiss) Thread A responds by sending its dirty copy, transitioning to S sw by Thread B generates a “other store miss” event (StMiss) Thread A responds by transitioning to I Thread A lw t0, r3, 0 ADDIU t0, t0, 1 sw t0, r3, 0 Thread B lw t0, r3, 0 ADDIU t0, t0, 1 sw t0, r3, 0 0 S:0 0 M:1 0 S:1 1 S:1 I: 1 M:2 CPU0 Mem CPU1
  • 84.Coherence introduces two new kinds of cache misses Upgrade miss On stores to read-only blocks Delay to acquire write permission to read-only block Coherence miss Miss to a block evicted by another processor’s requests Making the cache larger… Doesn’t reduce these type of misses As cache grows large, these sorts of misses dominate False sharing Two or more processors sharing parts of the same block But not the same bytes within that block (no actual sharing) Creates pathological “ping-pong” behavior Careful data placement may help, but is difficult Cache Coherence and Cache Misses 84
  • 85.In reality: many coherence protocols Snooping: VI, MSI, MESI, MOESI, … But Snooping doesn’t scale Directory-based protocols Caches & memory record blocks’ sharing status in directory Nothing is free  directory protocols are slower! Cache Coherency: requires that reads return most recently written value Is a hard problem! More Cache Coherence 85
  • 86.Informally, Cache Coherency requires that reads return most recently written value Cache coherence hard problem Snooping protocols are one approach Takeaway: Summary of cache coherence 86
  • 87.Is cache coherency sufficient? i.e. Is cache coherency (what values are read) sufficient to maintain consistency (when a written value will be returned to a read). Both coherency and consistency are required to maintain consistency in shared memory programs. Next Goal: Synchronization 87
  • 88.Are We Done Yet? 88 What just happened??? Is Cache Coherency Protocol Broken?? Thread A lw t0, r3, 0 ADDIU t0, t0, 1 sw t0, x, 0 Thread B lw t0, r3, 0 ADDIU t0, t0, 1 sw t0, x, 0 0 S:0 0 S:0 0 S:0 M:1 1 I: CPU0 Mem CPU1 I: 0 M:1
  • 89.Thread A (on Core0) Thread B (on Core1)for(int i = 0, i < 5; i++) { for(int j = 0; j < 5; j++) { LW t0, addr(x) LW t0, addr(x) ADDIU t0, t0, 1 ADDIU t0, t0, 1 SW t0, addr(x) SW t0, addr(x)} } Is Cache Coherency Sufficient? 89 Very expensive and difficult to maintain consistency Core0 Cache Memory I/O Interconnect Core1 Cache ... CoreN Cache ... ...
  • 90.The Previous example shows us that Caches can be incoherent even if there is a coherence protocol. Cache coherence protocols are not rich enough to support multi-threaded programs Coherent caches are not enough to guarantee expected program behavior. Multithreading is just a really bad idea. All of the above Clicker Question 90
  • 91.The Previous example shows us that Caches can be incoherent even if there is a coherence protocol. Cache coherence protocols are not rich enough to support multi-threaded programs Coherent caches are not enough to guarantee expected program behavior. Multithreading is just a really bad idea. All of the above Clicker Question 91
  • 92.Need it to exploit multiple processing units …to parallelize for multicore …to write servers that handle many clients Problem: hard even for experienced programmers Behavior can depend on subtle timing differences Bugs may be impossible to reproduce Needed: synchronization of threads Programming with Threads 92
  • 93.Within a thread: execution is sequential Between threads? No ordering or timing guarantees Might even run on different cores at the same time Problem: hard to program, hard to reason about Behavior can depend on subtle timing differences Bugs may be impossible to reproduce Cache coherency is not sufficient… Need explicit synchronization to make sense of concurrency! Programming with Threads 93
  • 94.Concurrency poses challenges for: Correctness Threads accessing shared memory should not interfere with each other Liveness Threads should not get stuck, should make forward progress Efficiency Program should make good use of available computing resources (e.g., processors). Fairness Resources apportioned fairly between threads Programming with Threads 94
  • 95. Apache web server void main() { setup(); while (c = accept_connection()) { req = read_request(c); hits[req]++; send_response(c, req); } cleanup(); } Example: Multi-Threaded Program 95
