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1.Building parallel machine learning algorithms: scaling out and up William Benton  willb@redhat.com • @willb

2.Motivation

3.Motivation

4.Motivation

5.Motivation

6.Forecast Introducing our case study: self-organizing maps Parallel implementations for partitioned collections (in particular, RDDs) Beyond the RDD: data frames and ML pipelines Practical considerations and key takeaways

7.Introducing our case study

8.

9.

10.Training self-organizing maps

11.Training self-organizing maps

12.Training self-organizing maps

13.Training self-organizing maps

14.Training self-organizing maps while t < maxupdates: random.shuffle(examples) for ex in examples: t = t + 1 if t == maxupdates: break bestMatch = closest(somt, ex) for (unit, wt) in neighborhood(bestMatch, sigma(t)): somt+1[unit] = somt[unit] + (ex - somt[unit]) * alpha(t) * wt

15.Training self-organizing maps process the training while t < maxupdates: set in random order random.shuffle(examples) for ex in examples: t = t + 1 if t == maxupdates: break bestMatch = closest(somt, ex) for (unit, wt) in neighborhood(bestMatch, sigma(t)): somt+1[unit] = somt[unit] + (ex - somt[unit]) * alpha(t) * wt

16.Training self-organizing maps process the training while t < maxupdates: set in random order random.shuffle(examples) for ex in examples: the neighborhood size controls t = t + 1 how much of the map around if t == maxupdates: the BMU is affected break bestMatch = closest(somt, ex) for (unit, wt) in neighborhood(bestMatch, sigma(t)): somt+1[unit] = somt[unit] + (ex - somt[unit]) * alpha(t) * wt

17.Training self-organizing maps process the training while t < maxupdates: set in random order the learning rate controls random.shuffle(examples) how much closer to the for ex in examples: example each unit gets the neighborhood size controls t = t + 1 how much of the map around if t == maxupdates: the BMU is affected break bestMatch = closest(somt, ex) for (unit, wt) in neighborhood(bestMatch, sigma(t)): somt+1[unit] = somt[unit] + (ex - somt[unit]) * alpha(t) * wt

18.Parallel implementations for partitioned collections

19.Historical aside: Amdahl’s Law 1 lim So = sp —> ∞ 1 - p

20.What forces serial execution?

21.What forces serial execution?

22.What forces serial execution? state[t+1] = combine(state[t], x)

23.What forces serial execution? state[t+1] = combine(state[t], x)

24.What forces serial execution? f1: (T, T) => T f2: (T, U) => T

25.What forces serial execution? f1: (T, T) => T f2: (T, U) => T

26.What forces serial execution? f1: (T, T) => T f2: (T, U) => T

27.How can we fix these? a⊕b=b⊕a (a ⊕ b) ⊕ c = a ⊕ (b ⊕ c)

28.How can we fix these? a⊕b=b⊕a (a ⊕ b) ⊕ c = a ⊕ (b ⊕ c)

29.How can we fix these? a⊕b=b⊕a (a ⊕ b) ⊕ c = a ⊕ (b ⊕ c)

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