Pack

Motivation

One of the applications for scan is pack operation. Given an input array, the operation produces an output array containing only the elements that satisfy some specified predicate.

The problem with parallelizing pack is that although it is easy to determine whether an element should be filtered out into the output, we can't know where to put the element in the output array. It seems that placing an element into the output requires knowledge of the placement of the previous elements.

Think about quick sort. In the partition step, we are given a pivot and need to separate the array by the predicate of whether an element is larger than the pivot. This is the perfect place to use the pack operation. While you will have a chance to build this partitioner at a future point in the class, You are going to build a more general pack filter in this studio

Background

This is where prefix sum becomes very useful. For example, if you have an integer array input:

2

6

1

3

7

9

4

You want to filter out all elements that are less than five. You can first create a flag array in which all the positions i where input[i] is less than 5 is flagged as "1" and all other positions are marked as "0".

1

0

1

1

0

0

1

The prefix sum of this flag array is:

1

1

2

3

3

3

4

Notice how each position that that was flagged now has a distinct number assigned to it in the prefix sum array. We can use this to help us index the output array.

Code To Implement

Parallel Pack

class:	ParallelPack.java
methods:	pack
package:	pack.studio
source folder:	student/src/main/java

method: public static <T> T[] pack(Class<T[]> arrayType, T[] arr, Predicate<T> predicate) (parallel implementation required)

Testing Your Solution

class:	PackTestSuite.java
package:	pack.studio
source folder:	testing/src/test/java