  • 96.Each client request handled by a separate thread (in parallel) Some shared state: hit counter, ... (look familiar?) Timing-dependent failure  race condition hard to reproduce  hard to debug Example: web server 96 Thread 52 read hits addiu write hits Thread 205 read hits addiu write hits
  • 97.Possible result: lost update! Timing-dependent failure  race condition Very hard to reproduce  Difficult to debug Two threads, one counter 97 ADDIU/SW: hits = 0 + 1 LW (0) ADDIU/SW: hits = 0 + 1 LW (0) T1 T2 hits = 1 hits = 0 time
  • 98.Timing-dependent error involving access to shared state Race conditions depend on how threads are scheduled i.e. who wins “races” to update state Challenges of Race Conditions Races are intermittent, may occur rarely Timing dependent = small changes can hide bug Program is correct only if all possible schedules are safe Number of possible schedules is huge Imagine adversary who switches contexts at worst possible time Race conditions 98
  • 99.What if we can designate parts of the execution as critical sections Rule: only one thread can be “inside” a critical section Critical Sections 99 Thread 52 CSEnter() read hits addi write hits CSExit() Thread 205 CSEnter() read hits addi write hits CSExit()
  • 100.To eliminate races: use critical sections that only one thread can be in Contending threads must wait to enter Critical Sections 100 CSEnter(); Critical section CSExit(); T1 T2 time CSEnter(); # wait # wait Critical section CSExit(); T1 T2
  • 101.Implement CSEnter and CSExit; ie. a critical section Only one thread can hold the lock at a time “I have the lock” Mutual Exclusion Lock (mutex) lock(m): wait till it becomes free, then lock it unlock(m): unlock it Mutual Exclusion Lock (Mutex) 101 safe_increment() { pthread_mutex_lock(&m); hits = hits + 1; pthread_mutex_unlock(&m); }
  • 102.Only one thread can hold a given mutex at a time Acquire (lock) mutex on entry to critical section Or block if another thread already holds it Release (unlock) mutex on exit Allow one waiting thread (if any) to acquire & proceed Mutexes 102 pthread_mutex_lock(&m); hits = hits+1; pthread_mutex_unlock(&m); T1 T2 pthread_mutex_lock(&m); # wait # wait hits = hits+1; pthread_mutex_unlock(&m); pthread_mutex_init(&m);
  • 103.How to implement mutex locks? What are the hardware primitives? Then, use these mutex locks to implement critical sections, and use critical sections to write parallel safe programs Next Goal 103
  • 104.Atomic read & write memory operation Between read & write: no writes to that address Many atomic hardware primitives test and set (x86) atomic increment (x86) bus lock prefix (x86) compare and exchange (x86, ARM deprecated) linked load / store conditional (pair of insns)(RISC-V, ARM, PowerPC, DEC Alpha, …) Hardware Support for Synchronization 104
  • 105.Load Reserved: LR.W rd, rs1 “I want the value at address X. Also, start monitoring any writes to this address.” Store Conditional: SC.W rd, rs1, rs2 SC.W rd, rs2, (rs1) “If no one has changed the value at address X since the LR.W, perform this store and tell me it worked.” Data at location has not changed since the LR? SUCCESS: Performs the store (rs2 to the address, rs1 is the value) Returns 0 in rd Data at location has changed since the LR? FAILURE: Does not perform the store Returns 1 in rd Synchronization in RISC-V 105
  • 106.Load Reserved: LR.W rd, rs1 Store Conditional: SC.W rd, rs1, rs2 SC.W rd, rs2, (rs1) Succeeds if location not changed since the LR Returns 0 in rd Fails if location is changed Returns 1 in rd Any time a processor intervenes and modifies the value in memory between the LR and SC instruction, the SC returns 1 in rd, causing the code to try again. i.e. use this value 1 in rd to try again. Synchronization in RISC-V 106
  • 107.Load Reserved: LR.W rd, rs1 Store Conditional: SC.W rd, rs1, rs2 SC.W rd, rs2, (rs1) Succeeds if location not changed since the LR Returns 1 in rd Fails if location is changed Returns 0 in rd Example: atomic incrementor i++ LW t0, s0, 0 ADDIU t0, t0, 1 SW t0, s0, 0 Synchronization in RISC-V 107 LR.W t0, (s0) ADDIU t0, t0, 1 SC.W t0, t0, (s0) BNEZ t0, try atomic(i++) Value in memory changed between LR and SC ?  SC returns 0 in t0  retry try: ↓ ↓
  • 108.Load Reserved: LR.W rd, (rs1) Store Conditional: SC.W rd, rs2, (rs1) Atomic Increment in Action 108
  • 109.Load Reserved: LR.W rd, (rs1) Store Conditional: SC.W rd, rs2, (rs1) Atomic Increment in Action 109 Success! Failure!
