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The Impact of High Performance Workloads on the Evolution of the Modern Data Center

Bert Lim

Solutions Consultant, Malaysia, Dell EMC

3

More Data Means More Complex Relationships• To analyze in real-time at a large scale

Amount of Data

4.4 Zb

2013 2020Source: IDC 2014

16+ Zb

Hot Data

44 Zb

The Data Multiplier Effect

Business

Human

Machine

1X 10X 100X

Database Data

VOLUME

VARIETY

VOLUME

VARIETY

VOLUME

VELOCITY

Enterprise/

External Data

Sensor/

External Data

Satellite

Imaging

Sensors

VideoRecording

M2M Log

Files

Bio-

Informatics

Email Documents

Web LogsSocial

• More Data Needs To Be Captured Faster

• Real-time Analytics For Business Insights

• Existing Applications Are Taxed

• Evolving New Applications & Architectures

4

Time is Money

How can faster performance pay off for you?

Source: www.acfe.com

FRAUD

5% of Worldwide GDP

($3.5 Trillion) is lost to

Fraud Annually –

ACFE

E-COMMERCE

More than $3 billion in

lost sales in 2014 (USA ecommerce sites) due to poor performance

Source: www.radware.com Source: www.thinkwithgoogle.com

SEARCH/ADVERTISING

If search results were 0.4 secs slower, Google could

lose over 15 million searches/day resulting in

millions less online ads and revenue

RESEARCH

Biology workloads that were

taking four to five days to run,

and in some cases crashing, are

now finishing in 4 to 5 hours

Source: www.nextplatform.com

http://www.radware.com/


http://www.thinkwithgoogle.com/


5

Future Proof Architecture Needed

LEGACY APPLICATIONS NEW APPLICATIONS

RDBMS

STORAGE SYSTEM

OL

AP

NoSQLIn-Memory DB

Data Pipeline

• Data is now both structured and unstructured

• Larger, undefined working data sets

• Move from batch processing to real-time

6

Vertical Applications Drive Innovation

Innovation Requires

Faster processing of data More cycles

No compromise More complex analytics

Concurrent/Collaborative access Promote experimentation

7

Applications Turn to Parallel File Systems (PFS)

NAS Parallel File system on SAN

File I/O

Storage

File System

Application

Storage

Parallel File System

Application

Compromised Architectural Choices

• Low performance density

• Limited by network bandwidth & latency

• I/O stack bottlenecks

• Can have a complex architecture

Block I/O

Network

StorageNetwork

8

High Performance Parallel File Systems on DSSDDeliver high performance innovation

High

Performance

Flexible

Architecture

Reduce TCO

9

High PerformancePowers next generation analytics applications

10 MILLIONIOPS

100 GB/SBandwidth

144 TBCapacity

100μSLatency

Bring Innovation to Market Faster!

10

Hosts Single or Multiple Apps

AnalyticsData Lake

Flexible Architecture

Minimize Time to Insight

• Simple, agile, & flexible data pipeline served from a single platform

• Move data thru the analytic pipeline anytime without impacting SLAs

• Faster Insights integrating transactional data, big data, and advanced analytics, with high performance parallel file system on DSSD D5

FILE SYSTEM

Flexible & Native Application Interface

Dual Connect up to 48 clients

Client #1 Client #48

Parallel File

System

Oracle Warehouse

11

Lower TCOIndustry leading bandwidth density

DSSD D5’s 5RU footprint is just 1/25th the size of leading solutions

5RU

Simplifies the Environment

Easy to Manage

Lowers TCO

Simplifies the Environment

Architectural Solutions and Benefits

13

Extreme file system performanceAccelerate Your Parallel File SYSTEM (PFS)

