Presented during the "Introduction to H2O4GPU and Driverless AI" webinar on April 11th, 2018. Watch the recording here: https://attendee.gotowebinar.com/register/6156356209443281667?source=SlideshareH2O4GPU

1. World's Fastest
Machine Learning
With GPUs
http://github.com/h2oai/h2o4gpu
Speaker: Jonathan C. McKinney

2. H2O4GPU TEAM
Mateusz Erin Navdeep Rory Terry
Karen Arno Jonathan Steve

3. Machine
Learning
c
Deep
Learning

5. 5
RISE OF GPU COMPUTING
GPU-Computing perf
1.5X per year
1000X
by
2025
102
103
104
105
106
107
Single-threaded perf
1.5X per year
1.1X per year
APPLICATIONS
SYSTEMS
ALGORITHMS
CUDA
ARCHITECTURE

6. H2O4GPU
/ Open-Source: http://github.com/h2oai/h2o4gpu
/ Used within our own Driverless AI Product to boost performance 30X
/ Scikit-Learn Python API (and now R API)
/ All Scikit-Learn algorithms included
/ Important algorithms ported to GPU

8. 8
Driverless AI

9. 9
Driverless AI

10. K-Means
• Significantly faster than Scikit-learn implementation (50x)
• Significantly faster than other GPU implementations (5x-10x)
• Supports kmeans++/kmeans|| initialization
• Supports multiple GPUs
• Supports batching data if exceeds GPU memory

11. https://github.com/h2oai/h2o4gpu/blob/master/examples/py/demos/H2O4GPU_KMeans_Images.ipynb
K-Means

12. 12
10 with latest solver

14. Principle Component Analysis (PCA)

16. 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

17. 17
https://www.youtube.com/watch?v=NkeSDrifJdg
171 with latest solver
87
51

18. H2O4GPU
http://github.com/h2oai/h2o4gpu
https://stackoverflow.com/questions/tagged/h2o4gpu
https://gitter.im/h2oai/h2o4gpu
Thank You!
Questions?