src/jsr166e/StripedAdder.java

/*
 * Written by Doug Lea with assistance from members of JCP JSR-166
 * Expert Group and released to the public domain, as explained at
 * http://creativecommons.org/publicdomain/zero/1.0/
 */

package jsr166e;
import java.util.Arrays;
import java.util.Random;
import java.util.concurrent.atomic.AtomicInteger;
import java.util.concurrent.atomic.AtomicLong;
import java.io.IOException;
import java.io.Serializable;
import java.io.ObjectInputStream;
import java.io.ObjectOutputStream;

/**
 * A set of variables that together maintain a sum.  When updates
 * (method {@link #add}) are contended across threads, this set of
 * adder variables may grow dynamically to reduce contention. Method
 * {@link #sum} returns the current combined total across these
 * adders. This value is <em>NOT</em> an atomic snapshot (concurrent
 * updates may occur while the sum is being calculated), and so cannot
 * be used alone for fine-grained synchronization control.
 *
 * <p> This class may be applicable when many threads frequently
 * update a common sum that is used for purposes such as collecting
 * statistics. In this case, performance may be significantly faster
 * than using a shared {@link AtomicLong}, at the expense of using
 * significantly more space.  On the other hand, if it is known that
 * only one thread can ever update the sum, performance may be
 * significantly slower than just updating a local variable.
 *
 * <p>A StripedAdder may optionally be constructed with a given
 * expected contention level; i.e., the number of threads that are
 * expected to concurrently update the sum. Supplying an accurate
 * value may improve performance by reducing the need for dynamic
 * adjustment.
 *
 * @author Doug Lea
 */
public class StripedAdder implements Serializable {
    private static final long serialVersionUID = 7249069246863182397L;

    /*
     * Overview: We maintain a table of Atomic long variables. The
     * table is indexed by per-thread hash codes that are initialized
     * to random values.
     *
     * The table doubles in size upon contention (as indicated by
     * failed CASes when performing add()), but is capped at the
     * nearest power of two >= #CPUS. This reflects the idea that,
     * when there are more threads than CPUs, then if each thread were
     * bound to a CPU, there would exist a perfect hash function
     * mapping threads to slots that eliminates collisions. When we
     * reach capacity, we search for this mapping by randomly varying
     * the hash codes of colliding threads.  Because search is random,
     * and failures only become known via CAS failures, convergence
     * will be slow, and because threads are typically not bound to
     * CPUS forever, may not occur at all. However, despite these
     * limitations, observed contention is typically very low in these
     * cases.
     *
     * Table entries are of class Adder; a form of AtomicLong padded
     * to reduce cache contention on most processors. Padding is
     * overkill for most Atomics because they are most often
     * irregularly scattered in memory and thus don't interfere much
     * with each other. But Atomic objects residing in arrays will
     * tend to be placed adjacent to each other, and so will most
     * often share cache lines without this precaution.  Except for
     * slot adders[0], Adders are constructed upon first use, which
     * further improves per-thread locality and helps reduce (an
     * already large) footprint.
     *
     * A single spinlock is used for resizing the table as well as
     * populating slots with new Adders. Upon lock contention, threads
     * try other slots rather than blocking. We guarantee that at
     * least one slot (0) exists, so retries will eventually find a
     * candidate Adder. During these retries, there is increased
     * contention and reduced locality, which is still better than
     * alternatives.
     */

    /**
     * Number of processors, to place a cap on table growth.
     */
    static final int NCPU = Runtime.getRuntime().availableProcessors();

    /**
     * Padded version of AtomicLong
     */
    static final class Adder extends AtomicLong {
        long p0, p1, p2, p3, p4, p5, p6, p7, p8, p9, pa, pb, pc, pd;
        Adder(long x) { super(x); }
    }

    /**
     * Holder for the thread-local hash code. The code starts off with
     * a given random value, but may be set to a different
     * pseudo-random value (using a cheaper but adequate xorshift
     * generator) upon collisions.
     */
    static final class HashCode {
        int code;
        HashCode(int h) { code = h; }
    }

    /**
     * The corresponding ThreadLocal class
     */
    static final class ThreadHashCode extends ThreadLocal<HashCode> {
        static final Random rng = new Random();
        public HashCode initialValue() {
            int h = rng.nextInt();
            return new HashCode((h == 0) ? 1 : h); // ensure nonzero
        }
    }

    /**
     * Static per-thread hash codes. Shared across all StripedAdders
     * because adjustments due to collisions in one table are likely
     * to be appropriate for others.
     */
    static final ThreadHashCode threadHashCode = new ThreadHashCode();

    /**
     * Table of adders. Minimum size 2. Size grows to be at most NCPU.
     */
    private transient volatile Adder[] adders;

