Concurrent Algorithms

Summing the elements of an array

Parallel sum and parallel prefix sum It’s relatively easy to see how to sum up the elements of an array in a parallel fashion This is a special case of a reduce operation—combining a number of values into a single value It’s harder to see how to do a prefix (cumulative) sum For example, the list [3, 1, 4, 1, 6] to [3, 4, 8, 9, 15] This is a special case of what is sometimes called a scan operation An example is shown on the next slide The algorithm is done in two passes: The first pass is “up” the tree, retaining the summands The second pass is “down” the tree Note: These two examples are from Principles of Parallel Programming by Calvin Lin and Lawrence Snyder 3

Summing the elements of an array = = = = = = = (0+35) (0+10) 66 (35+31) (10+15) (41+13)

Using parallel prefix sum to filter Apply the filter operation to each element of the sequence (in parallel), yielding 1’s and 0’s Starting with -1, perform prefix sum on the resultant list The first element for each sum is to be kept Example: Selecting only even numbers a[0]=4, a[1]=2, a[2]=6, a[3]=6 5

Batcher’s Bitonic sort Batcher’s bitonic sort is a sorting algorithm with the following characteristics: It’s a variation of MergeSort It’s designed for 2 n processors It fully occupies all 2 n processors Unlike array sum, which uses fewer processors on each pass I’m not going to go through this algorithm—I just want you to be able to say you’ve heard of it 6

MapReduce MapReduce is a patented technique perfected by Google to deal with huge data sets on clusters of computers From Wikipedia: "Map" step: The master node takes the input, chops it up into smaller sub-problems, and distributes those to worker nodes. A worker node may do this again in turn, leading to a multi-level tree structure. The worker node processes that smaller problem, and passes the answer back to its master node.tree "Reduce" step: The master node then takes the answers to all the sub- problems and combines them in a way to get the output - the answer to the problem it was originally trying to solve. Hadoop is a free Apache version of MapReduce 7

Basic idea of MapReduce In MapReduce, the programmer has to write only two functions, and the framework takes care of everything else The Map function is applied (in parallel) to each item of data, producing a list of key-value pairs The framework collects all the lists, and groups the key-value pairs by key The Reduce function is applied (in parallel) to each group, returning either a single value, or nothing The framework collects all the returns 8

MapReduce picture Source: reduce.png&imgrefurl= o9xqKbO4FaSEXpfViPX2cgesJo=&h=393&w=504&sz=12&hl=en&start=0&sig2=m4ExSHfMsoQUbWTbGTHwwA&zoom=1&tbnid=xi4TPuXkb g5f- M:&tbnh=150&tbnw=193&ei=voOwTe6mM6Xt0gGJtd2SCQ&prev=/images%3Fq%3Dmapreduce%26hl%3Den%26safe%3Doff%26biw%3D981% 26bih%3D666%26gbv%3D2%26tbm%3Disch&itbs=1&iact=rc&dur=505&page=1&ndsp=12&ved=1t:429,r:11,s:0&tx=48&ty=53 9

Example: Counting words (Python) The following Python program counts how many times each word occurs in a set of data, and returns the list of words and their counts def mapper(key, value): words=key.split() for word in words: Wmr.emit(word, '1') def reducer(key, iter): sum = 0 for s in iter: sum = sum + int(s) Wmr.emit(key, str(sum)) 10

Example: Counting words (Java) * Mapper for word count */ class Mapper { public void mapper(String key, String value) { String words[] = key.split(" "); int i = 0; for (i = 0; i < words.length; i++) Wmr.emit(words[i], "1"); } } /* Reducer for word count */ class Reducer { public void reducer(String key, WmrIterator iter) { int sum = 0; while (iter.hasNext()) { sum += Integer.parseInt(iter.next()); } Wmr.emit(key, Integer.valueOf(sum).toString()); } } 11

Example: Average movie ratings #!/usr/bin/env python def mapper(key, value): avgRating = float(value) binRating = 0.0 if (0 < avgRating < 1.25): binRating = 1.0 elif (1.25 <= avgRating < 1.75): binRating = 1.5 elif (1.75 <= avgRating < 2.25): binRating = 2.0 elif (2.25 <= avgRating < 2.75): binRating = 2.5 elif (2.75 <= avgRating < 3.25): binRating = 3.0 elif (3.25 <= avgRating < 3.75): binRating = 3.5 elif (3.75 <= avgRating < 4.25): binRating = 4.0 elif (4.25 <= avgRating < 4.75): binRating = 4.5 elif (4.75 <= avgRating < 5.0): binRating = 5.0 else: binRating = 99.0 Wmr.emit(str(binRating), key) #!/usr/bin/env python def reducer(key, iter): count = 0 for s in iter: count = count + 1 Wmr.emit(key, str(count)) 12

The End 13