2007-03-16 1 The ATOMOS Transactional Programming Language Mehdi Amirijoo Linköpings universitet

2007-03-16 1

The ATOMOS Transactional Programming Language

Mehdi AmirijooLinköpings universitet

2007-03-16 Mehdi Amirijoo 2

Background Transactional memory Conditional waiting Transaction Nesting Evaluation Conclusion

2007-03-16 Mehdi Amirijoo 3

Background

Multi-threaded application

Access to shared data using locks

semaphore s;...wait(s); //decrease scount++;signal(s); //increase s...

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Background

Course-grained locking leads to serialization on high-contention data structures

Process P1 { wait(s); ... d.a++; ... signal(s);}

Process P2 { wait(s); ... d.b++; ... signal(s);}

Process P3 { wait(s); ... d.a++; ... signal(s);}

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Background

Fine-grained locking: improves concurrency increases code complexity (deadlocks) degrading performance

Process P1 { ...

wait(s1); d.a++; signal(s1); ...}

Process P2 { ... wait(s2); d.b++; signal(s2); ...}

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Transactional Memory

Focus on where atomic execution is necessary Not how it is implemented

Process P1 { ...

atomic { d.a++; } ...}

Process P2 { ... atomic { d.b++; } ...}

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Transactional Memory

Statements within atomic appear to have serialization with respect to: Other transactions Reads and writes outside of transactions

Nested transactions Optimistic speculation

(transactions) vspessimistic waiting (locks)

Roll-back due to writesoutside the transaction

Process P1 {

atomic { atomic { ... } } ...

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Conditional Waiting

Conditional critical region (CCR) Similar property as critical sections, and A process can enter the critical region iff the

condition evaluates to be true. In ATOMOS:

atomic {

if ( !condition(condition_variables) ) {

watch condition_variables;

retry;} // critical region}

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Conditional WaitingClass Buffer {

public int get (){ synchronized (this) { while (!available) wait(); available = false; notifyAll(); return contents;}}

public void put(int value) { synchronized (this) { while (available) wait(); contents = value; available = true; notifyAll();}} }

Class Buffer {

public int get() { atomic { if (!available) { watch available; retry;} available = false; return contents;}}

public void put (int value) { atomic { if (available) { watch available; retry;} contents = value; available = true;}}}

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Conditional Waiting

synchronized (lock) { count++; if (count != nThreads) lock.wait(); else lock.notifyAll();}

atomic { count++; if (count != nThreads) { watch count; retry; }}

Example: Barrier synchronization

atomic { count++; }atomic { if (count != nThreads) { watch count; retry; }}

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Nesting

In databases transactions usually reduce isolation to improve performance Communication from within uncommitted

transactions! Closed-nested transaction:

Results of children visible only to parent Open-nested Transaction:

Results of children visible globally

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Nesting

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Nesting

public static int generateID { atomic { return id++; }}

public static void createOrder (...) { atomic { Order order = new Order(); order.setID(generateID()); // finish initialization of order. This could // include more calls to generateID. orders.put(new Integer(order.getID()),order); }}

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Nesting

public static open int generateID { open { return id++; }}

public static void createOrder (...) { atomic { Order order = new Order(); order.setID(generateID()); // finish initialization of order. This could // include more calls to generateID. orders.put(new Integer(order.getID()),order); }}

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Evaluation

Goal:Compare ATOMOS with Java

Synchronized → atomic wait(), notify(), notifyAll() → watch, retry

1 to 32 CPU:s (no thread migration) No garbage collection Measure execution time

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Evaluation

Benchmark focusing on business object manipulation

Only 1% chance of

contention between

threads.

ATOMOS does not

incur additional

bottlenecks

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Evaluation

Benchmark focusing on business object manipulation

Hashtable and

HashMap use only

one mutex

ConcurrentHashMap

uses fine-grained

locking

Atomos uses single

atomic statement

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Conclusion

Transactional programming simplifies design of programs

Conditional waiting enables CCR Open nesting increases performance by

reducing time to commit Evaluation shows that the approach is

scalable with the number of processors