10.2 MILLION 4k

IOPS

64.3 GB/S Read

Bandwidth

100 μS Latency

PFS

Up to 48 clients

14

PFS on DSSD– Direct Attached

Simple & scalable architecture up to 48 systems

Server 1 Server 3Server 2 Client 1 Client 45

• 3 EXPRESS Servers

• Up to 48 directly attached systems

• Redundant Connections

• Up to 100TB usable

• Options to scale beyond if needed


15

PFS on DSSD – Hybrid ArchitectureSimple & scalable architecture beyond 48 systems

40Gbps Network

Server 1 Server 8 Fast Client 1 Fast Client 32

• 8 EXPRESS Server Licenses

• Plus N Client Licenses

• Servers dedicated to PFS

• Fast clients with direct access

2x40Gb 10Gb

10GbClient 1 Client N


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PFS PerformanceRead Throughput

Up to 64.3 GB/s

Read

17

PFS PerformanceWrite Throughput

Up to

17.5 GB/s Write

Up to

17.5 GB/s Write

18

PFS PerformanceIOPS & Latency

Up to

10.2M 4k IOPS

Up to

10.2M 4k IOPS

@ 600 μs

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Built for Analytics and Designed for Speed

Simple and Flexible Architecture

Lowest TCO & Highest Bandwidth Density

Accelerates Insights, Enables Better Decisions

PFS on DSSDBrings innovation to Market Faster

FILE SYSTEM

Up to 48 clients

20

Use case: Life Sciences

• Application: DNA Sequencing

• Workload: Second Phase in Bioinformatics Analysis where the data is transferred to Genomic analysis software for analysis, mapping and pairing. Variant discovery and genotyping happens in this stage. Working data set is usually in ~10s of TBs (ex: 74 Files of totaling 140GB)

• Challenge: Analysis takes weeks or days to complete

Background

• With DSSD Parallel File System (PFS) solutions, Bioinformatics analytics software solutions leverage a shared-disk high performance and low latency file management solution that provides fast, reliable access to Next Generation Sequencing (NGS) data for optimizing performance

• Analysis that used to take weeks or days it can be done in hours and many more sequences can be run in parallel

• Faster variant discovery & creating personalized medicine.

Business Challenges Customer Plans to Solve with DSSD

21

Use case: Industrial Manufacturing

• Application: Nastran & Abaqus – Finite element analysis (FEA) simulations

• Workload: Simulation currently running on 10 servers * 100 jobs per Node (1000 copies at once) - coping 2GB files (2TB total)

• Challenge: Performance problems - Current customer infrastructure takes 4 hours to write 2TB

Background

• Using DSSD PFS solution customer can decrease writing wait time from 4 hours to 270 seconds

• DSSD PFS is 53x better compared to their current architecture performance

• Faster analysis and faster time to market


22

Use case: Financial Industry

• Application: Potential Future Exposure (PFE) Simulation, Expected Positive Exposure (EPE) Simulation, and Value At Risk (VAR) Simulation

• Workload: Long Running Parallel Calculations/Simulations. Datasets are 10TBs, low latency and performance is highly important

• Challenge: Need faster analysis and parallel calculations to predict the outcome of an asset

Background

• DSSD PFS solution provides a scalable, low-latency, high performance and reliable file system to support these simulations. Also, it will accelerate parallel calculations such as PFE, EPE, and VAR on very large data sets.

• Larger data set can be analyzed in the same period

• Predict the outcome of an asset faster


23

Background

Use case: Oil & Gas

• DSSD PFS solution provides a scalable, low-latency, high performance and reliable file system to support these simulations.

• This architecture speeds up many HPC simulations including significantly reducing the time it take to run parallel simulation. It also supports high levels on concurrent reads and writes with extreme performance and low latency.

