Database Camp 2016 @ United Nations, NYC - Brad Bebee, CEO, Blazegraph

http://blazegraph.com/http://blazegraph.com/
Global Knowledge Collaboration to Cure
Cancer: How GPUs Impact Graph &
Predictive Analytics
Database Camp
July 10, 2016
Blazegraph
Brad Bebee, CEO

http://blazegraph.com/
Exploding Data Volumes Requires New Approach
for Relationship Insight
SYSTAP™, LLC. © 2006-2015 All Rights Reserved
Graph databases are designed to analyze diverse entities and relationships.
Today’s datasets have billions of edges and nodes.
A (very small) Knowledge Graph
2

Powers Business
with Billion Edge
Datasets
 Information
Management
 Retrieval
 Industrial
 IoT
 Cyber
 Defense
 Intelligence
 Financial
Services
 Fraud
Detection
 Life Sciences
 Precision
Medicine
SYSTAP™, LLC. © 2006-2016 All Rights Reserved 3
Blazegraph Vision: The Scalable Solution for Graphs
500+ weekly downloads
Thousands of active deployments
Blazegraph
 Enterprise
High Availability
 Enterprise
GPU Acceleration
 Embedded and
Single Server
Deployments

Precision Medicine and Hospitals
•  5,686 Hospitals in the United States
•  Top 10% have 100B+ Edge Graph Problems
•  256 K-80 GPUs for a 100B Edge Graph Integration Application
•  Syapse Precision Medicine Video
4
3/10/15
5-10 B Edge Graph Problems

Uncovering inﬂuence links in molecular knowledge networks to streamline personalized medicine | Shin, Dmitriy et al.Journal of Biomedical
Informatics , Volume 52 , 394 - 405"
Finding the Next Cure for Cancer is a
Billion+ Edge Graph Challenge

Graphs Enable People to Find Knowledge
A Bunch of Pages! An Answer

Graph Analytic Applications: Cyber Defense Example
•  Path algorithms (BFS, SSSP,
APSP, CC) and their
extensions (closeness,
betweenness, etc)
•  Ranking algorithms (cardinality,
PageRank, BadRank,
SecureRank)
•  Clustering algorithms (canopy,
k-means, Jaccard, community
detection, etc)
•  Many others
7

http://bit.ly/1UdP2Sn
Blazegraph Stands Out!
Wikimedia
Evaluation:
SYSTAP, LLC DBA Blazegraph. © 2006-2016 All Rights Reserved 8

Blazegraph™: Embedded and Single Server
•  High performance, Scalable
–  50B edges/node
–  RDF/SPARQL level query language
–  Efficient Graph Traversal
–  High 9s solution
•  Property graphs
–  Blueprints, gremlin, rextser
•  REST API (NSS)
•  Extension points
–  Stored queries for custom application logic on
the server.
–  Custom services & indices
–  Custom functions
–  Vertex-centric programs
Embedded Server
Standalone Server
9
JVM
Journal
WAR
Journal

Blazegraph™: High Availability
•  Shared nothing architecture
–  Same data on each node
–  Coordinate only at commit
–  Transparent load balancing
•  Scaling
–  50 billion triples or quads
–  Query throughput scales linearly
•  Self healing
–  Automatic failover
–  Automatic resync after disconnect
–  Online single node disaster recovery
•  Online Backup
–  Online snapshots (full backups)
–  HA Logs (incremental backups)
•  Point in time recovery (offline)
10
HAService
Quorum
k=3
size=3
follower
leader
HAService
HAService

The Billion-Edge Graph Challenge:
Scaling Up Requires the Right Paradigm and Hardware
https://datatake.files.wordpress.com/2015/09/latency.png
TypeofCacheorMemory
Access Latency Per Clock Cycle
4.3
3
4
11
11
11
14
18
38
167
0 50 100 150
L1 Cache sequential access
L1 Cache in Page Random access
L1 Cache in Full Random access
Main memory
CPU Cache Access Latencies in Clock Cycles
Graph Cache Thrash
The CPU just waits for graph
data from main memory...

