This talk was given at H2O World 2018 NYC and can be viewed here: https://youtu.be/rKoBJcnsFpM Speaker's Bio: Mateusz is a software developer who loves all things distributed, machine learning and hates buzzwords. His favourite hobby data juggling. He obtained his M.Sc. in Computer Science from AGH UST in Krakow, Poland, during which he did an exchange at L’ECE Paris in France and worked on distributed flight booking systems. After graduation he move to Tokyo to work as a researcher at Fujitsu Laboratories on machine learning and NLP projects, where he is still currently based. In his spare time he tries to be part of the IT community by organizing, attending and speaking at conferences and meet ups.

1. Mateusz Dymczyk
Senior Software Engineer
H2O.ai
@mdymczyk
Introduction to
H2O4GPU

2. Practical Machine Learning
Machine
Learning

3. Moore’s Law
1980 1990 2000 2010 2020
102
103
104
105
106
107
40 Years of Microprocessor Trend Data
Original data up to the year 2010 collected and plotted by M. Horowitz, F. Labonte, O.
Shacham, K. Olukotun, L. Hammond, and C. Batten New plot and data collected for
2010-2015 by K. Rupp
Single-threaded perf
1.5X per year
1.1X per year
Transistors
(thousands)

4. GPU
1980 1990 2000 2010 2020
GPU-Computing perf
1.5X per year
1000X
by
2025
Original data up to the year 2010 collected and plotted by M. Horowitz, F. Labonte, O.
Shacham, K. Olukotun, L. Hammond, and C. Batten New plot and data collected for
2010-2015 by K. Rupp
102
103
104
105
106
107
Single-threaded perf
1.5X per year
1.1X per year
APPLICATIONS
SYSTEMS
ALGORITHMS
CUDA
ARCHITECTURE

5. GPU architecture
Low latency vs High throughput
GPU
• Optimized for data-parallel,
throughput computation
• Architecture tolerant of
memory latency
• More transistors dedicated to
computation
CPU
• Optimized for low-latency
access to cached data sets
• Control logic for out-of-order
and speculative execution

6. GPU Enhanced Applications
Application Code
GPU
Use GPU to
Parallelize
Compute-Intensive
Functions CPU
Rest of Sequential
CPU Code

7. Machine Learning and GPUs

8. Matrix Multiplication

9. Matrix Multiplication

10. Matrix Multiplication

11. Matrix Multiplication

12. Matrix Multiplication

13. H2O4GPU
• Open-Source: https://github.com/h2oai/h2o4gpu
• Collection of important ML algorithms ported to the GPU (with CPU fallback option):
• Gradient Boosted Machines
• GLM
• Truncated SVD
• PCA
• KMeans
• (soon) Field Aware Factorization Machines
• Performance optimized, multi-GPU support (certain algorithms)
• Used within our own Driverless AI Product to boost performance 30X
• Scikit-Learn compatible Python API (and now R API)

14. Gradient Boosting Machines
• Based upon XGBoost
• Raw floating point data -> Binned into Quantiles
• Quantiles are stored as compressed instead of floats
• Compressed Quantiles are efficiently transferred to GPU
• Sparsity is handled directly with highly GPU efficiency
• Multi-GPU by sharding rows using NVIDIA NCCL AllReduce

16. KMeans
• Significantly faster than Scikit-learn implementation (up to 50x)
• Significantly faster than other GPU implementations (5x-10x)
• Supports kmeans|| initialization
• Supports multiple GPUs by sharding the dataset
• Supports batching data if exceeds GPU memory

17. 12 with kmeans||

18. Truncated SVD & PCA
• Matrix decomposition
• Popular for text processing
and dimensionality reduction
• GPU optimizes linear algebra
operations

19. Truncated SVD & PCA
• The intrinsic dimensionality of certain datasets is much lower than the
original (e.g. here 4096 vs. actual ~200)
• PCA can reduce the dimensionality and preserve most of the explained
variance at the same time
• Better input for further modeling - takes less time

21. Field Aware Factorization Machines
* under development
• Click Through Rate (CTR):
• One of the most important tasks in computational advertising
• Percentage of users, who actually click on ads
• Until recently solved with logistic regression - bad at finding feature conjunctions
(learns the effect of all variables or features individually)
Clicked Publisher (P) Advertiser (A) Gender (G)
Yes ESPN Nike Male
No NBC Adidas Male

22. Field Aware Factorization Machines
* under development
• Separates the data into fields (Publisher, Advertiser, Gender) and features (EPSN, NBC,
Adidas, Nike, Male, Female)
• Uses a latent space for each pair to generate the model
• Used to win the first prize of three CTR competitions hosted by Criteo, Avazu, Outbrain,
and also the third prize of RecSys Challenge 2015.

23. More info
• Code: http://github.com/h2oai/h2o4gpu
• Questions:
• https://stackoverflow.com/questions/tagged/h2o4gpu
• https://gitter.im/h2oai/h2o4gpu

24. Q&A