    /**
     * Serves as a lock when resizing and/or creating Adders.  There
     * is no need for a blocking lock: When busy, other threads try
     * other slots.
     */
    private final AtomicInteger mutex;

    /**
     * Marsaglia XorShift random generator for rehashing on collisions
     */
    private static int xorShift(int r) {
        r ^= r << 13;
        r ^= r >>> 17;
        return r ^ (r << 5);
    }

    /**
     * Creates a new adder with zero sum.
     */
    public StripedAdder() {
        this(2);
    }

    /**
     * Creates a new adder with zero sum, and with stripes presized
     * for the given expected contention level.
     *
     * @param expectedContention the expected number of threads that
     * will concurrently update the sum.
     */
    public StripedAdder(int expectedContention) {
        int cap = (expectedContention < NCPU) ? expectedContention : NCPU;
        int size = 2;
        while (size < cap)
            size <<= 1;
        Adder[] as = new Adder[size];
        as[0] = new Adder(0); // ensure at least one available adder
        this.adders = as;
        this.mutex = new AtomicInteger();
    }

    /**
     * Adds the given value.
     *
     * @param x the value to add
     */
    public void add(long x) {
        HashCode hc = threadHashCode.get();
        for (int h = hc.code;;) {
            Adder[] as = adders;
            int n = as.length;
            Adder a = as[h & (n - 1)];
            if (a != null) {
                long v = a.get();
                if (a.compareAndSet(v, v + x))
                    break;
                if (n >= NCPU) {                 // Collision when table at max
                    h = hc.code = xorShift(h);   // change code
                    continue;
                }
            }
            final AtomicInteger mutex = this.mutex;
            if (mutex.get() != 0)
                h = xorShift(h);                 // Try elsewhere
            else if (mutex.compareAndSet(0, 1)) {
                boolean created = false;
                try {
                    Adder[] rs = adders;
                    if (a != null && rs == as)   // Resize table
                        rs = adders = Arrays.copyOf(as, as.length << 1);
                    int j = h & (rs.length - 1);
                    if (rs[j] == null) {         // Create adder
                        rs[j] = new Adder(x);
                        created = true;
                    }
                } finally {
                    mutex.set(0);
                }
                if (created) {
                    hc.code = h;                 // Use this adder next time
                    break;
                }
            }
        }
    }

    /**
     * Returns an estimate of the current sum.  The result is
     * calculated by summing multiple variables, so may not be
     * accurate if updates occur concurrently with this method.
     *
     * @return the estimated sum
     */
    public long sum() {
        long sum = 0;
        Adder[] as = adders;
        int n = as.length;
        for (int i = 0; i < n; ++i) {
            Adder a = as[i];
            if (a != null)
                sum += a.get();
        }
        return sum;
    }

    /**
     * Resets each of the variables to zero. This is effective in
     * fully resetting the sum only if there are no concurrent
     * updates.
     */
    public void reset() {
        Adder[] as = adders;
        int n = as.length;
        for (int i = 0; i < n; ++i) {
            Adder a = as[i];
            if (a != null)
                a.set(0L);
        }
    }

    /**
     * Equivalent to {@code add(1)}.
     */
    public void increment() {
        add(1L);
    }

    /**
     * Equivalent to {@code add(-1)}.
     */
    public void decrement() {
        add(-1L);
    }

    /**
     * Equivalent to {@link #sum} followed by {@link #reset}.
     *
     * @return the estimated sum
     */
    public long sumAndReset() {
        long sum = 0;
        Adder[] as = adders;
        int n = as.length;
        for (int i = 0; i < n; ++i) {
            Adder a = as[i];
            if (a != null) {
                sum += a.get();
                a.set(0L);
            }
        }
        return sum;
    }

    private void writeObject(java.io.ObjectOutputStream s)
        throws java.io.IOException {
        s.defaultWriteObject();
        s.writeLong(sum());
    }

    private void readObject(ObjectInputStream s)
        throws IOException, ClassNotFoundException {
        s.defaultReadObject();
        long c = s.readLong();
        Adder[] as = new Adder[2];
        as[0] = new Adder(c);
        this.adders = as;
        mutex.set(0);
    }

