Design Patterns & Concurrency Sebastian Graf, Oliver Haase 1 - - PowerPoint PPT Presentation

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Design Patterns & Concurrency Sebastian Graf, Oliver Haase 1 - - PowerPoint PPT Presentation

Mar 14, 2023 762 likes •1.02k views

Design Patterns & Concurrency Sebastian Graf, Oliver Haase 1 Expectations ? ...on the concurrency-part... 2 Outline of the concurrency part I. Fundamentals II. Concurrent Applications III. Liveness, Performance and Hazards IV.

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Sebastian Graf, Oliver Haase

1

Design Patterns & Concurrency

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Expectations ?

...on the concurrency-part...

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Outline

  • f the concurrency part
  • I. Fundamentals
  • II. Concurrent Applications
  • III. Liveness, Performance and Hazards
  • IV. Advanced T
  • pics

All mapped on object-oriented programming with Java.

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Why?

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Why?

  • More responsive programs due to less blocking
  • Exploiting multi-processor architectures
  • T

ask-oriented working (e.g. like in Servlets, RMI)

  • Simply handling of asynchronous events
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Threads are everywhere

  • Garbage Collection
  • RMI Invocation (marshalling / unmarshalling)
  • Servlets

Importance of thread-safety is crucial!

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Threadsafe ?

public class Sequence { private int value; /** Returns a unique value. */ public int getNext() { return value++; } }

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Thread-Unsafe !

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Threadsafe Impl.

@ThreadSafe public class Sequence { @GuardedBy("this") private int nextValue; public synchronized int getNext() { return nextValue++; } }

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Definition of Threadsafety

Managing access to state, in particular to shared, mutable state (directly to member- variables of one class) with

  • Atomic change of state
  • Invariants, Pre- /Postconditions
  • …

Providing any necessary synchronization so that the client needs no own one.

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Simple Example

public class StatelessFactorizer implements Servlet { public void service(ServletRequest req, ServletResponse resp) { BigInteger i = extractFromRequest(req); BigInteger[] factors = factor(i); encodeIntoResponse(resp, factors); } }

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State variable example

public class CountingFactorizer implements Servlet { private long count = 0; public long getCount() { return count; } public void service(ServletRequest req, ServletResponse resp) { BigInteger i = extractFromRequest(req); BigInteger[] factors = factor(i); ++count; encodeIntoResponse(resp, factors); } }

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Thread-safe state variable example

public class CountingFactorizer implements Servlet { private final AtomicLong count = new AtomicLong(0); public long getCount() { return count.get(); } public void service(ServletRequest req, ServletResponse resp) { BigInteger i = extractFromRequest(req); BigInteger[] factors = factor(i); count.incrementAndGet(); encodeIntoResponse(resp, factors); } }

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Next one...

public class UnsafeCachingFactorizer implements Servlet { private final AtomicReference<BigInteger> lastNumber = new AtomicReference<BigInteger>(); private final AtomicReference<BigInteger[]> lastFactors = new AtomicReference<BigInteger[]>(); public void service(ServletRequest req, ServletResponse resp) { BigInteger i = extractFromRequest(req); if (i.equals(lastNumber.get())) encodeIntoResponse(resp, lastFactors.get() ); else { BigInteger[] factors = factor(i); lastNumber.set(i); lastFactors.set(factors); encodeIntoResponse(resp, factors); } } }

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One Solution...

public class SynchronizedFactorizer implements Servlet { @GuardedBy("this") private BigInteger lastNumber; @GuardedBy("this") private BigInteger[] lastFactors; public synchronized void service(ServletRequest req, ServletResponse resp) { BigInteger i = extractFromRequest(req); if (i.equals(lastNumber)) encodeIntoResponse(resp, lastFactors); else { BigInteger[] factors = factor(i); lastNumber = i; lastFactors = factors; encodeIntoResponse(resp, factors); } } }

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Poor concurrency

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Excursion, Amdahl's Law

“For the past thirty years, computer performance has been driven by Moore’s Law; from now on, it will be driven by Amdahl’s Law. [...]”

  • - Doron Rajwan, Research Scientist, Intel Corp
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Amdahl's law in detail

  • P: Parts to be parallized
  • 1-P: Part not to be parallized
  • N: Number of Threads
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Scaling of Amdahl's Law

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One better solution

public class CachedFactorizer implements Servlet { @GuardedBy("this") private BigInteger lastNumber; @GuardedBy("this") private BigInteger[] lastFactors; @GuardedBy("this") private long hits; @GuardedBy("this") private long cacheHits; public synchronized long getHits() { return hits; } public synchronized double getCacheHitRatio() { return (double) cacheHits / (double) hits; } public void service(ServletRequest req, ServletResponse resp) { BigInteger i = extractFromRequest(req); BigInteger[] factors = null; synchronized (this) { ++hits; if (i.equals(lastNumber)) { ++cacheHits; factors = lastFactors.clone(); } } if (factors == null) { factors = factor(i); synchronized (this) { lastNumber = i; lastFactors = factors.clone(); } } encodeIntoResponse(resp, factors); } }

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Fix broken code

  • Do not share state variables across threads
  • Make sate variables immutable
  • Synchronizing access to shared state variables
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But...

  • Do not serialize heavy computations
  • Remember Amdahl's law
  • Think about what needs to be parallized from

the point of view of program correctness

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Finally