Blazegraph Multi-GPU: Extreme Scale
Traverse 1B Edges/Sec (GTEP) 40x more affordably!
SYSTAP, LLC DBA Blazegraph. © 2006-2016 All Rights Reserved
Cray XMT-2
$~180K per GTEP
COST
COST
Large Hadoop Cluster
$~18M per GTEP 1 GTEP = 1 Billion Traversed Edges Per Second
Blazegraph with
GPU Clusters
$16K per GTEP (K40)
$4K per GTEP (Pascal)

With familiar graph APIs; 200-300x acceleration with almost no code changes.
Enabling GPU Acceleration without Code Changes
Blazegraph GPU
Acceleration
NVIDIA  
Tesla GPU
Graph DB
Blazegraph Plug-in for GPU Acceleration Solves “Graph Cache Thrash”
Funded by and
co-developed with:

172,632 results
187ms on Blazegraph GPU
172,632 results
53,960ms on Blazegraph CPU
LUBM Query #9 U1000 (167,697,263 Edges w/Inference)
Just Add GPUs –Testing Shows 200-300x Speed-up
Graph
Database
Graph
Database
290X

1403
1843
700
0
200
400
600
800
1000
1200
1400
1600
1800
2000
700M Edges Single Node
Xeon 2650 (128G) vs 2 K80
(48G)
1.98 Edges 16 EC2
r3.xlarge (488G) vs 16 K40s
(192G)
1.98 Edges 16 EC2
r3.4xlarge (1952G) vs 16
K40s (192G)
1.98 Edges Spark CPU
Baseline
GPUs 700x-1800x Faster for Graphs
Compared to Apache Spark on 700M and 1.9B Edge Graph
SYSTAP™, LLC. © 2006-2015 All Rights Reserved
Speed-up(Higherisfaster)
1
15
Speed-up over Baseline Spark CPU Configuration

Did you hear the one about the Analytics Developer who is
also a Parallel Programming expert?
SYSTAP™, LLC
© 2006-2015 All Rights Reserved
16
Need: Simpler, smarter algorithms that run eﬃciently on GPUs without requiring knowledge of the parallel
programming or device-speciﬁc opTmizaTon.
Algorithm Developer Parallel Programmer

Blazegraph DASL for GPU Acceleration
Blazegraph DASL – A Domain specific language for graphs with Accelerated Scala using Linear algebra
…you can see why we call it “DASL”
Automation Enabling Analytics Coders to Optimize for GPU
Delivering ease of use of Spark and Scala for graph algorithms and predictive
analytics… your analytic code can work on GPUs and Blazegraph.
DASL Executor Multi-GPU Extension
DASL Translator
DASL Graph Algorithms
…with the speed of CUDA…
…Graph and Machine
Learning Algorithms…

DASL your analytics
•  DASL is functional, domain-specific language (DSL) for graph and machine
learning algorithms
•  Provides of linear algebra primitives supporting the DSL designed for efficient,
multi-GPU execution
•  Reduce the barrier of graphs on GPUS, enabling simpler and smarter
algorithms
•  Co-opt existing data ecosystems, such as Apache Spark and Hadoop, for ease
of adoption and mission impact
SYSTAP™, LLC
© 2006-2015 All Rights Reserved
18
3/10/1
5
Graph and Machine Learning
Algorithms
1000X
DASL Executor Multi-GPU Extension
DASL Translator
DASL Graph Algorithms

Anatomy of a DASL
algorithm
19

GPU-Accelerated DASL Algorithms
Graph
•  Hierarchical
partitioning/graph
coarsening
•  Louvain modularity
•  Jaccard Similarity
•  Triangle counting
Collaborative
Filtering
(Recommendation Systems)
•  User-based, Item-
based
•  Matrix Factorization
with ALS (alternating
least squares)
•  Weighted Matrix
Factorization, SVD+
Supervised Neural
Network Techniques
•  Hidden Markov
Models
•  Multilayer Perceptron
Clustering
•  Canopy, k-Means,
Fuzzy k-Means,
Streaming k-Means
•  Spectral Clustering
Dimensionality
Reduction
•  Singular Value
Decomposition (top-k)
•  Lanczos Algorithm
•  Stochastic SVD
•  QR Decomposition
•  Non-Negative matrix
factorization (NNMF)
•  Distance Geometry
…and many others!

