Companies are exploring data in ways we once only associated with science fiction films. Data scientists and analysts live in a world with access to a plethora of tools to analyze and visualize this data - but considering the vast amount of data businesses collect and the machine learning limitations of CPU compute capacity, end users are forced to design their structures and systems with limitations. Until now. Graphic Processing Units (GPUs) have stepped in to massively advance and parallel machine learning, data science and analytics for companies both small and large. Equipped with the ability to render graphics instantly, GPUs are computing, exploring and visualizing billions of rows of data in milliseconds - all on one chip. As a result, the ability to analyze data and run queries in real-time is giving machine learning algorithms the tools to become even smarter and faster, and is giving companies in industries like financial services, government, retail, adtech and telecommunications the types of tools to compete more effectively, respond more rapidly and tackle challenges they previously considered too hard for their legacy compute platforms.

1. Machine Learning & Data Science in
the Age of the GPU:
Smarter, Faster, Better
Aaron Williams @_arw_
October 22, 2017

2. MapD: Extreme Analytics
2
100x Faster Queries
MapD Core
The world’s fastest
columnar database, powered
by GPUs
+
Visualization at the Speed of Thought
MapD Immerse
A visualization front end that
leverages the speed &
rendering superiority of GPUs

3. MapD System Architecture
Accelerating the existing data infrastructure
3

4. 4
MAPD DEMO

5. GPUs Recharge Moore’s Law
5
GPU Processing Power
50% per year
Data Growth
40% per year
CPU Processing Power
20% per year

6. Core Density Makes a Huge Difference
6
GPU ProcessingCPU Processing
40,000
Cores
20 Cores
*fictitious example
Latency Throughput
CPU
1 ns
per task
(1 task/ns) x (20 cores) =
20 tasks/ns
GPU
10 ns
per task
(0.1 task per ns) x (40,000 cores) =
4,000 task per ns
Latency: Time to do a task. | Throughput: Number of tasks per unit time.

7. MapD Benchmarks
Blogger Mark Litwintschik benchmarked MapD on a billion-row taxi data set and
found it to be up to orders-of-magnitude faster than the fastest CPU databases
7
MapD Core: Comparative Query Acceleration*
System Q 1 Q 2 Q 3 Q 4
BrytlytDB & 2-node p2.16xlarge cluster 36x 47x 25x 12x
ClickHouse, Intel Core i5 4670K 49x 58x 32x 25x
Redshift, 6-node ds2.8xlarge cluster 74x 24x 14x 6x
BigQuery 95x 38x 6x 6x
Presto, 50-node n1-standard-4 cluster 190x 75x 61x 41x
Amazon Athena 305x 117x 37x 13x
Elasticsearch (heavily tuned) 386x 343x n/a n/a
Spark 2.1, 11 x m3.xlarge cluster w/ HDFS 485x 153x 119x 169x
Presto, 10-node n1-standard-4 cluster 524x 189x 127x 61x
Vertica, Intel Core i5 4670K 685x 607x 203x 132x
Elasticsearch (lightly tuned) 1,642x 1,194x n/a n/a
Presto, 5-node m3.xlarge cluster w/ HDFS 1,667x 735x 388x 159x
Presto, 50-node m3.xlarge cluster w/ S3 2,048x 849x 164x 86x
PostgreSQL 9.5 & cstore_fdw 7,238x 3,302x 1,424x 722x
Spark 1.6, 5-node m3.xlarge cluster w/ S3 12,571x 5,906x 3,758x 1,884x
*All speed comparisons are to the “MapD & 1 Nvidia Pascal DGX-1” benchmark
Source: http://tech.marksblogg.com/benchmarks.html

8. Query Compilation with LLVM
8
Traditional DBs can be highly inefficient
• each operator in SQL treated as a separate function
• incurs tremendous overhead and prevents vectorization
MapD compiles queries w/LLVM to create one custom function
• Queries run at speeds approaching hand-written functions
• LLVM enables generic targeting of different architectures (GPUs, X86, ARM, etc).
• Code can be generated to run query on CPU and GPU simultaneously
10111010101001010110101101010101
00110101101101010101010101011101
LLVM

9. Keeping Data Close to Compute
MapD maximizes performance by optimizing memory use
9
SSD or NVRAM STORAGE (L3)
250GB to 20TB
1-2 GB/sec
CPU RAM (L2)
32GB to 3TB
70-120 GB/sec
GPU RAM (L1)
24GB to 256GB
1000-6000 GB/sec
Hot Data
Speedup = 1500x to 5000x
Over Cold Data
Warm Data
Speedup = 35x to 120x
Over Cold Data
Cold Data
COMPUTE
LAYER
STORAGE
LAYER
Data Lake/Data Warehouse/System Of Record
SpeedIncreases
SpaceIncreases

10. The GPU Open Analytics Initiative Model
Standard in-memory format; zero-copy interchange
10
GPU

11. 11
GOAI DEMO

12. MapD Immerse
Using a hybrid approach to speed and scale visualization
12
Basic charts are frontend
rendered using D3 and other
related toolkits
Scatterplots, pointmaps +
polygons are backend rendered
using the Iris Rendering Engine on
GPUs
Geo-Viz is composited over a
frontend rendered basemap

13. Try MapD
It’s free and it’s easy (and @ortelius sez it’s “… the new h0t sh1t”)
13
Play with the live demos:
https://www.mapd.com/demos/
Download the Community Edition:
https://www.mapd.com/platform/download-community/
Join our forums:
https://community.mapd.com/
Review these slides:
https://speakerdeck.com/mapd

14. Aaron Williams
VP of Global Community
@_arw_
aaron@mapd.com
/in/aaronwilliams/
/williamsaaron