ibm platform computing cloud servicespscicomp.org/wordpress/wp-content/uploads/2014/05/... ·...

© 2014 IBM Corporation

Platform Computing

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IBM Platform Computing Cloud Service Ready to use Platform LSF & Symphony clusters in the SoftLayer cloud February 25, 2014


Platform Computing

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Agenda

v  Mapping clients needs to cloud technologies

v  Addressing your pain points

v  Introducing IBM Platform Computing Cloud Service

v  Product features and benefits

v  Use cases

v  Performance benchmarks


Platform Computing

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HPC cloud characteristics and economics are different than general-purpose computing

• High-end hardware and special purpose devices (e.g. GPUs) are typically used to supply the needed processing, memory, network, and storage capabilities

• The performance requirements of technical computing and service-oriented workloads means that performance may be impacted in a virtualized cloud environment, especially when latency or I/O is a constraint

• HPC cluster/grid utilization is usually in the 70-90% range, removing a major potential advantage of a public cloud service provider for stable workload volumes

HPC Workloads Recommended for Private Cloud

HPC Workloads with Best Potential for Virtualized Public & Hybrid Cloud

Primary HPC Workloads


Platform Computing

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IBM’s HPC cloud strategy provides a flexible approach to address a variety of client needs

Evolve existing infrastructure to

HPC Cloud to enhance responsiveness,

flexibility, and cost effectiveness.

Enable integrated approach to improve

HPC cost and capability 60%

Access additional HPC capacity with

variable cost model

Private Clouds

Hybrid Clouds

Public Clouds

Based on HPC Cloud’s potential impact, organizations are evolving their infrastructures to enable private cloud deployments, exploring hybrid clouds, and considering public clouds.


Platform Computing

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Are you experiencing any of these pain points?

•  Unable to meet business objectives (delay to market, etc.) •  Existing resources insufficient to meet peek compute demand

–  Long run times on existing cluster or grid –  No access to local technical computing resources (workstation users)

•  Technical resources expensive and time consuming to acquire •  The skills/staff to architect and manage a technical computing infrastructure can

be difficult to acquire

-

10,000

20,000

30,000

40,000

50,000

1 4 7 10 13 16 19 22

Planned Daily Cycle (24 x 365)

Financial Services

0 200 400 600 800

1000 1200 1400 1600

April May June

Planned Project

Life Sciences


Platform Computing

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IBM Platform Computing Cloud Service Making the cloud work for you

Build • Complete, ready to run

clusters in the cloud •  Add additional capacity

in hours instead of months

Manage •  Seamless workload

management, on-premise and in the cloud

•  Transparent user experience

Support •  24X7 cloud operation

support •  Access to technical

computing expertise when you need it

Protect • Data encryption,

dedicated physical machines and network

•  Security through physical isolation

Complete, end to end dynamic cloud solution


Platform Computing

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Ready to use Platform LSF & Platform Symphony clusters in the cloud

IBM Platform Computing Cloud Service (SaaS)

IBM Platform LSF IBM Platform Symphony

SoftLayer, an IBM Company Infrastructure

24X7 CloudOps Support

Client and ISV Applications


Platform Computing

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Dedicated physical and virtual machine infrastructure as a service

•  13+ data centers •  17 network PoPs •  Global private network •  Bare metal and virtual machines

190,000+ SERVERS

21,000+ CUSTOMERS

22,000,000+ DOMAINS


Platform Computing

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Workload I/O intensity

• SoftLayer’s architecture outperforms by >50% equivalent AWS instances for high I/O workloads

Control (APIs, hardware / network configurability)

• SoftLayer offers hundreds of hardware configurations vs. 14 for AWS •  ~2,000 APIs for SoftLayer vs. ~60

for AWS and none for RAX

Integrated platform of multiple architectures

• Unified integration & control panel for multiple cloud architectures • RAX requires paid bridge,

different control interfaces

Ready to use Platform LSF & Platform Symphony clusters in the cloud

Low intensity

workloads

Low degree of control and

customization

AWS IBM

High intensity

workloads

High degree of control and

customization

Single platform Seamless integration

DIFFERENTIATOR RATING IBM ADVANTAGES

RAX


Platform Computing

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Non-shared physical machines for added security and performance

•  Dedicated and isolated compute environment

•  All machine instances are dedicated to the client

•  Each cluster is isolated on a VLAN

•  Only the VPN gateway has an addressable interface

•  All customer data at rest is encrypted on shared file systems

•  When machines instances are decommissioned the disks are scrubbed using DoD approved methods


Platform Computing

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Optimal performance for technical computing apps

Industrial Manufacturing Benchmark – Structural Mechanics

EDA Benchmark (IBM-MESA)

Note: Benchmark results were obtained by IBM and have not yet been externally audited or validated.


