Hardware Support for Concurrency

Exclusive locking is a pessimistic techniqueit assumes the worst (if you don't lock your door, gremlins will come in and rearrange your stuff) and doesn't proceed until you can guarantee, by acquiring the appropriate locks, that other threads will not interfere.

For fine-grained operations, there is an alternate approach that is often more efficientthe optimistic approach, whereby you proceed with an update, hopeful that you can complete it without interference. This approach relies on collision detection to determine if there has been interference from other parties during the update, in which case the operation fails and can be retried (or not). The optimistic approach is like the old saying, "It is easier to obtain forgiveness than permission", where "easier" here means "more efficient".

Processors designed for multiprocessor operation provide special instructions for managing concurrent access to shared variables. Early processors had atomic test-and-set, fetch-and-increment, or swap instructions sufficient for implementing mutexes that could in turn be used to implement more sophisticated concurrent objects. Today, nearly every modern processor has some form of atomic read-modify-write instruction, such as compare-and-swap or load-linked/store-conditional. Operating systems and JVMs use these instructions to implement locks and concurrent data structures, but until Java 5.0 they had not been available directly to Java classes.

15.2.1. Compare and Swap

The approach taken by most processor architectures, including IA32 and Sparc, is to implement a compare-and-swap (CAS) instruction. (Other processors, such as PowerPC, implement the same functionality with a pair of instructions: loadlinked and store-conditional.) CAS has three operandsa memory location V on which to operate, the expected old value A, and the newvalue B. CAS atomically updates V to the new value B, but only if the value in V matches the expected old value A; otherwise it does nothing. In either case, it returns the value currently in V. (The variant called compare-and-set instead returns whether the operation succeeded.) CAS means "I think V should have the value A; if it does, put B there, otherwise don't change it but tell me I was wrong." CAS is an optimistic techniqueit proceeds with the update in the hope of success, and can detect failure if another thread has updated the variable since it was last examined. SimulatedCAS in Listing 15.1 illustrates the semantics (but not the implementation or performance) of CAS.

When multiple threads attempt to update the same variable simultaneously using CAS, one wins and updates the variable's value, and the rest lose. But the losers are not punished by suspension, as they could be if they failed to acquire a lock; instead, they are told that they didn't win the race this time but can try again. Because a thread that loses a CAS is not blocked, it can decide whether it wants to try again, take some other recovery action, or do nothing.[3] This flexibility eliminates many of the liveness hazards associated with locking (though in unusual cases can introduce the risk of livelocksee Section 10.3.3).

[3] Doing nothing may be a perfectly sensible response to a failed CAS; in some nonblocking algorithms, such as the linked queue algorithm in Section 15.4.2, a failed CAS means that someone else already did the work you were planning to do.

Listing 15.1. Simulated CAS Operation.

@ThreadSafe
public class SimulatedCAS {
 @GuardedBy("this") private int value;

 public synchronized int get() { return value; }

 public synchronized int compareAndSwap(int expectedValue,
 int newValue) {
 int oldValue = value;
 if (oldValue == expectedValue)
 value = newValue;
 return oldValue;
 }

 public synchronized boolean compareAndSet(int expectedValue,
 int newValue) {
 return (expectedValue
 == compareAndSwap(expectedValue, newValue));
 }
}

The typical pattern for using CAS is first to read the value A from V, derive the new value B from A, and then use CAS to atomically change V from A to B so long as no other thread has changed V to another value in the meantime. CAS addresses the problem of implementing atomic read-modify-write sequences without locking, because it can detect interference from other threads.

15.2.2. A Nonblocking Counter

CasCounter in Listing 15.2 implements a thread-safe counter using CAS. The increment operation follows the canonical formfetch the old value, transform it to the new value (adding one), and use CAS to set the new value. If the CAS fails, the operation is immediately retried. Retrying repeatedly is usually a reasonable strategy, although in cases of extreme contention it might be desirable to wait or back off before retrying to avoid livelock.

