The Need for Speed: Parallel I/O and the New Tick-Tock in Computing

Today’s PresentersCo

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Jon ToigoChairman, Data Management Institute

Sushant RaoSenior Director of Product Marketing, DataCore

Presented ByJon Toigo

Managing Principal Toigo Partners InternationalChairman Data Management Institute

Ask anyone…• Application performance, especially in virtual server settings, is

becoming a big problem…• Flash memories and faster disk drives and interconnects seem to be a

temporary fix to the problem…• What can be done? History provides a clue…

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History repeats itself• In the 1890s, Karl Benz, Gottlieb Daimler and

Wilhelm Maybach perfected the first automobiles to run on petroleum.

• Other notables, including Frederick William Lanchester and George F. Foss, improved designs…though Foss drove his vehicle for four years, ignoring official warnings of impending arrest for his “mad antics.”

• Foss’s single cylinder engine produced about .024 horsepower and delivered dizzying speeds of up to 24 mph (45 kmh)

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The Need for Speed• By 1906, two, three, four and eight

cylinder engines began to appear. • The V8, invented by Levavasser in 1902

and first built in 1906 by engineers in Redondo Beach, CA, who called their engine “the Coyote,” leveraged essentially two 4 cylinder engines driving a common crankshaft

• Rolls Royce first manufactured a V8 engine powered vehicle, but the first mass-produced vehicle came from Cadillac, the Model 51, sporting 70 horsepower…

Buick Model F2 CylinderTouring Car

4 cylinder Fiat with 48 horses and56 mph (90 kmph)

Cadillac Model 51 (1915) First production V8 made 70 horsepowerCo

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An example of how embracing parallelism drove performance gains• Over time, we learned to increase the

number of cylinders and to configure them to derive greater power and speed

• 4 cylinder engines produced power strokes every half crankshaft revolution; an 8 cylinder engine every quarter revolution.

• Power strokes are translated to the drive train to increase propulsion

• Crossplane crankshafts were introduced to reduce vibration from multi-engine parallel operations – though racing V8s continue to use a single plane crankshaft for faster acceleration

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Today…• Chevy Camaro SS creates 455 hp using a 6.2 liter

V8; the ZL1 model delivers 184 mph• Ford Mustang GT does 435 hp with a 5.0 liter V8;

the Shelby GT500 version makes 189 mph• The Challenger SRT Hellcat delivers 707 hp and

199 mph (burning 1.5 gallons of petrol per minute) with a 6.4 liter V8

• Those are American “muscle cars”

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Equal time to the Europeans• The Aston Martin Alfa Romeo 4C boasts 237 hp from a

1.7 liter turbocharged 4 cylinder engine• The 2015 Porsche Boxster delivers 265 hp from a 2.7 liter

6 cylinder engine, while the S and GTS produce 315 and 330 hp respectively from a 3.4 liter 6

• Jaguar’s F Type delivers 190 mph and 495 horses from an optional supercharged V8

• The BMW Z4 makes 249 hp from a turbo-charged 2.0 liter 4 cylinder, and sDrive35i models deliver 335 hp from a 3.0 liter twin turbo six cylinder

• Ferrari 458 Italia makes 562 hp with a 4.5 liter V8• Lamborghini Aventador makes well over 200 mph with a

6.5 liter 12 cylinder engine sporting 691 hp

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Okay. We get it…• Engine designers have been coupling many cylinders into parallel

configurations yielding all kinds of expensive go fast cars that few of us can afford…

Mercedes-Benz SLS AMG583 hp from 6.2 liter V8

196 mphStarting at $221,580

Audi R84.2 liter V8 does 430 hp

5.3 liter V10 does 550 hpStarting at $182,500

Lamborghini Huracan LP610-45.2 liter v10 for 602 hp

202 mphStarting at $244,000

Porsche 918 Spyder4.6 liter V8 makes 608 hp$845,000 (recently pulled

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So it has been with application performance…• Server virtualization promised application performance and hosting

efficiency…

• When it wasn’t delivered, different explanations and solutions were proffered…

• Servers overburdened with I/O tasks they aren’t needed to execute – offload these tasks to array controllers (aka server motherboards attached to disk drives) to free up CPU for more important work

• Storage is too slow to keep up with CPU – add lots of Flash memory cache – spoofing the problem

• Storage is too slow to keep up with CPU – ditch “legacy” SAN storage for direct-attached software-defined storage