  • 110.Load Reserved / Store Conditional m = 0; // m=0 means lock is free; otherwise, if m=1, then lock locked mutex_lock(int *m) { while(test_and_set(m)){} } int test_and_set(int *m) { old = *m; *m = 1; return old; } Mutex from LR and SC 110 LR.W Atomic SC.W
  • 111.Load Reserved / Store Conditional m = 0; // m=0 means lock is free; otherwise, if m=1, then lock locked mutex_lock(int *m) { while(test_and_set(m)){} } int test_and_set(int *m) { LI t0, 1 LR.W t1, (a0) SC.W t0, t0, (a0) MOVE a0, t1 } Mutex from LR and SC 111 BNEZ t0, try try:
  • 112.Load Reserved / Store Conditional m = 0; // m=0 means lock is free; otherwise, if m=1, then lock locked mutex_lock(int *m) { while(test_and_set(m)){} } int test_and_set(int *m) { try: LI t0, 1 LR.W t1, (a0) SC.W t0, t0, (a0) BNEZ t0, try MOVE a0, t1 } Mutex from LR and SC 112
  • 113.Load Reserved / Store Conditional m = 0; // m=0 means lock is free; otherwise, if m=1, then lock locked mutex_lock(int *m) { test_and_set: LI t0, 1 LR.W t1, (a0) BNEZ t1, test_and_set SC.W t0, t0, (a0) BNEZ t0, test_and_set } mutex_unlock(int *m) { *m = 0; } Mutex from LR and SC 113
  • 114.Load Reserved / Store Conditional m = 0; // m=0 means lock is free; otherwise, if m=1, then lock locked mutex_lock(int *m) { test_and_set: LI t0, 1 LR.W t1, (a0) BNEZ t1, test_and_set SC.W t0, t0, (a0) BNEZ t0, test_and_set } mutex_unlock(int *m) { SW zero, a0, 0 } Mutex from LR and SC 114 This is called a Spin lock Aka spin waiting
  • 115.Load Reserved / Store Conditional m = 0; // m=0 means lock is free; otherwise, if m=1, then lock locked mutex_lock(int *m) { Mutex from LR and SC 115
  • 116.Load Reserved / Store Conditional m = 0; // m=0 means lock is free; otherwise, if m=1, then lock locked mutex_lock(int *m) { Mutex from LR and SC 116
  • 117.Load Reserved / Store Conditional m = 0; // m=0 means lock is free; otherwise, if m=1, then lock locked mutex_lock(int *m) { Mutex from LR and SC 117 Success grabbing mutex lock! Inside Critical section Failed to get mutex lock – try again
  • 118.Load Reserved / Store Conditional m = 0; // m=0 means lock is free; otherwise, if m=1, then lock locked mutex_lock(int *m) { test_and_set: LI t0, 1 LR.W t1, (a0) BNEZ t1, test_and_set SC.W t0, t0, (a0) BNEZ t0, test_and_set } mutex_unlock(int *m) { SW zero, a0, 0 } Mutex from LR and SC 118 This is called a Spin lock Aka spin waiting
  • 119.Load Reserved / Store Conditional m = 0; // m=0 means lock is free; otherwise, if m=1, then lock locked mutex_lock(int *m) { Mutex from LR and SC 119
  • 120.Load Reserved / Store Conditional m = 0; // m=0 means lock is free; otherwise, if m=1, then lock locked mutex_lock(int *m) { Mutex from LR and SC 120
  • 121.Thread A                                  Thread Bfor(int i = 0, i < 5; i++) {      for(int j = 0; j < 5; j++) {   x = x + 1;                         x = x + 1; } } Now we can write parallel and correct programs 121 mutex_lock(m); mutex_lock(m); mutex_unlock(m); mutex_unlock(m);
  • 122.Other atomic hardware primitives - test and set (x86) - atomic increment (x86) - bus lock prefix (x86) - compare and exchange (x86, ARM deprecated) - load reserved / store conditional (RISC-V, ARM, PowerPC, DEC Alpha, …) Alternative Atomic Instructions 122
  • 123.Synchronization techniques clever code must work despite adversarial scheduler/interrupts used by: hackers also: noobs disable interrupts used by: exception handler, scheduler, device drivers, … disable preemption dangerous for user code, but okay for some kernel code mutual exclusion locks (mutex) general purpose, except for some interrupt-related cases Synchronization 123
  • 124.Need parallel abstractions, especially for multicore Writing correct programs is hard Need to prevent data races Need critical sections to prevent data races Mutex, mutual exclusion, implements critical section Mutex often implemented using a lock abstraction Hardware provides synchronization primitives such as LR and SC (load reserved and store conditional) instructions to efficiently implement locks Summary 124
  • 125.Next Goal 125 How do we use synchronization primitives to build concurrency-safe data structure?
