Slides for the workshop on Parallel Programming in Python I gave on November 10th, 2015 at PyData NYC.

1. Domino Data Lab November 10, 2015
Faster data science — without a cluster
Parallel programming in Python
Manojit Nandi
mnandi92@gmail.com
@mnandi92

2. Who am I?
Domino Data Lab November 10, 2015
• Data Scientist at STEALTHBits Technologies
• Data Science Evangelist at Domino Data Lab
• BS in Decision Science

3. Domino Data Lab November 10, 2015
Agenda and Goals
• Motivation
• Conceptual intro to parallelism, general principles and pitfalls
• Machine learning applications
• Demos
Goal: Leave you with principles, and practical concrete tools, that will
help you run your code much faster

4. Motivation
Domino Data Lab November 10, 2015
• Lots of “medium data” problems
• Can fit in memory on one machine
• Lots of naturally parallel problems
• Easy to access large machines
• Clusters are hard
• Not everything fits map-reduce
CPUs with multiple cores have become the standard in the recent
development of modern computer architectures and we can not only find
them in supercomputer facilities but also in our desktop machines at
home, and our laptops; even Apple's iPhone 5S got a 1.3 GHz Dual-core
processor in 2013.
- Sebastian Rascka

5. Parallel programing 101
Domino Data Lab November 10, 2015
• Think about independent tasks (hint: “for” loops are a good place to start!)
• Should be CPU-bound tasks
• Warning and pitfalls
• Not a substitute for good code
• Overhead
• Shared resource contention
• Thrashing
Source: Blaise Barney, Lawrence Livermore National Laboratory

6. Can parallelize at different “levels”
Domino Data Lab November 10, 2015
Will focus on algorithms, with some brief comments on
Experiments
Run against underlying libraries that parallelize
low-level operations, e.g., openBLAS, ATLAS
Write your code (or use a package) to
parallelize functions or steps within your
analysis
Run different analyses at once
Math ops
Algorithms
Experiments

7. Parallelize tasks to match your resources
Domino Data Lab November 10, 2015
Computing something (CPU)
Reading from disk/database
Writing to disk/database
Network IO (e.g., web scraping)
Saturating a resource will create a bottleneck

8. Don't oversaturate your resources
Domino Data Lab November 10, 2015
itemIDs = [1, 2, … , n]
parallel-for-each(i = itemIDs){
item = fetchData(i)
result = computeSomething(item)
saveResult(result)
}

9. Parallelize tasks to match your resources
Domino Data Lab November 10, 2015
items = fetchData([1, 2, … , n])
results = parallel-for-each(i = items){
computeSomething(item)
}
saveResult(results)

10. Avoid modifying global state
Domino Data Lab November 10, 2015
itemIDs = [0, 0, 0, 0]
parallel-for-each(i = 1:4) {
itemIDs[i] = i
}
A = [0,0,0,0]Array initialized in process 1
[0,0,0,0] [0,0,0,0][0,0,0,0][0,0,0,0]Array copied to each sub-process
[0,0,0,0] [0,0,0,3][0,0,2,0][0,1,0,0]The copy is modified
[0,0,0,0]
When all parallel tasks finish, array in original process remained unchanged

11. Demo
Domino Data Lab November 10, 2015

12. Many ML tasks are parallelized
Domino Data Lab November 10, 2015
• Cross-Validation
• Grid Search Selection
• Random Forest
• Kernel Density Estimation
• K-Means Clustering
• Probabilistic Graphical Models
• Online Learning
• Neural Networks (Backpropagation)
Harder to parallelize
Intuitive to parallelize

13. Cross validation
Domino Data Lab November 10, 2015

14. Grid search
Domino Data Lab November 10, 2015
1 10 100 1000
Linear
RBF
C
Kernel

15. Random forest
Domino Data Lab November 10, 2015

16. Parallel programing in Python
Domino Data Lab November 10, 2015
• Joblib
pythonhosted.org/joblib/parallel.html
• scikit-learn (n_jobs) scikit-learn.org
• GridSearchCV
• RandomForest
• KMeans
• cross_val_score
• IPython Notebook clusters
www.astro.washington.edu/users/vanderplas/Astr599/notebooks/
21_IPythonParallel

17. Demo
Domino Data Lab November 10, 2015

18. Parallel Programming using the GPU
Domino Data Lab November 10, 2015
• GPUs are essential to deep learning
because they can yield 10x speed-up
when training the neural networks.
• Use PyCUDA library to write Python
code that executes using the GPU.

19. Demo
Domino Data Lab November 10, 2015

20. Can compose layers of parallelism
Domino Data Lab November 10, 2015
c1 c2 cn… c1 c2 cn…c1 c2 cn…
Machines
(experiments)
Cores
RF NN GridSearched
SVC

21. Demo
Domino Data Lab November 10, 2015

22. FYI: Parallel programing in R
Domino Data Lab November 10, 2015
• General purpose
• parallel
• foreach
cran.r-project.org/web/packages/foreach
• More specialized
• randomForest
cran.r-project.org/web/packages/randomForest
• caret
topepo.github.io/caret
• plyr
cran.r-project.org/web/packages/plyr

23. Domino Data Lab November 10, 2015
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