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

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Powers Business with Billion Edge

Datasets

 Information Management  Retrieval

 Industrial  IoT

 Cyber  Defense  Intelligence

 Financial Services  Fraud

Detection

 Life Sciences  Precision Medicine

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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

5-10BEdgeGraphProblems

http://blazegraph.com/ 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


GraphsEnablePeopletoFindKnowledge

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

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http://bit.ly/1UdP2Sn

Blazegraph Stands Out!

Wikimedia Evaluation:

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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

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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

Quorumk=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

Type

of C

ache

or M

emor

y

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

CO

ST C

OS

T

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

Spe

ed-u

p (H

ighe

r is

fast

er)

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

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Need:Simpler,smarteralgorithmsthatrunefficientlyonGPUswithoutrequiringknowledgeoftheparallelprogrammingordevice-specificopTmizaTon.

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

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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

[email protected] 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

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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!

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Challenges of Network Flow Analysis

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•  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

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Interactive Visual Query Session

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Visual Analysis - Identification of Exfiltrated Traffic

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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



Use

rs

Original data sources

Fron

tend

B

acke

nd

Knowledge Graph Creation

Museum visitor

Museums and other sources

•  Data crawling •  Data transformation (CIDOC-CRM) •  Data Interlinking •  Data enrichment / Information

extraction

Cards

Social networks

DBpediaBriTshMuseumData UserData

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 visitor Data 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

Technology

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