  • 126.Access to shared data must be synchronized Goal: enforce datastructure invariants Producer/Consumer Example (1) 126 // invariant: // data is in A[h … t-1] char A[100]; int h = 0, t = 0; // producer: add to list tail void put(char c) { A[t] = c; t = (t+1)%n; } 1 2 3 head tail
  • 127.Access to shared data must be synchronized Goal: enforce datastructure invariants Producer/Consumer Example (1) 127 // invariant: // data is in A[h … t-1] char A[100]; int h = 0, t = 0; // producer: add to list tail void put(char c) { A[t] = c; t = (t+1)%n; } // consumer: take from list head char get() { while (h == t) { }; char c = A[h]; h = (h+1)%n; return c; } 1 2 3 4 head tail
  • 128.Access to shared data must be synchronized Goal: enforce datastructure invariants Producer/Consumer Example (1) 128 // invariant: // data is in A[h … t-1] char A[100]; int h = 0, t = 0; // producer: add to list tail void put(char c) { A[t] = c; t = (t+1)%n; } // consumer: take from list head char get() { while (h == t) { }; char c = A[h]; h = (h+1)%n; return c; } What is wrong with code? Will lose update to t and/or h Invariant is not upheld Will produce if full Will consume if empty All of the above 2 3 4 head tail
  • 129.Access to shared data must be synchronized Goal: enforce datastructure invariants Producer/Consumer Example (1) 129 // invariant: // data is in A[h … t-1] char A[100]; int h = 0, t = 0; // producer: add to list tail void put(char c) { A[t] = c; t = (t+1)%n;  } // consumer: take from list head char get() {  while (h == t) { }; char c = A[h];  h = (h+1)%n; return c; } What is wrong with the code? Could miss an update to t or h Breaks invariant: only produce if not full and only consume if not empty  Need to synchronize access to shared data 2 3 4 head tail
  • 130.Access to shared data must be synchronized Goal: enforce datastructure invariants Producer/Consumer Example (1) 130 // invariant: // data is in A[h … t-1] char A[100]; int h = 0, t = 0; // producer: add to list tail void put(char c) { A[t] = c; t = (t+1)%n; } // consumer: take from list head char get() { while (h == t) { }; char c = A[h]; h = (h+1)%n; return c; } acquire-lock() release-lock() acquire-lock() release-lock() Rule of thumb: all access and updates that can affect invariant become critical sections 2 3 4 head tail
  • 131.Protecting an invariant Example (2) 131 // invariant: (protected by mutex m)// data is in A[h … t-1] pthread_mutex_t *m = pthread_mutex_create(); char A[100]; int h = 0, t = 0; // producer: add to list tail void put(char c) { pthread_mutex_lock(m); A[t] = c; t = (t+1)%n; pthread_mutex_unlock(m); } // consumer: take from list head char get() { pthread_mutex_lock(m); while(h == t) {} char c = A[h]; h = (h+1)%n; pthread_mutex_unlock(m); return c; } Does this fix work? Rule of thumb: all access and updates that can affect invariant become critical sections
  • 132.Protecting an invariant Example (2) 132 // invariant: (protected by mutex m)// data is in A[h … t-1] pthread_mutex_t *m = pthread_mutex_create(); char A[100]; int h = 0, t = 0; // producer: add to list tail void put(char c) { pthread_mutex_lock(m); A[t] = c; t = (t+1)%n; pthread_mutex_unlock(m); } // consumer: take from list head char get() { pthread_mutex_lock(m); while(h == t) {} char c = A[h]; h = (h+1)%n; pthread_mutex_unlock(m); return c; } Does this fix work? BUG: Can’t wait while holding lock Rule of thumb: all access and updates that can affect invariant become critical sections
  • 133.Insufficient locking can cause races Skimping on mutexes? Just say no! Poorly designed locking can cause deadlock know why you are using mutexes! acquire locks in a consistent order to avoid cycles use lock/unlock like braces (match them lexically) lock(&m); …; unlock(&m) watch out for return, goto, and function calls! watch out for exception/error conditions! Guidelines for successful mutexing 133 P1: lock(m1); lock(m2); P2: lock(m2); lock(m1); Circular Wait
