Patterns of parallel programming

Alex Tumanoff

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1.Patterns of Parallel ProgrammingPrepared by Yan Drugalya ydrugalya@gmail.com @ydrugalya
2.Agenda• Why parallel?• Terms and measures• Building Blocks• Patterns overview – Pipeline and data flow – Producer-Consumer – Map-Reduce – Other
3.Why Moore's law is not working anymore• Power consumption• Wire delays• DRAM access latency• Diminishing returns of more instruction-level parallelism
4.Power consumption Sun’s Surface 10,000 1,000 Rocket NozzlePower Density (W/cm2) 100 Nuclear Reactor 10 Pentium® processors Hot Plate 1 8080 ‘70 ‘80 ’90 ’00 ‘10
5.Wire delays
6.Diminishing returns• 80’s – 10 CPI  1 CPI• 90 – 1 CPI  0.5CPI• 00’s: multicore
7.No matter how fast processors get, softwareconsistently finds new ways to eat up the extraspeed. Herb Sutter
8.Survival To scale performance, put many processing cores on the microprocessor chip New Moore’s law edition is about doubling of cores.
9.Terms & Measures• Work = T1• Span = T∞• Work Law: Tp>=T1/P• Span Law: Tp>=T∞• Speedup: Tp/T1 – Linear: θ(P) – Perfect: P• Parallelism: T1/T∞• Tp<=(T1-T∞)/P + T∞
10.Definitions• Concurrent - Several things happenings at the same time• Multithreaded – Multiple execution contexts• Parallel – Multiple simultaneous computations• Asynchronous – Not having to wait
11.Dangers• Race Conditions• Starvations• Deadlocks• Livelock• Optimizing compilers• …
12.Data parallelismParallel.ForEach(letters, ch => Capitalize(ch));
13.Task parallelismParallel.Invoke(() => Average(), () => Minimum() …);
14.Fork-Join • Additional work may be started only when specific subsets of the original elements have completed processing • All elements should be given the chance to run even if one invocation fails (Ping) Parallel.Invoke( () => ComputeMean(), Fork () => ComputeMedian(), () => ComputeMode());Compute Compute Compute static void MyParallelInvoke(params Action[] actions) Median Mean Mode { var tasks = new Task[actions.Length]; for (int i = 0; i < actions.Length; i++) tasks[i] = Task.Factory.StartNew(actions[i]); Join Task.WaitAll(tasks); }
15.Pipeline pattern Task<int> T1 = Task.Factory.StartNew(() =>Task 1 { return result1(); }); Task<double> T2 = T1.ContinueWith((antecedent) =>Task 2 { return result2(antecedent.Result); }); Task<double> T3 = T2.ContinueWith((antecedent) =>Task 3 { return result3(antecedent.Result); });
16.Producer/Consumer Disk/Net Read 1 Read 2 Read 3BlockingCollection<T> Process Process Process
18.Other patterns• Speculative Execution• APM (IAsyncResult, Begin/end pairs)• EAP(Operation/Callback pairs)
19.References• Patterns for Parallel Programming: Understanding and Applying Parallel Patterns with the .NET Framework 4• Pluralsight: – Introduction to Async and Parallel Programming in .NET 4 – Async and Parallel Programming: Application Design• The Free Lunch Is Over: A Fundamental Turn Toward Concurrency in Software• Chapter 27 Multithreaded Algorithms from Introduction to algorithms 3rd edition