}


Revision:	1.3
Committed:	Fri Jul 22 13:25:12 2011 UTC (12 years, 10 months ago) by dl
Branch:	MAIN
Changes since 1.2:	+72 -26 lines
Log Message:	Add non-default constructor; improve documentation
#	Content
1	/*
2	* Written by Doug Lea with assistance from members of JCP JSR-166
3	* Expert Group and released to the public domain, as explained at
4	* http://creativecommons.org/publicdomain/zero/1.0/
5	*/
6
7	package jsr166e;
8	import java.util.Arrays;
9	import java.util.Random;
10	import java.util.concurrent.atomic.AtomicInteger;
11	import java.util.concurrent.atomic.AtomicLong;
12	import java.io.IOException;
13	import java.io.Serializable;
14	import java.io.ObjectInputStream;
15	import java.io.ObjectOutputStream;
16
17	/**
18	* A set of variables that together maintain a sum. When updates
19	* (method {@link #add}) are contended across threads, this set of
20	* adder variables may grow dynamically to reduce contention. Method
21	* {@link #sum} returns the current combined total across these
22	* adders. This value is <em>NOT</em> an atomic snapshot (concurrent
23	* updates may occur while the sum is being calculated), and so cannot
24	* be used alone for fine-grained synchronization control.
25	*
26	* <p> This class may be applicable when many threads frequently
27	* update a common sum that is used for purposes such as collecting
28	* statistics. In this case, performance may be significantly faster
29	* than using a shared {@link AtomicLong}, at the expense of using
30	* significantly more space. On the other hand, if it is known that
31	* only one thread can ever update the sum, performance may be
32	* significantly slower than just updating a local variable.
33	*
34	* <p>A StripedAdder may optionally be constructed with a given
35	* expected contention level; i.e., the number of threads that are
36	* expected to concurrently update the sum. Supplying an accurate
37	* value may improve performance by reducing the need for dynamic
38	* adjustment.
39	*
40	* @author Doug Lea
41	*/
42	public class StripedAdder implements Serializable {
43	private static final long serialVersionUID = 7249069246863182397L;
44
45	/*
46	* Overview: We maintain a table of Atomic long variables. The
47	* table is indexed by per-thread hash codes that are initialized
48	* to random values.
49	*
50	* The table doubles in size upon contention (as indicated by
51	* failed CASes when performing add()), but is capped at the
52	* nearest power of two >= #CPUS. This reflects the idea that,
53	* when there are more threads than CPUs, then if each thread were
54	* bound to a CPU, there would exist a perfect hash function
55	* mapping threads to slots that eliminates collisions. When we
56	* reach capacity, we search for this mapping by randomly varying
57	* the hash codes of colliding threads. Because search is random,
58	* and failures only become known via CAS failures, convergence
59	* will be slow, and because threads are typically not bound to
60	* CPUS forever, may not occur at all. However, despite these
61	* limitations, observed contention is typically very low in these
62	* cases.
63	*
64	* Table entries are of class Adder; a form of AtomicLong padded
65	* to reduce cache contention on most processors. Padding is
66	* overkill for most Atomics because they are most often
67	* irregularly scattered in memory and thus don't interfere much
68	* with each other. But Atomic objects residing in arrays will
69	* tend to be placed adjacent to each other, and so will most
70	* often share cache lines without this precaution. Except for
71	* slot adders[0], Adders are constructed upon first use, which
72	* further improves per-thread locality and helps reduce (an
73	* already large) footprint.
74	*
75	* A single spinlock is used for resizing the table as well as
76	* populating slots with new Adders. Upon lock contention, threads
77	* try other slots rather than blocking. We guarantee that at
78	* least one slot (0) exists, so retries will eventually find a
79	* candidate Adder. During these retries, there is increased
80	* contention and reduced locality, which is still better than
81	* alternatives.
82	*/
83
84	/**
85	* Number of processors, to place a cap on table growth.
86	*/
87	static final int NCPU = Runtime.getRuntime().availableProcessors();
88
89	/**
90	* Padded version of AtomicLong
91	*/
92	static final class Adder extends AtomicLong {
93	long p0, p1, p2, p3, p4, p5, p6, p7, p8, p9, pa, pb, pc, pd;
94	Adder(long x) { super(x); }
95	}
96
97	/**
98	* Holder for the thread-local hash code. The code starts off with
99	* a given random value, but may be set to a different
100	* pseudo-random value (using a cheaper but adequate xorshift
101	* generator) upon collisions.
102	*/