  • 134.Writers must check for full buffer& Readers must check for empty buffer ideal: don’t busy wait… go to sleep instead Beyond mutexes Example (3) 134 char get() { acquire(L); char c = A[h]; h = (h+1)%n; release(L); return c; } while(empty) {} head last==head empty
  • 135.char get() { acquire(L); char c = A[h]; h = (h+1)%n; release(L); return c; } Writers must check for full buffer& Readers must check for empty buffer ideal: don’t busy wait… go to sleep instead Beyond mutexes Example (3) 135 char get() { acquire(L); while (h == t) { }; char c = A[h]; h = (h+1)%n; release(L); return c; } char get() { while (h == t) { }; acquire(L); char c = A[h]; h = (h+1)%n; release(L); return c; } head last==head empty Cannot check condition while Holding the lock, BUT, empty condition may no longer hold in critical section Dilemma: Have to check while holding lock,
  • 136.Writers must check for full buffer& Readers must check for empty buffer ideal: don’t busy wait… go to sleep instead Beyond mutexes Example (3) 136 char get() { acquire(L); while (h == t) { }; char c = A[h]; h = (h+1)%n; release(L); return c; } Dilemma: Have to check while holding lock, but cannot wait while hold lock head last==head empty Cannot check condition while Holding the lock, BUT, empty condition may no longer hold in critical section
  • 137.Writers must check for full buffer& Readers must check for empty buffer ideal: don’t busy wait… go to sleep instead Beyond mutexes Example (4) 137 char get() { do { acquire(L); empty = (h == t); if (!empty) { c = A[h]; h = (h+1)%n; } release(L); } while (empty); return c; } Does this work? Yes No
  • 138.Writers must check for full buffer& Readers must check for empty buffer ideal: don’t busy wait… go to sleep instead Beyond mutexes Example (4) 138 char get() { do { acquire(L); empty = (h == t); if (!empty) { c = A[h]; h = (h+1)%n; } release(L); } while (empty); return c; } It works. But, it is wasteful Due to the spinning
  • 139.Language-level Synchronization 139
  • 140.Use [Hoare] a condition variable to wait for a condition to become true (without holding lock!) wait(m, c) : atomically release m and sleep, waiting for condition c wake up holding m sometime after c was signaled signal(c) : wake up one thread waiting on c broadcast(c) : wake up all threads waiting on c POSIX (e.g., Linux): pthread_cond_wait, pthread_cond_signal, pthread_cond_broadcast Condition variables 140
  • 141.wait(m, c) : release m, sleep until c, wake up holding m signal(c) : wake up one thread waiting on c Using a condition variable Example (5) 141 char get() { lock(m); while (t == h) wait(m, not_empty); char c = A[h]; h = (h+1) % n; unlock(m); signal(not_full); return c; } cond_t *not_full = ...; cond_t *not_empty = ...; mutex_t *m = ...; void put(char c) { lock(m); while ((t-h) % n == 1) wait(m, not_full); A[t] = c; t = (t+1) % n; unlock(m); signal(not_empty); }
  • 142.A Monitor is a concurrency-safe datastructure, with… one mutex some condition variables some operations All operations on monitor acquire/release mutex one thread in the monitor at a time Ring buffer was a monitor Java, C#, etc., have built-in support for monitors Monitors 142
  • 143.Java objects can be monitors “synchronized” keyword locks/releases the mutex Has one (!) builtin condition variable o.wait() = wait(o, o) o.notify() = signal(o) o.notifyAll() = broadcast(o) Java wait() can be called even when mutex is not held. Mutex not held when awoken by signal(). Useful? Java concurrency 143
  • 144.Lots of synchronization variations…Reader/writer locks Any number of threads can hold a read lock Only one thread can hold the writer lock Semaphores N threads can hold lock at the same time Monitors Concurrency-safe data structure with 1 mutex All operations on monitor acquire/release mutex One thread in the monitor at a time Message-passing, sockets, queues, ring buffers, … transfer data and synchronize Language-level Synchronization 144
  • 145.Summary 145 Hardware Primitives: test-and-set, LR.W/SC.W, barrier, ... … used to build … Synchronization primitives: mutex, semaphore, ... … used to build … Language Constructs: monitors, signals, ...
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