• VMs producing random I/O, ultimately randomizing data placement on storage: deploy log structuring to overcome the I/O blender effectCo

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Innovation is good when it does the job…• But when the problem is performance, shouldn’t we address the

causes of performance problems…• Four basic categories

• Overloaded CPU• Overloaded Memory• Storage Latency• Network Latency

• Focus has been on storage latency…

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But to understand speed problems, sometimes it helps to look at the track…• The I/O path has lots of interlocking pieces…

STORAGE I/OElements that handle the reading and writing of data to physical storage media

RAW I/O Elements ahead of storage that impact I/O performance

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But some performance issues have to do with inefficiencies in the engine…• The CPU is the engine of the computer• Like cylinders in an auto engine, chip

cores determine the capacity and throughput of the CPU

• As with autos, CPUs have undergone significant change over time…

From the 60s until the early 90s,much work in industry focused onmaking multiple low power CPUswork together in parallel processingconfigurations for improved performance…

First transistorized CPU

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Then came the unicore tick-tock…• Unicore (uniprocessor) – a computer system with a single central

processing unit. All processing tasks share a single CPU

Moore: “Transistors on a die will double every 24 months…”House: “What he said. Plus, we will see chip clock speeds double every 18 months…”

ALU = Arithmetic Logic Unit

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Ramifications…• Computer designs based on sequential

processing and unicore chips drove the PC and server revolution…

• Innovations and speed improvements occurred too fast for the parallel processing engineers to keep pace…leading to some inherently limited thinking…

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It worked…until it didn’t• Two things have happened

• First, unicore ran out of steam (House’s Hypothesis peaked in 2004)

• Second, Moore’s Law proceeded and transistor densities continued to double

• Result: Multicore processors that did not demonstrate significant clock speed improvements…

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Multicore chips + threading technology enabled many more logical cores…• Seized upon by Microsoft, VMware and others to do concurrent sequential

processing of application code• But at the expense of I/O efficiency: I/O waits for sequential processing

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Multicore chips, meet Parallel I/O• Parallel I/O is simply the use of a percentage of available logical CPU

cores to provide a scalable I/O processing engine…• Derived from multiprocessor architecture, implemented by Datacore

as easily deployed and configured software…

Eight “Logical” Cores

Allocate a percentage of logical cores toprocessing I/O exclusively…

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And in parallel…

Truth be told, comparatively few virtualized application performance issues are storage related…

• Easy to see when they aren’t• Despite what hypervisor vendors

may say, what you may need is parallel I/O processing to alleviate RAW I/O congestion and to facilitate I/O operational efficiency…

Short Storage I/O Queues

SERV

ERS

STORAGE

High CPU Processing CyclesHot CPU

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There are other issues that contribute to performance problems…• The I/O Blender Effect is real,

needs to be addressed to prevent randomization of data placement on storage devices

• Interconnects need to be properly configured or virtualized for more efficient allocation

• Storage devices need to be used in a manner appropriate to capabilities and workload requirements…Co

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However, Parallel I/O establishes a new tick-tock in storage that will only improve as cores multiply…

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Copyright © 2015 DataCore Software Corp. – All Rights Reserved.


Impact of Parallel I/O on Storage PerformanceSushant Rao, Sr. Director of Product Marketing


Storage (I/O) is the Bottleneck,especially for Virtualized Infrastructure

1990 2000 2010 2020

Performance gap between Compute

& Storage

Compute vs StoragePerformanceYearly Performance Gains

Compute: 26%

Storage: 2%

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Big Picture: DataCore Parallel I/O Architecture

26

Copyright © 2015 DataCore Software Corp. – All Rights Reserved. 27

IO-Starved Virtualized Servers

Increasingly faster Uni-processors

Com

pute

WorkPotential

2010 20202000

CPU clock ratesslow down

More cores per socket




Com

pute

Serial IO

WorkPotential

2010 20202000




IO Gap

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Com

pute

Serial IO

WorkPotential

2010 20202000




Serial vs. Parallel Processing

1 worker(Serial)

Pile of work

Load 1



1 worker(Serial)

Pile of work

Load 1 Load 2 Load 3



Time to finish1 worker(Serial)

Pile of work

Load 1 Load 2 Load 3 Load 4 Load 5



Time to finish1 worker(Serial)

Pile of work

5workers

(Parallel)

