似乎是Java Streams＆＃39;的核心。并行化是ForEachTask。理解其逻辑似乎对于获取预测针对Streams API编写的客户端代码的并发行为所必需的心智模型至关重要。然而，我发现我的期望与实际行为相矛盾。
供参考，以下是密钥compute()方法（java/util/streams/ForEachOps.java:253）：
public void compute() {
Spliterator<S> rightSplit = spliterator, leftSplit;
long sizeEstimate = rightSplit.estimateSize(), sizeThreshold;
if ((sizeThreshold = targetSize) == 0L)
targetSize = sizeThreshold = AbstractTask.suggestTargetSize(sizeEstimate);
boolean isShortCircuit = StreamOpFlag.SHORT_CIRCUIT.isKnown(helper.getStreamAndOpFlags());
boolean forkRight = false;
Sink<S> taskSink = sink;
ForEachTask<S, T> task = this;
while (!isShortCircuit || !taskSink.cancellationRequested()) {
if (sizeEstimate <= sizeThreshold ||
(leftSplit = rightSplit.trySplit()) == null) {
task.helper.copyInto(taskSink, rightSplit);
break;
}
ForEachTask<S, T> leftTask = new ForEachTask<>(task, leftSplit);
task.addToPendingCount(1);
ForEachTask<S, T> taskToFork;
if (forkRight) {
forkRight = false;
rightSplit = leftSplit;
taskToFork = task;
task = leftTask;
}
else {
forkRight = true;
taskToFork = leftTask;
}
taskToFork.fork();
sizeEstimate = rightSplit.estimateSize();
}
task.spliterator = null;
task.propagateCompletion();
}
在高级描述中，主循环不断分解分裂器，交替地分离块的处理并内联处理它，直到分裂器拒绝进一步分裂或剩余大小低于计算的阈值。 / p>
现在考虑上面的算法，在未分级流的情况下，整体没有被分成大致相等的一半;而是从流的头部重复地取出预定大小的块。在这种情况下，＆＃34;建议的目标大小＆＃34;块的异常大，这基本上意味着块永远不会被重新拆分成较小的块。
因此，该算法似乎交替分叉一个块，然后处理一个内联。如果每个块需要相同的时间来处理，这应该导致使用不超过两个核。但是，实际行为是我的机器上的所有四个核都被占用。显然，我错过了该算法的一个重要难题。
我错过了什么？
这是一段自包含的代码，可用于测试此问题的主题行为：
package test;
import static java.util.concurrent.TimeUnit.NANOSECONDS;
import static java.util.concurrent.TimeUnit.SECONDS;
import static test.FixedBatchSpliteratorWrapper.withFixedSplits;
import java.io.IOException;
import java.io.PrintWriter;
import java.nio.file.Files;
import java.nio.file.Path;
import java.nio.file.Paths;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;
import java.util.concurrent.atomic.AtomicLong;
public class Parallelization {
static final AtomicLong totalTime = new AtomicLong();
static final ExecutorService pool = Executors.newFixedThreadPool(4);
public static void main(String[] args) throws IOException {
final long start = System.nanoTime();
final Path inputPath = createInput();
System.out.println("Start processing");
try (PrintWriter w = new PrintWriter(Files.newBufferedWriter(Paths.get("output.txt")))) {
withFixedSplits(Files.newBufferedReader(inputPath).lines(), 200).map(Parallelization::processLine)
.forEach(w::println);
}
final double cpuTime = totalTime.get(), realTime = System.nanoTime() - start;
final int cores = Runtime.getRuntime().availableProcessors();
System.out.println(" Cores: " + cores);
System.out.format(" CPU time: %.2f s\n", cpuTime / SECONDS.toNanos(1));
System.out.format(" Real time: %.2f s\n", realTime / SECONDS.toNanos(1));
System.out.format("CPU utilization: %.2f%%", 100.0 * cpuTime / realTime / cores);
}
private static String processLine(String line) {
final long localStart = System.nanoTime();
double ret = 0;
for (int i = 0; i < line.length(); i++)
for (int j = 0; j < line.length(); j++)
ret += Math.pow(line.charAt(i), line.charAt(j) / 32.0);
final long took = System.nanoTime() - localStart;
totalTime.getAndAdd(took);
return NANOSECONDS.toMillis(took) + " " + ret;
}
private static Path createInput() throws IOException {
final Path inputPath = Paths.get("input.txt");
try (PrintWriter w = new PrintWriter(Files.newBufferedWriter(inputPath))) {
for (int i = 0; i < 6_000; i++) {
final String text = String.valueOf(System.nanoTime());
for (int j = 0; j < 20; j++)
w.print(text);
w.println();
}
}
return inputPath;
}
}
package test;
import static java.util.Spliterators.spliterator;
import static java.util.stream.StreamSupport.stream;
import java.util.Comparator;
import java.util.Spliterator;
import java.util.function.Consumer;
import java.util.stream.Stream;
public class FixedBatchSpliteratorWrapper<T> implements Spliterator<T> {
private final Spliterator<T> spliterator;
private final int batchSize;
private final int characteristics;
private long est;
public FixedBatchSpliteratorWrapper(Spliterator<T> toWrap, long est, int batchSize) {
final int c = toWrap.characteristics();
this.characteristics = (c & SIZED) != 0 ? c | SUBSIZED : c;
this.spliterator = toWrap;
this.batchSize = batchSize;
this.est = est;
}
public FixedBatchSpliteratorWrapper(Spliterator<T> toWrap, int batchSize) {
this(toWrap, toWrap.estimateSize(), batchSize);
}
public static <T> Stream<T> withFixedSplits(Stream<T> in, int batchSize) {
return stream(new FixedBatchSpliteratorWrapper<>(in.spliterator(), batchSize), true);
}
@Override public Spliterator<T> trySplit() {
final HoldingConsumer<T> holder = new HoldingConsumer<>();
if (!spliterator.tryAdvance(holder)) return null;
final Object[] a = new Object[batchSize];
int j = 0;
do a[j] = holder.value; while (++j < batchSize && tryAdvance(holder));
if (est != Long.MAX_VALUE) est -= j;
return spliterator(a, 0, j, characteristics());
}
@Override public boolean tryAdvance(Consumer<? super T> action) {
return spliterator.tryAdvance(action);
}
@Override public void forEachRemaining(Consumer<? super T> action) {
spliterator.forEachRemaining(action);
}
@Override public Comparator<? super T> getComparator() {
if (hasCharacteristics(SORTED)) return null;
throw new IllegalStateException();
}
@Override public long estimateSize() { return est; }
@Override public int characteristics() { return characteristics; }
static final class HoldingConsumer<T> implements Consumer<T> {
Object value;
@Override public void accept(T value) { this.value = value; }
}
}
答案 0 :(得分：1)
具有讽刺意味的是，答案几乎在问题中说明：因为“左”和“右”任务轮流分叉而不是内联处理，一半时间是正确的任务，由表示，例如分流的完整剩余部分正在分拆。这意味着分块的速度稍微减慢（每隔一段时间发生一次），但显然它会发生。