1. Map-Reduce and Parallel Computing for Large-Scale Media Processing Youjie Zhou

2. Outline ► Motivations ► Map-Reduce Framework ► Large-scale Multimedia Processing Parallelization ► Machine Learning Algorithm Transformation ► Map-Reduce Drawbacks and Variants ► Conclusions

3. Motivations ► Why we need Parallelization?  “Time is Money” ► Simultaneously ► Divide-and-conquer  Data is too huge to handle ► 1 trillion (10^12) unique URLs in 2008 ► CPU speed limitation

4. Motivations ► Why we need Parallelization?  Increasing Data ► Social Networks ► Scalability!  “Brute Force” ► No approximations ► Cheap clusters v.s. expensive computers

5. Motivations ► Why we choose Map-Reduce?  Popular ► A parallelization framework Google proposed and Google uses it everyday ► Yahoo and Amazon also involve in  Popular  Good? ► “Hides” parallelization details from users ► Provides high-level operations that suit for majority algorithms  Good start on deeper parallelization researches

6. Map-Reduce Framework ► Simple idea inspired by function language (like LISP)  map ► a type of iteration in which a function is successively applied to each element of one sequence  reduce ► a function combines all the elements of a sequence using a binary operation

7. Map-Reduce Framework ► Data representation    map generates pairs  reduce combines pairs according to same key ► “Hello, world!” Example

8. Map-Reduce Framework data split0 split1 split2 map reduce output

9. Map-Reduce Framework ► Count the appearances of each different word in a set of documents void map (Document) for each word in Document generate void reduce (word,CountList) int count = 0; for each number in CountList count += number generate

10. Map-Reduce Framework ► Different Implementations  Distributed computing ► each computer acts as a computing node ► focusing on reliability over distributed computer networks ► Google’s clusters  closed source  GFS: distributed file system ► Hadoop  open source  HDFS: hadoop distributed file system

11. Map-Reduce Framework ► Different Implementations  Multi-Core computing ► each core acts as a computing node ► focusing on high speed computing using large shared memories ► Phoenix++  a two dimensional table stored in the memory where map and reduce read and write pairs  open source created by Stanford ► GPU  10x higher memory bandwidth than a CPU  5x to 32x speedups on SVM training

12. Large-scale Multimedia Processing Parallelization ► Clustering  k-means  Spectral Clustering ► Classifiers training  SVM ► Feature extraction and indexing  Bag-of-Features  Text Inverted Indexing

13. Clustering ► k-means  Basic and fundamental  Original Algorithm 1. Pick k initial center points 2. Iterate until converge 1.Assign each point with the nearest center 2.Calculate new centers  Easy to parallel!

14. Clustering ► k-means  a shared file contains center points  map 1. for each point, find the nearest center 2. generate pair  key : center id  value : current point’s coordinate  reduce 1.collect all points belonging to the same cluster (they have the same key value) 2.calculate the average  new center  iterate

15. Clustering ► Spectral Clustering  S is huge: 10^6 points (double) need 8TB  Sparse It! ► Retain only S_ij where j is among the t nearest neighbors of i ► Locality Sensitive Hashing?  It’s an approximation ► We can calculate directly  Parallel

16. Clustering ► Spectral Clustering  Calculate distance matrix ► map  creates so that every n/p points have the same key  p is the number of node in the computer cluster ► reduce  collect points with same key so that the data is split into p parts and each part is stored in each node ► for each point in the whole data set, on each node, find t nearest neighbors

17. Clustering ► Spectral Clustering  Symmetry ► x_j in t-nearest-neighbor set of x_i ≠ x_i in t- nearest-neighbor set of x_j ► map  for each nonzero element, generates two  for each nonzero element, generates two  first: key is row ID; value is column ID and distance  second: key is column ID; value is row ID and distance ► reduce  uses key as row ID and fills columns specified by column ID in value

18. Classification ► SVM

19. Classification ► SVM  SMO  instead of solving all alpha together  coordinate ascent ► pick one alpha, fix others ► optimize alpha_i

20. Classification ► SVM  SMO  But we cannot optimize only one alpha for SVM  We need to optimize two alpha each iteration

21. Classification ► SVM  repeat until converge: ► map  given two alpha, updating the optimization information ► reduce  find the two maximally violating alpha

22. Feature Extraction and Indexing ► Bag-of-Features  features  feature clusters  histogram  feature extraction ► map  takes images in and outputs features directly  feature clustering ► clustering algorithms, like k-means

23. Feature Extraction and Indexing ► Bag-of-Features  feature quantization histogram ► map  for each feature on one image, find the nearest feature cluster  generates  generates ► reduce    for each feature cluster, updating the histogram  generates  generates

24. Feature Extraction and Indexing ► Text Inverted Indexing  Inverted index of a term ► a document list containing the term ► each item in the document list stores statistical information  frequency, position, field information  map ► for each term in one document, generates ► for each term in one document, generates  reduce ► ► ► for each document, update statistical information for that term ► generates ► generates

25. Machine Learning Algorithm Transformation ► How can we know whether an algorithm can be transformed into a Map-Reduce fashion?  if so, how to do that? ► Statistical Query and Summation Form  All we want is to estimate or inference ► cluster id, labels…  From sufficient statistics ► distances between points ► points positions  statistic computation can be divided

26. Machine Learning Algorithm Transformation ► Linear Regression Summation Form reduce map reduce map reduce map

27. Machine Learning Algorithm Transformation ► Naïve Bayesian map reduce

28. Machine Learning Algorithm Transformation ► Solution  Find statistics calculation part  Distribute calculations on data using map  Gather and refine all statistics in reduce

29. Map-Reduce Systems Drawbacks ► Batch based system  “pull” model ► reduce must wait for un-finished map ► reduce “pull” data from map  no iteration support directly ► Focusing too much on distributed system and failure tolerance  local computing cluster may not need them

30. Map-Reduce Systems Drawbacks ► Focusing too much on distributed system and failure tolerance

31. Map-Reduce Variants ► Map-Reduce online  “push” model ► map “pushes” data to reduce  reduce can also “push” results to map from the next job  build a pipeline ► Iterative Map-Reduce  higher level schedulers  schedule the whole iteration process

32. Map-Reduce Variants ► Series Map-Reduce? Multi-Core Map-Reduce Multi-Core Map-Reduce Multi-Core Map-Reduce Multi-Core Map-Reduce Map-Reduce? MPI? Condor?

33. Conclusions ► Good parallelization framework  Schedule jobs automatically  Failure tolerance  Distributed computing supported  High level abstraction ► easy to port algorithms on it ► Too “industry”  why we need a large distributed system?  why we need too much data safety?

34. References [1] Map-Reduce for Machine Learning on Multicore [2] A Map Reduce Framework for Programming Graphics Processors [3] Mapreduce Distributed Computing for Machine Learning [4] Evaluating mapreduce for multi-core and multiprocessor systems [5] Phoenix Rebirth: Scalable MapReduce on a Large-Scale Shared-Memory System [6] Phoenix++: Modular MapReduce for Shared-Memory Systems [7] Web-scale computer vision using MapReduce for multimedia data mining [8] MapReduce indexing strategies: Studying scalability and efficiency [9] Batch Text Similarity Search with MapReduce [10] Twister: A Runtime for Iterative MapReduce [11] MapReduce Online [12] Fast Training of Support Vector Machines Using Sequential Minimal Optimization [13] Social Content Matching in MapReduce [14] Large-scale multimedia semantic concept modeling using robust subspace bagging and MapReduce [15] Parallel Spectral Clustering in Distributed Systems

35. Thanks Q & A