Platform Computing

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Run and supported by dedicated, 24X7 HPC Cloud Operations Team

CloudOps functions •  Pre-provisioning: Provide guidance to client on how to enable VPN, multi-cluster settings &

security settings on the client on-premise environment •  One time setup testing: Extensive testing of the cluster prior to release to the client •  Extensive testing of the cluster on every event of flex-up prior to release to the client •  Email alerts prior to flex-down & cluster shutdown operations •  Email alerts in case of any overage (compute hours, download bandwidth) •  Provide billing details of monthly usage including overage details •  Provide support under IBM SLA by experts highly experienced in Platform Computing

products

Value: quality, peace of mind & minimum disruption to business •  Extensive quality checks ensures minimum loss of usage hours & disruptions •  Proactive alerts ensures that in-progress critical jobs are not killed in case of Flex-down &

Cluster Shutdowns and Overages •  Highly trained & experienced Support ensures smooth on-boarding and minimize

disruptions


Platform Computing

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Industry-leading workload management

•  20 years managing distributed scale-out systems with 2000+ customers in many industries

•  High performance workload management combined with intelligent resource scheduling engine

•  Unmatched scalability (small clusters to global grids) and production-proven reliability

•  Heterogeneous – manages System x and Power plus 3rd party systems, virtual and bare metal, accelerators / GPU, cloud, etc.

•  Shared services for both compute and data intensive workloads

•  Integrated solutions with vertical reference architectures

23 of 30 largest

commercial enterprises

Over 5M CPUs under management

60% of top financial services

companies


Platform Computing

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IBM Platform LSF Overview Powerful workload management for demanding, distributed and mission-critical high performance computing environments.

Key Capabilities •  Powerful

- Policy and resource-aware scheduling - Resource consolidation for optimal performance - Advanced self-management

•  Flexible - Heterogeneous platform support - Policy-driven automation - CLI, web services, APIs

•  Scalable -  Thousands of concurrent users and jobs - Virtualized pool of shared resources -  Flexible control, multiple policies

Client Benefits •  Optimal utilization: reduced infrastructure cost •  Robust capabilities: improved productivity •  High throughput: faster time to results

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Platform Computing

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IBM Platform Symphony

Overview Low-latency grid management platform for distributed computing and analytics with sophisticated resource sharing

Key Capabilities •  Accelerates service-oriented applications •  Extreme app scalability and throughput with very low

latency •  Compute and data-intensive applications on a single

platform •  Sophisticated, hierarchical resource sharing •  Open and flexible: choice of OS, frameworks and

languages

Client Benefits •  Increase performance and analytic result quality •  Reduces IT costs - increase utilization, simplify

application onboarding, reduce administration costs

Low Latency / High throughput Sub-millisecond, 17,000 tasks per second

Large Scale 10k cores per application, 40k cores per grid

Efficient shared services Heterogeneous & Open

Linux, Windows, AIX, C/C++, C#, Java, Excel, Python, R

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Platform Computing

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Use case 1 – hybrid cluster

The problem • Existing resources cannot meet peak demand • Resources are expensive and time consuming to acquire • Skills to architect and manage clusters are difficult to find • Fixed or reduced budgets • On-premise constraints in space, cooling and power

The solution • Fully functioning IBM Platform LSF or Symphony clusters are

provisioned on the SoftLayer cloud and connected to the on-premise cluster, expanding capacity as needed

• Leverage MultiCluster capability for managed forwarding of jobs from on premise cluster to off premise cluster

The Value • Access to additional compute capacity on a temporary basis as needed • Near-zero wait times • Reduce costs by paying for only what is used • Pay for additional capacity as an operating expense • Fully supported, end-to-end solution, from the on-premise to the on-cloud clusters • Expected and reliable performance from running technical computing workloads on physical machines • Transparent access to cloud resources, the end user experience does not change


Platform Computing

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Use case 2 – stand-alone cluster in the cloud