103	static final class HashCode {
104	int code;
105	HashCode(int h) { code = h; }
106	}
107
108	/**
109	* The corresponding ThreadLocal class
110	*/
111	static final class ThreadHashCode extends ThreadLocal<HashCode> {
112	static final Random rng = new Random();
113	public HashCode initialValue() {
114	int h = rng.nextInt();
115	return new HashCode((h == 0) ? 1 : h); // ensure nonzero
116	}
117	}
118
119	/**
120	* Static per-thread hash codes. Shared across all StripedAdders
121	* because adjustments due to collisions in one table are likely
122	* to be appropriate for others.
123	*/
124	static final ThreadHashCode threadHashCode = new ThreadHashCode();
125
126	/**
127	* Table of adders. Minimum size 2. Size grows to be at most NCPU.
128	*/
129	private transient volatile Adder[] adders;
130
131	/**
132	* Serves as a lock when resizing and/or creating Adders. There
133	* is no need for a blocking lock: When busy, other threads try
134	* other slots.
135	*/
136	private final AtomicInteger mutex;
137
138	/**
139	* Marsaglia XorShift random generator for rehashing on collisions
140	*/
141	private static int xorShift(int r) {
142	r ^= r << 13;
143	r ^= r >>> 17;
144	return r ^ (r << 5);
145	}
146
147	/**
148	* Creates a new adder with zero sum.
149	*/
150	public StripedAdder() {
151	this(2);
152	}
153
154	/**
155	* Creates a new adder with zero sum, and with stripes presized
156	* for the given expected contention level.
157	*
158	* @param expectedContention the expected number of threads that
159	* will concurrently update the sum.
160	*/
161	public StripedAdder(int expectedContention) {
162	int cap = (expectedContention < NCPU) ? expectedContention : NCPU;
163	int size = 2;
164	while (size < cap)
165	size <<= 1;
166	Adder[] as = new Adder[size];
167	as[0] = new Adder(0); // ensure at least one available adder
168	this.adders = as;
169	this.mutex = new AtomicInteger();
170	}
171
172	/**
173	* Adds the given value.
174	*
175	* @param x the value to add
176	*/
177	public void add(long x) {
178	HashCode hc = threadHashCode.get();
179	for (int h = hc.code;;) {
180	Adder[] as = adders;
181	int n = as.length;
182	Adder a = as[h & (n - 1)];
183	if (a != null) {
184	long v = a.get();
185	if (a.compareAndSet(v, v + x))
186	break;
187	if (n >= NCPU) { // Collision when table at max
188	h = hc.code = xorShift(h); // change code
189	continue;
190	}
191	}
192	final AtomicInteger mutex = this.mutex;
193	if (mutex.get() != 0)
194	h = xorShift(h); // Try elsewhere
195	else if (mutex.compareAndSet(0, 1)) {
196	boolean created = false;
197	try {
198	Adder[] rs = adders;
199	if (a != null && rs == as) // Resize table
200	rs = adders = Arrays.copyOf(as, as.length << 1);
201	int j = h & (rs.length - 1);
202	if (rs[j] == null) { // Create adder
203	rs[j] = new Adder(x);
204	created = true;
205	}
206	} finally {
207	mutex.set(0);
208	}
209	if (created) {
210	hc.code = h; // Use this adder next time
211	break;
212	}
213	}
214	}
215	}
216
217	/**
218	* Returns an estimate of the current sum. The result is
219	* calculated by summing multiple variables, so may not be
220	* accurate if updates occur concurrently with this method.
221	*
222	* @return the estimated sum
223	*/
224	public long sum() {
225	long sum = 0;
226	Adder[] as = adders;
227	int n = as.length;
228	for (int i = 0; i < n; ++i) {
229	Adder a = as[i];
230	if (a != null)
231	sum += a.get();
232	}
233	return sum;
234	}
235
236	/**
237	* Resets each of the variables to zero. This is effective in
238	* fully resetting the sum only if there are no concurrent
239	* updates.
240	*/
241	public void reset() {
242	Adder[] as = adders;
243	int n = as.length;
244	for (int i = 0; i < n; ++i) {
245	Adder a = as[i];
246	if (a != null)
247	a.set(0L);
248	}
249	}
250
251	/**
252	* Equivalent to {@code add(1)}.
253	*/
254	public void increment() {
255	add(1L);
256	}
257
258	/**
259	* Equivalent to {@code add(-1)}.
260	*/
261	public void decrement() {
262	add(-1L);
263	}
264
265	/**
266	* Equivalent to {@link #sum} followed by {@link #reset}.
267	*
268	* @return the estimated sum
269	*/
270	public long sumAndReset() {
271	long sum = 0;
272	Adder[] as = adders;
273	int n = as.length;
274	for (int i = 0; i < n; ++i) {
275	Adder a = as[i];
276	if (a != null) {
277	sum += a.get();
278	a.set(0L);
279	}
280	}
281	return sum;
282	}
283
284	private void writeObject(java.io.ObjectOutputStream s)
285	throws java.io.IOException {
286	s.defaultWriteObject();
287	s.writeLong(sum());
288	}
289
290	private void readObject(ObjectInputStream s)
291	throws IOException, ClassNotFoundException {
292	s.defaultReadObject();
293	long c = s.readLong();
294	Adder[] as = new Adder[2];
295	as[0] = new Adder(c);
296	this.adders = as;
297	mutex.set(0);
298	}
299
300	}
301
302