visit 淘宝面试题：如何充分利用多核CPU，计算很大的List中所有整数的和
I've seen these two:
http://www.iteye.com/topic/711162?page=13
http://www.iteye.com/topic/713259
But I am not sure whether I missed something or the majority missed something. Though the chancee of later is slim, I am going to take the chance.
Say we have 100 numbers and we are break them into batches of size 3,
{1, 2, 3}, {4, 5, 6} .... {97, 98, 99}, {100}
now compute the sums,
6, 15, ..., 294, 100
There are 34 numbers. Now we should break this array again into batches of size 3, right? Why does nobody do this? I am scratching my hair!
So here is my code. First, let's create a simple version
package my.test.brainteaser.sum;
public class SingleThreadCalculator
{
public long calc(long[] values, int start, int end)
{
if (values == null || values.length == 0) return 0;
long sum = 0;
for (int i=start; i<end; i++) sum += values[i];
return sum;
}
}
Here we are using long to avoid overflow (java won't raise a flag like fortran for overflow). The extra variables start and end for avoiding array copying later on. These are counterintuitive, we shall talk about them later on.
Now let's create a Runnable wrapper for this:
package my.test.brainteaser.sum;
import java.util.concurrent.CountDownLatch;
public class RunnableCalculator implements Runnable
{
private SingleThreadCalculator calc;
private long[] values;
private int start;
private int end;
private long result;
private CountDownLatch doneSignal;
public RunnableCalculator(SingleThreadCalculator calc, long[] values, int start, int end, CountDownLatch doneSignal)
{
this.calc = calc;
this.values = values; // for fast performance, no copy
this.start = start;
this.end = end;
this.doneSignal = doneSignal;
}
public void run()
{
System.out.println("Thread: " + Thread.currentThread() + " start=" + start + " end=" + end);
result = calc.calc(values, start, end);
this.doneSignal.countDown();
}
public long getResult() { return result; }
}
The countdown latcher is to wait for all jobs finishes.
Now the core code:
package my.test.brainteaser.sum;
import java.util.ArrayList;
import java.util.List;
import java.util.concurrent.CountDownLatch;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;
public class MultiThreadedCalculator
{
private int numThreads = 0; // 0 means no threading
private int partSize = 100;
private SingleThreadCalculator singleThreadCalculator = new SingleThreadCalculator();
public long calc(long[] values)
{
if (numThreads == 0)
return singleThreadCalculator.calc(values, 0, values.length);
if (values == null || values.length == 0) return 0;
// compute how many parts we can divide
int len = values.length / partSize;
if (values.length % partSize != 0) len++;
long[] sums = new long[len]; // partial results
CountDownLatch doneSignal = new CountDownLatch(len);
ExecutorService executor = Executors.newFixedThreadPool(numThreads);
List<RunnableCalculator> calcs = new ArrayList<RunnableCalculator>(len);
for (int i=0; i<len; i++)
{
int end;
if (i < len - 1) // not the last one
end = (i+1) * partSize;
else // last one
{
end = values.length;
}
RunnableCalculator rc = new RunnableCalculator(singleThreadCalculator,
values, i*partSize, end, doneSignal);
calcs.add(rc);
executor.execute(rc);
}
try
{
doneSignal.await();
for (int i=0; i<len; i++)
sums[i] = calcs.get(i).getResult();
if (sums.length <= partSize)
return singleThreadCalculator.calc(sums, 0, sums.length);
else return calc(sums);
}
catch (InterruptedException ie)
{
throw new RuntimeException("got interrupted", ie);
}
}
public void setNumThreads(int numThreads)
{
if (numThreads < 0) this.numThreads = 0;
this.numThreads = numThreads;
}
public void setPartSize(int partSize)
{
this.partSize = partSize;
}
}
Here we have two variables, one for number of threads, another is for the part size in each thread. They are different, and very important when we want to optimize the performance in the real world.
There is a try block, inside, there is a recursive call, this is where we apply the same logic to the intermediate results.
The test case is:
package my.test.brainteaser.sum;
import junit.framework.TestCase;
public class SummationTest extends TestCase
{
public void testMultithreading()
{
long[] a = new long[10000];
for (int i=0; i<a.length; i++)
a[i] = i+1;
MultiThreadedCalculator c = new MultiThreadedCalculator();
c.setNumThreads(4);
c.setPartSize(100);
long sum = c.calc(a);
System.out.println("sum=" + sum);
assertTrue(sum == 10001 * 5000);
}
public void testMore()
{
MultiThreadedCalculator c = new MultiThreadedCalculator();
int len = 100000;
c.setNumThreads(7);
c.setPartSize(321);
long sum = c.calc(generateCase(len, true));
System.out.println("sum=" + sum);
long res = 1L * len * (len + 1) / 2;
System.out.println("res=" + res);
assertTrue(sum == res);
}
public void testMore1()
{
MultiThreadedCalculator c = new MultiThreadedCalculator();
int len = 54321;
c.setNumThreads(7);
c.setPartSize(123);
long sum = c.calc(generateCase(len, false));
System.out.println("sum=" + sum);
long res = 1L * len * (len + 1) / 2;
System.out.println("res=" + res);
assertTrue(sum == res);
}
private long[] generateCase(int len, boolean inc)
{
long[] ret = new long[len];
if (inc)
{
for (int i=0; i<ret.length; i++)
ret[i] = i+1;
}
else
{
for (int i=0; i<ret.length; i++)
ret[i] = len - i;
}
return ret;
}
}
To go further beyond this puzzle, there are two practical concerns:
1. We should extract a general interface from SingleThreadCalculator and MultiThreadedCalculator to shield out the runtime environment(single threaded or multi threaded env) so that users can choose. There are quite a few runtime environments I've experienced, such as message based, parallel computing based, in addition to multi-threaded.
2. The calculator interface can be abstracted to a general computing job interface which will be passed into a computing runtime environment so that we could run different computings, such as sum, average, or others.