Using Java 8 feature takes more time - multithreading

Question

Sample Program provided to count number of elements from an array that are less than the specified value.
Processing time taken by the program varies, using Java 8 forEach and stream takes more time for execution. Please explain if I should go with Java 8 features and if not which areas should it be avoided, additionally will parallelStream or multithreading with multi core processor help?
Code:
public static void main(String[] args) {
int lessThan = 4;
// Using for loop iteration
List<Integer> integerForSort1 = Arrays.asList(4, 1, 1, 2, 3);
long startTime1 = System.nanoTime();
long count1 = countNumbers(integerForSort1, lessThan);
long stopTime1 = System.nanoTime();
System.out.println(stopTime1 - startTime1);
System.out.println(count1);
integerForSort1 = null;
System.gc();
// Using binary search
List<Integer> integerForSort2 = Arrays.asList(4, 1, 1, 2, 3);
long startTime2 = System.nanoTime();
long count2 = countByBinarySearch(integerForSort2, lessThan);
long stopTime2 = System.nanoTime();
System.out.println(stopTime2 - startTime2);
System.out.println(count2);
integerForSort2 = null;
System.gc();
// Using Java 8
List<Integer> integerForSort3 = Arrays.asList(4, 1, 1, 2, 3);
long startTime3 = System.nanoTime();
long count3 = integerForSort3.stream()
.filter(p -> p < lessThan)
.count();
long stopTime3 = System.nanoTime();
System.out.println(stopTime3 - startTime3);
System.out.println(count3);
integerForSort3 = null;
System.gc();
//Using Java 8 for each loop
List<Integer> integerForSort4 = Arrays.asList(4, 1, 1, 2, 3);
long startTime4 = System.nanoTime();
long count4 = process(integerForSort4, p -> p < lessThan);
long stopTime4 = System.nanoTime();
System.out.println(stopTime4 - startTime4);
System.out.println(count4);
integerForSort4 = null;
}
public static long countNumbers(List<Integer> integerForSort, int lessThan) {
long count = 0;
Collections.sort(integerForSort);
for (Integer anIntegerForSort : integerForSort) {
if (anIntegerForSort < lessThan)
count++;
}
return count;
}
public static long countByBinarySearch(List<Integer> integerForSort, int lessThan){
if(integerForSort==null||integerForSort.isEmpty())
return 0;
int low = 0, mid = 0, high = integerForSort.size();
Collections.sort(integerForSort);
while(low != high){
mid = (low + high) / 2;
if (integerForSort.get(mid) < lessThan) {
low = mid + 1;
}
else {
high = mid;
}
}
return low;
}
public static long process(List<Integer> integerForSort, Predicate<Integer> predicate) {
final AtomicInteger i = new AtomicInteger(0);
integerForSort.forEach((Integer p) -> {
if (predicate.test(p)) {
i.getAndAdd(1);
}
});
return i.intValue();
}
Output:
345918
4
21509
4
29651234
4
2242999
4
Questions:
Is it possible to reduce the process time using Java 8 features?
Why does Java 8 stream takes more time?
How can I use lambda expression with binary Search, will it process faster?
Even using multithreading with concurrent.ExecutorService gave consistent results:
Result 4 : Thread 'pool-1-thread-1' ran process - Using Java 8 stream in 6 millisecond, from 11:16:05:361 to 11:16:05:367
Result 4 : Thread 'pool-1-thread-2' ran process - Using Java 8 forEach in 3 millisecond, from 11:16:05:361 to 11:16:05:364
Result 4 : Thread 'pool-1-thread-4' ran process - Using Java 7 binary Search in 0 millisecond, from 11:16:05:379 to 11:16:05:379
Result 4 : Thread 'pool-1-thread-3' ran process - Using Java 7 for loop in 1 millisecond, from 11:16:05:362 to 11:16:05:363

Answer 1

I do not know the answer, since I didn't perform any tests, but I think, the performance tests with 5 elements have no diagnostic value and are senseless. You should generate arrays of 10s,100s of thousands, or hundreds of milions, to see the performance difference.
Java 8 creates several objects, which causes some overhead in opposite of simple for loop. So having only 5 test elements, your results depend on how much work is needed for initialization of you execution.
You know, multiplying of 5 numbers on CPU is faster then even copying it to GPU memory, so you have your result on CPU even before GPU starts to compute. But if your data grows up and your GPU multiplies parallely hundreds of numbers, you will see the speed difference.