CasCounter does not block, though it may have to retry several[4] times if other threads are updating the counter at the same time. (In practice, if all you need is a counter or sequence generator, just use AtomicInteger or AtomicLong, which provide atomic increment and other arithmetic methods.)

[4] Theoretically, it could have to retry arbitrarily many times if other threads keep winning the CAS race; in practice, this sort of starvation rarely happens.

Listing 15.2. Nonblocking Counter Using CAS.

@ThreadSafe
public class CasCounter {
 private SimulatedCAS value;

 public int getValue() {
 return value.get();
 }

 public int increment() {
 int v;
 do {
 v = value.get();
 }
 while (v != value.compareAndSwap(v, v + 1));
 return v + 1;
 }
}

At first glance, the CAS-based counter looks as if it should perform worse than a lock-based counter; it has more operations and a more complicated control flow, and depends on the seemingly complicated CAS operation. But in reality, CAS-based counters significantly outperform lock-based counters if there is even a small amount of contention, and often even if there is no contention. The fast path for uncontended lock acquisition typically requires at least one CAS plus other lock-related housekeeping, so more work is going on in the best case for a lock-based counter than in the normal case for the CAS-based counter. Since the CAS succeeds most of the time (assuming low to moderate contention), the hardware will correctly predict the branch implicit in the while loop, minimizing the overhead of the more complicated control logic.

The language syntax for locking may be compact, but the work done by the JVM and OS to manage locks is not. Locking entails traversing a relatively complicated code path in the JVM and may entail OS-level locking, thread suspension, and context switches. In the best case, locking requires at least one CAS, so using locks moves the CAS out of sight but doesn't save any actual execution cost. On the other hand, executing a CAS from within the program involves no JVM code, system calls, or scheduling activity. What looks like a longer code path at the application level is in fact a much shorter code path when JVM and OS activity are taken into account. The primary disadvantage of CAS is that it forces the caller to deal with contention (by retrying, backing off, or giving up), whereas locks deal with contention automatically by blocking until the lock is available.[5]

[5] Actually, the biggest disadvantage of CAS is the difficulty of constructing the surrounding algorithms correctly.

CAS performance varies widely across processors. On a single-CPU system, a CAS typically takes on the order of a handful of clock cycles, since no synchronization across processors is necessary. As of this writing, the cost of an uncontended CAS on multiple CPU systems ranges from about ten to about 150 cycles; CAS performance is a rapidly moving target and varies not only across architectures but even across versions of the same processor. Competitive forces will likely result in continued CAS performance improvement over the next several years. A good rule of thumb is that the cost of the "fast path" for uncontended lock acquisition and release on most processors is approximately twice the cost of a CAS.

15.2.3. CAS Support in the JVM

So, how does Java code convince the processor to execute a CAS on its behalf? Prior to Java 5.0, there was no way to do this short of writing native code. In Java 5.0, low-level support was added to expose CAS operations on int, long, and object references, and the JVM compiles these into the most efficient means provided by the underlying hardware. On platforms supporting CAS, the runtime inlines them into the appropriate machine instruction(s); in the worst case, if a CAS-like instruction is not available the JVM uses a spin lock. This low-level JVMsupport is used by the atomic variable classes (AtomicXxx in java.util.concurrent. atomic) to provide an efficient CAS operation on numeric and reference types; these atomic variable classes are used, directly or indirectly, to implement most of the classes in java.util.concurrent.


Introduction

Part I: Fundamentals

Thread Safety

Sharing Objects

Composing Objects

Building Blocks

Part II: Structuring Concurrent Applications

Task Execution

Cancellation and Shutdown

Applying Thread Pools

GUI Applications

Part III: Liveness, Performance, and Testing

Avoiding Liveness Hazards

Performance and Scalability

Testing Concurrent Programs

Part IV: Advanced Topics

Explicit Locks

Building Custom Synchronizers

Atomic Variables and Nonblocking Synchronization

The Java Memory Model



Java Concurrency in Practice
Java Concurrency in Practice
ISBN: 0321349601
EAN: 2147483647
Year: 2004
Pages: 141

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