• Faster & more accurate identification of reservoir since larger working data set can be analyzed quicker


• Application: Seismic Processing & Hydrocarbon Reserve Modeling

• Workload: Customers with smaller scale processing and velocity models (e.g. Hydrocarbon subsurface reservoir modeling and simulation). These models and simulations are highly complex and IOPS intensive. These smaller jobs consist of high levels of concurrent read and write IO (Data set size: 1 TBs to tens of TBs)

• Challenge: Compromised working data set (smaller set) and analysis take a long time

24

Use case: High Tech Manufacturing

• Application: Electronic Design Automation (EDA)

• Workload: Analysis and verification is the most time consuming process in Electrical Design Automation flow. Diverse set of software algorithms and complex simulations run for weeks at a time.

• Challenge: Slow and complex NAS architecture. The slowness of network NAS translates to longer simulation runs, longer time to manufacturing, and eventually longer time to market.

Background

• SSD PFS solution provides up to 4-6x more performance than current NAS solutions in this market. DSSD doesn’t require complex network architectures and is not bound by network latencies and performance loss

• With DSSD, EDA customer can finish analysis much faster, run more analysis in the same time, and have a much smaller storage footprint

• Customer benefits include faster time to market and higher quality product with better resource utilization


Welcome to WranglerUsing DSSD Storage to Accelerate Discovery

26

Ranger: 62,976 Processor Cores,

123TB RAM, 579 TeraFlops in 2008

Fastest Open Science Cluster 2008

2 of the 6 Magnum Core Switches

Stampede: #10 in the world today,

Originally #6 in 2013

~500,000 cores 6400 nodes : >10 Petaflops.

First Xeon Phi system in production

28

TIME FOR HUMAN DNA SEQUENCING DECREDED FROM YEARS IN 2000 TO 3 HOURS TODAY (AND

WITHIN A OFFICE VISIT IN THE NEAR FUTURE)

29

For Data Moore’s Law is Not Enough

• Growth in Data production all around

exceeds the pace of expansion of in core

memory

• Not just for genome data

– All sensor derived data is exploding

– Internet of Things…

• Not limited by the CPU

30

DOZENS OF NEW DISRUPTIVE NEW MEASUREMENT DEVICES ARE EMERGING

THAT MAY BE CONNECTED

31

HOW DO WE TACKLE THE PROBLEM FOR DATA

KNOWING WHAT WE DID FOR SIMULATIONS

32

Wrangler: HPC meets Big Data

DSSD D5 bridging performance gap between

data analytics and storage

600 TB Usable DSSD D5 Storage

Software Defined Flash Storage supporting

both HPC and Big Data environments

– 240 TB GPFS file system

– On Demand HDFS for projects

– High Transaction Rate Databases

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Why DSSD D5

Software Defined Storage = Flexible

Environment

– Supporting GPFS, HDFS, Individual Node

Block Store, Object Store

– Support each workflow’s needs on each node

in real time

Unparalleled Throughput & IOPS across

orders of magnitude changes in block size

34

Genomics/Protein GroupingHoffmann Lab (UT Austin)

Comparing DNA sequences to find

common sources of biological

characteristic – MySQL DB stored on GPFS

distributed over 4 D5s

– Wrangler did in 4 hours what our

other systems could not complete in

multiple days

35

Electrical EngineeringAli Yilmaz/Guneet Kanur, UT Austin

Electromagnetic Backscatter Simulation (FFT

computations)

Stampede limited by I/O not core count

Wrangler GPFS accelerates application 5X (40

Nodes vs 256 on Stampede)

Smaller workflow done in RAM only 2x faster

than out or core on DSSD D5

– Same scale would take ALL of Stampede

36

Exceeding Expectations

Chris Mattmann (JPL) exploring data

capture for The Square Kilometer Array

– To be deployed in the 2020s

– Data Capture Rate of 10 GB/s needed by

2023 using HDFS and Apache Kafka

– Wrangler data capture rate with early DSSD software within a factor of 2 of goal

(6.4 GB/s)

› Limited by Java I/O bottlenecks not D5

“it was amazing to be operating in what felt like a limitless environment”

Dr. Jane Wyngaard (NASA JPL)