The problem • New and emerging need for technical computing • Skills to architect and manage clusters are difficult to find • Resources are expensive and time consuming to acquire •  Inconsistent demand does not justify the investment

The solution • Fully functioning Platform LSF and Symphony clusters are

provisioned on the SoftLayer cloud providing resources as needed

The value § Market-leading Platform LSF and Platform Symphony software

§ Access to technical computing resources on a temporary basis without the need to acquire, install and configure the infrastructure and cluster software

§ Keep costs low by paying for only what is used

§ Pay for capacity as an operating expense

§ Fully supported solution

§ Expected and reliable performance from running workloads on physical machines


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Is IBM Platform Computing Cloud Service a good fit for you?

Business pain points •  And you experiencing lost profit due to missed deadlines? •  Do you experience pressure to convert your compute environment capital expense to

operational expense? •  Have you ever missed a deadline or delayed a project because technical computing

resource procurement took too long ?

Technology pain points •  Do your users ever scale back their analyses to lower fidelity or less accuracy in order to fit

them into the local compute environment or to a time window? •  Do you regularly, occasionally, or permanently have fewer resources (CPUs, disk, memory,

etc) than you would like to have to service the user’s compute demand? •  Do you experience a large variance in compute resource utilization? •  Have you reached, or will you reach the capacity of your datacenter(s), and do you need a

plan to grow beyond that capacity ? •  Are your customers asking you for cloud licenses for Platform LSF or Platform Symphony?


Platform Computing

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IBM Platform Computing Cloud Service Making the Cloud Work for You

Unmatched Expertise Analytics, Technical Computing,

Software, Services and ISV Partnerships

IBM Hybrid Cloud

Consolidation Supporting heterogeneous IBM and non-IBM infrastructure

Cloud Leadership Expertise from

Client Engagements

powered by

On SmartCloud

Unmatched Capabilities Policy-driven Workload

Management

On Premise

Software & Systems


Platform Computing

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Thank You


Platform Computing

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SoftLayer and Amazon EC2 Products tested

NAME

IaaS Provider

CPU Cores Memory (GB)

Disk Space (GB)

Physical / Virtual

Hourly Rate (USD)

SL PM So'Layer 16 64 1000[1] Physical $1.85[2]

SL VM So'Layer 8 8 500[3] Virtual $0.88

SL PM (ded) So'Layer 16 64 1000[1] Physical $3.83[5]

EC2 CC2

Amazon EC2 (CC2)

32 60.5 3360 Virtual $2.40[4]

EC2 2XL

Amazon EC2 (c1.xlarge)

8 7 840 Virtual $0.58

SL Physical Machine Intel(R) Xeon(R) CPU E5-‐2650 0 @ 2.00GHz SL Physical Machine (dedicated) Intel® Xeon® CPU E5-‐2690 0 @ 2.90GHz SL Virtual Machine Intel(R) Xeon(R) CPU E5-‐2650 v2 @ 2.60GHz Amazon CCI2 Intel(R) Xeon(R) CPU E5-‐2670 0 @ 2.60GHz Amazon 2XL Intel(R) Xeon(R) CPU E5-‐2650 0 @ 2.00GHz


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Memory Bandwidth

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

SL PM SL VM EC2 CCI2 EC2 2XL SL PM (ded)

STREAM (higher is better)

COPY

SCALE

ADD

TRIAD

0.00

500.00

1,000.00

1,500.00

2,000.00

2,500.00

3,000.00

3,500.00

4,000.00

4,500.00


STREAM Price Performance (higher is better)

COPY

SCALE

ADD

TRIAD


Platform Computing

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CPU Performance

0

100

200

300

400

500

600

700

800


Elap

sed

Tim

e

SuperPI (lower is better)

0.00

2.00

4.00

6.00

8.00

10.00


thro

ughp

ut p

er d

olla

r

SuperPI Price-Performance (higher is better)


Platform Computing

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Network Bandwidth

1

10

100

1000

10000

100000

1 10 100 1000 10000 100000 1000000 10000000

Ban

dwid

th (M

bits

/s)

Message Size (Bytes)

openMPI

SLVM

EC2 2XL

EC2 CCI2

SL PM

SL PM Dedicated


Platform Computing

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Network Latency

0

20

40

60

80

100

120

SL VM MPI 2 node EC2 2XL MPI 2 node EC2 CCI2 MPI 2 node

SL PM MPI 2 node SL PM (ded) MPI 2 node

openMPI Latency (lower is better)