Load 1

Load 2

Load 3

Load 4

Load 5

Load 1 Load 2 Load 3 Load 4 Load 5


Modern Multi-core CPUs

Worker1

Worker2

Worker3

Worker4

Worker5

Worker6

Worker7

Worker8

Worker9

Worker10

Multiple “workers” capable of simultaneously handling compute, networking and I/O loads

10-cores


Standard use of Multi-core CPUsin Virtual Servers

VM1

VM2

VM3

VM4

VM5

idle I/Oidle idle idle

Sequential Computing with Concurrency

Serial I/O

VM = Virtual Machine


idle

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Serial I/O Bottleneck in Virtualized Server

idleidleidleidle I/O

Compute

I/O

Compute waits on I/O CPU cores are wasted Very little work gets done

Workload


idle

37



Compute

I/O


Workload


idle

38



Compute

I/O


Workload


idle

39



Compute

I/O


Workload


idle

40


idleidleidleidleI/O

Compute

I/O


Workload


Impact: Many servers needed to spread I/O

Workload

Server 2 Server 3 Server 4 Server 5Server 1


I/OI/OI/OI/O

Turbo-Charge through Parallel I/O

Compute

I/O

Workload I/O keeps pace with

compute demands CPU cores are fully used Lots of work gets done in

very little timeI/O


I/OI/OI/OI/O


Compute

I/O



very little timeI/O


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Adaptive Parallel I/O

Workload

51


52


Workload

ResponseTime

(millisec)IOPS

52


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Workload

ResponseTime

(millisec)IOPS

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Workload

ResponseTime

(millisec)IOPS

54


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Workload

ResponseTime

(millisec)IOPS

400,000 IOPS< 1 millisec

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56


Workload

ResponseTime

(millisec)IOPS


56


57


Workload

ResponseTime

(millisec)IOPS


57


58


Workload

ResponseTime

(millisec)IOPS


58


59


Workload

No more load


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60


Workload

No more load


60


61


Workload

No more load


61


62


Workload

No more load


62


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Workload

No more load


63


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Workload

No more load


64


Worker1

Worker2

Worker3

Worker4

Worker5

Worker6

Worker7

Worker8

Worker9

Worker10

DataCore’s Adaptive use of Multi-core CPUs in Virtual Servers


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Worker1

Worker2

Worker3

Worker4

Worker5

Worker6

Worker7

Worker8

Worker9

Worker10


VM1

VM2

VM3

VM4

VM5



66


Worker1

Worker2

Worker3

Worker4

Worker5

Worker6

Worker7

Worker8

Worker9

Worker10


VM1

VM2

VM3

VM4

VM5

I/OParallel I/O


I/OI/O I/O I/O

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Translations:

68

Work completes in 1/5th* the time

2* machines can do the work of 10

*Varies based on number of I/O worker cores

\ˈper-ə-ˌlel, ˈpa-rə-, -ləl\ \ˈī-(ˌ)ō\

PARALLEL I/O


Work completes in 1/5 the time 2 machines can do work of 10 5X lower overall solution cost All-inclusive simplicity

► Compute & storage services combined

69

DataCore Parallel I/O Breakthrough


SANsymphony-V Converged Storage

Server Separate compute and

shared storage tiers Virtualized and non-

virtualized applications

Virtual SAN Hyper-converged Compute & shared

storage on same nodes Virtualized applications

Adaptive Parallel I/O available in 2 Products

Free Trial: datacore.com/resources/software-downloads.aspx

http://datacore.com/resources/software-downloads.aspx


100%reduction in

storage-related downtime

71

DataCore Benefits at a Glance

Surveyed DataCore™ Customers Report Up To:

www.techvalidate.com

75%reduction in storage

costs

4xcapacity

utilization

90%decrease in time spent on routine

storage tasks

10xperformance

increase

http://www.techvalidate.com/


25,000+ Deployments Worldwide

10,000+ Customers 10th Gen Product

Companies in all Industries & Sizes

Market: Software-defined Storage

Technology: Storage Virtualization

Main Offices• Australia• Germany• France• Japan• UK• USA

Proven. Globally.


Schedule a 15-minute live demo with one our technical consultants at http://info.datacore.com/LiveDemo

73

Request a Live Demo

http://info.datacore.com/LiveDemo




Thank You!

www.datacore.com

http://www.datacore.com/

The Need for Speed: Parallel I/O and the New Tick-Tock in Computing

Technology

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