Stay in Touch
Brad Bebee, CEO
beebs@blazegraph.com
http://blazegraph.com
@blazegraph

Case Study - NetFlow Data Analysis
•  What is NetFlow data?
•  NetFlow is a network protocol developed by Cisco for
collecting IP traffic information and monitoring network
traffic. http://www.solarwinds.com/what-is-netflow.aspx
•  Sample NetFlow record
{"start_time":"2007-08-01 14:31:02.946”,”duration":0.000,
“src_addr":"122.166.71.110", “dst_addr":"9.155.118.136",
"src_port":10822,"dst_port":13567,"protocol":"UDP
“,"tcp_flags":"......","input_packets":1,"input_bytes":46}
•  How is NetFlow collected?
•  Generated by routers and forwarded to collection point
•  Processed out of pcap records using tools such as nfcapd/
nfdump
22

Graph Algorithms Applicable to NetFlow
•  Path algorithms (BFS, SSSP, APSP,
CC) and their extensions (closeness,
betweenness, etc)
•  Ranking algorithms (cardinality,
PageRank, BadRank, SecureRank)
•  Clustering algorithms (canopy, k-
means, Jaccard, community detection,
etc)
•  Many others!
23

Challenges of Network Flow Analysis
24
•  The volume of NetFlow packets could not previously be efficiently analyzed or
visualized to identify threats in real time
–  One hour of collection on a modest network can easily generate more than 100M NetFlow
records
–  Visualizing this data to identify points of interest is nearly impossible due to the “hairball
effect”, even after time windowing the data
–  These issues largely led to attempts at batch processing the data on large clusters of
machines using Hadoop and/or Spark
–  Pushing the problem into the batch space puts organizations in the unenviable position of
hoping to identify why and by whom their network was attacked last month rather than being
able to identify an attack as it is occurring.

Initial Graph Filtered by PageRank using DASL
25

Interactive Visual Query Session
26

Visual Analysis - Identification of Exfiltrated Traffic
27

Other new capabilities for high-speed, large-scale
graph processing
New technologies to deliver to deliver an HPC capability for
processing very large scale graphs at high speed.
•  Blazegraph DASL provides a high-level way to author graph
analytics and integrate with Apache Spark and Hadoop Data
ecosystems.
•  Power8 / OpenPOWER bring high speed interconnects for
moving data quickly (3X faster)
•  New NVIDIA P100 GPUs provide stacked memory, 720 GB/s
of memory bandwidth, and unified memory addressing.

Research Space

Users
Original
data
sources
FrontendBackend
Knowledge Graph Creation
Museum visitor
Museums and other sources
•  Data crawling
•  Data transformation (CIDOC-CRM)
•  Data Interlinking
•  Data enrichment / Information
extraction
Cards
Social networks
DBpedia BriTsh Museum Data User Data
Mobile App
•  HTML5 Templates + CSS for mobile
devices
•  Google Glass App
•  QR Code recognition
•  Pattern / image recognition
•  Context reasoning
Knowledge Graph Exploration
•  Templates for visualization (CIDOC-CRM
and external data)
•  Timeline, Maps
•  PivotViewer for visual exploration
•  Semantic Search
•  Graph Analytics (GAS)
Website visitorData Expert
Research Space / British Museum Project Architecture
and Use Cases

System will either have
their own terminology to
qualify place
Or
More commonly it will be
implicit and not explicit.
Relationships both
support and bypass this
heterogeneity at the same
time

FROM means
•  Thing has been
located at a place
or
•  Thing was created at a
place
or
•  Thing was created by
a person from a place
or
•  Thing was part of an
event that happened at
a place
or
•  Thing was acquired at
a place
Research Space / British Museum Video

This subset of FROM is just “is/was located at” – These
are photographs that are located in India

This subset of FROM is just “refers to” India – These
objects may not be in India, or from India, but make
some reference to India. India is a subject.

Things from India created in the 17th century

This is a life boat from a ship
(The Nancy Packet) wrecked
while retuning from India.

Getting to 1 Trillion Edge Graphs on GPUs
-  1 GPU
-  K20 (6G) : 125M edges
-  K40 (12G) : 250M edges
-  K80 (24G) : 500M edges (Gemini)
-  M40 (24G) : 500M edges
-  Pascal (32G) : 750M edges (Future)
-  1 Node
-  8 x K80 : 4B edges
-  8 x Pascal : 6B edges
-  Cluster with 1T edges
-  2000 Pascal GPUs (242 nodes) (Future)
38

Database Camp 2016 @ United Nations, NYC - Brad Bebee, CEO, Blazegraph

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