Platform Computing

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Input / Output Performance

0

50000

100000

150000

200000

250000

300000

350000

0 1 2 3 4 5

kB/s

ec

I/O file size (factor of memory size)

I/O Bandwidth - WRITE (higher is better)

SL VM Write

EC2 2XL Write

EC2 CCI2 Write

SL PM Write

SL PM Ded Write

0

50000

100000

150000

200000

250000

300000

350000

400000

0 1 2 3 4 5

kB/s

ec

I/O file size (factor of memory size)

I/O Bandwidth - READ (higher is better)

SL VM Read

EC2 CCI2 Read

EC2 2XL Read

SL PM Read

SL PM Ded Read


Platform Computing

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Software Compilation

0

100

200

300

400

500

600

700

800

SL VM SL PM EC2 2XL EC2 CCI SL PM Ded

Elap

sed

Tim

e (s

)

Software Compile Performance (lower is better)

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

9.00

SL VM SL PM EC2 2XL EC2 CCI SL PM Ded

Run

s / $

Software Compile Price-Performance (higher is better)


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Life Science (BWA)

SL PM (ded) SL PM SL VM EC2 CCI2 EC2 2XL Series1 20846.481 26509.368 25897.44 22442.7 37491

0

5000

10000

15000

20000

25000

30000

35000

40000

Elap

sed

time

(sec

)

Life Sciences Benchmark (BWA) (lower is better)

SL PM (ded) SL PM SL VM EC2 CCI2 EC2 2XL Series1 22.21 7.79 6.33 14.96 6.04

0.00

5.00

10.00

15.00

20.00

25.00

$ / r

un

Life Sciences Benchmark (BWA) Price Performance (lower is better)


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EDA Benchmark (IBM-MESA)

0

500

1000

1500

2000

2500

3000

3500

SL PM (ded) SL PM SL VM EC2 2XL EC2 CCI2

Elap

sed

Tim

e (s

ec)

EDA - IBM Mesa (lower is better)

0.00

0.50

1.00

1.50

2.00

2.50

SL PM (ded) SL PM SL VM EC2 2XL EC2 CCI2

Run

s / $

EDA - IBM Mesa - Price-Performance (higher is better)


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Provisioning Time

1

10

100

1000

10000

100000

SL PM SL VM EC2 CCI2 EC2 2XL SL PM Ded

Provisioning Time (sec) (lower is better)


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Industrial Manufacturing – Structural Mechanics

1

3

5

7

9

11

13

0 2 4 6 8 10 12 14 16

Spee

dup

(rel

ativ

e to

EC

2 2X

L)

CPUs

One Node - S4D

SL PM

EC2 CCI2

SL VM

EC2 2XL

SL PM (ded) 1

2

3

4

5

6

7

0 2 4 6 8 10 12 14 16 Spee

dup

(rel

ativ

e to

EC

2 2X

L)

CPUs

One Node - S6

SL PM

EC2 CCI2

SL VM

EC2 2XL

SL PM (ded)

1 3 5 7 9

11 13 15 17 19

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 Spee

dup

(rel

ativ

e to

EC

2 2X

L)

CPUs

Two Nodes - S4D

SL PM

EC2 CCI2

SL VM

EC2 2XL

SL PM (ded) 1

2

3

4

5

6

7

8

9

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32

Spee

dup

(rel

ativ

e to

EC

2 2X

L)

CPUs

Two Nodes - S6

SL PM

EC2 CCI2

SL VM

EC2 2XL

SL PM (ded)


Platform Computing

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Industrial Manufacturing – CFD

0

2

4

6

8

10

12

14

16

18

1 3 5 7 9 11 13 15

Spee

dup

(rel

ativ

e to

EC

2 2X

L)

# cores

OpenFoam Speedup Backplane (higher is better)

SL PM (ded)

SL PM

SL VM

EC2 CCI2

EC2 2XL

0

1

2

3

4

5

6

7

8

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31

Spee

dup

(rel

ativ

e to

EC

2 2X

L)

# cores

OpenFoam Speedup Ethernet (higher is better)

SL PM (ded)

SL PM

SL VM

EC2 CCI2

EC2 2XL

ibm platform computing cloud servicespscicomp.org/wordpress/wp-content/uploads/2014/05/... ·...

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