HPC in the Cloud - Hewlett Packard Enterprise · HPC in the Cloud Keynote, IEEE 8th World Congress...
Transcript of HPC in the Cloud - Hewlett Packard Enterprise · HPC in the Cloud Keynote, IEEE 8th World Congress...
© Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
HPC in the Cloud Keynote, IEEE 8th World Congress on Services Honolulu, Hawaii, June 26, 2012
Dejan Milojicic
Hewlett-Packard Laboratories
Work with
Nigel Cook, Paolo Faraboschi, Abhishek Gupta, Laxmikant V. Kalé, Sudarsun Kanan,
Richard Kaufmann, Bu-Sung Lee, Filippo Gioachin, Verdi March, and Chun Hui Suen
© Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 2
Who is Dejan Milojicic? • 27 Years of Experience in three world class industrial labs
−HP Labs, Palo Alto, CA,1998-now
−Open Software Foundation Research Institute, Cambridge, MA, ‘94-’98
−Research Institute “Mihajlo Pupin”, Belgrade, Serbia, 1983-1991
• Areas of expertise −Distributed systems; service management; cloud and distributed computing;
HPC; support automation; and systems software
• Education −PhD (93) Kaiserslautern Germany; MSc (86), BSc (83) Belgrade University
• Professional Activities −EIC IEEE Computing Now, IEEE Internet Computing Edit. Board, ICAC’12
general chair, IEEE Services Program Chair, Many PCs, etc.
−IEEE Computer Society, Board of Governors, 2014 President Nominee
−IEEE Fellow, ACM Distinguished Engineer, USENIX member
−Over 120 publications; 11 granted patents,19 filed
© Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 3
HP LABS AROUND THE WORLD
PALO ALTO
BRISTOL
ST. PETERSBURG
HAIFA
BEIJING
BANGALORE
SINGAPORE
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Content Transformation
HP LABS RESEARCH PORTFOLIO
Cloud
Information Management
Digital Commercial Print
Sustainability
Immersive Interaction
Analytics
Intelligent Infrastructure
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What is High Performance Computing?
• Revenue around $10B
• Supercomputers; divisional, departmental, workgroup servers
• Market: servers, storage, middleware, applications, service
• Verticals
− Bio; Chemical; Lifesciences; Medical; Pharmaceutical;
− National Security and Homeland Defense; university and academic
− Automotive; Gas and Oil; Financial; Weather Forecasting
− CAD; CAE; Electronic Design and Analysis (EDA); Geo Engineering
− Gaming, Digital Content and Entertainment, etc., etc.
• Vendors: Bull, Cray, DDN, Fujitsu, HP, IBM, Intel, Mellanox, NEC, NVIDIA, Panasas, SGI, etc., etc.
• ISVs: Adaptive Computing, Altair, Platform Computing, etc., etc
© Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 6
What is Cloud
• Revenue:
−Public Cloud: $21.5B (2010) $72.9B (2015) CAGR 27.6%
− IT infrastructure public Cloud $4.2B (2010) $10.3B (2015) CAGR 19.6%
− IT infrastructure private Clouds $4.8B (2010) $11B (2015) CAGR 17.8%
−Self-built Clouds less than $1B revenue in 2015.
• IaaS:
− Rackspace, IBM Cloud, Dell, HP, Hosteurope, LayeredTech, LongJump, NetApp, Newservers, 10gen, ReliaCloud, Symetriq, Skytap, Zoho,
• PaaS:
− Google AppEngine, Microsoft Azure, VMware CloudFoundry, IBM Workload deployer, CloudBees, SalesForce Heroku, RightScale Zend, Zoho creator,
© Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 7
5 Key Megatrends
7
• Urbanization
• Increased Middle Class
• Aging Population
• Health Awareness
Demographics
• Brands
• Markets
• Competition
• Partners
• Suppliers
Globalization
• Client Interactions
• Referencing
• Advise
• Advertising
Social Media
• Product features
• Usage
• Communication
• Business Models
Mobility
Sustainability
© Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 8
Accelerating Innovation & Change
8
The Internet
Client/Server
Mobile, Social,
Big Data & The Cloud
Mainframe
Database
ERP
CRM
SCM
HCM
HCM
PLM
MRM
Amazon Web Services
OpSource
IBM
GoGrid
Rackspace
Joyent
Hosting.com
Tata Communications
Datapipe
PPM
Alterian
Hyland LimeLight
NetDocuments
NetReach
OpenText
PaperHost
Xerox
HP
Microsoft SLI Systems
EMC
IntraLinks
Jive Software
Qvidian
Sage
salesforce.com
SugarCRM
Volusion
Xactly
Zoho
Adobe
Avid
Corel
Microsoft
Paint.NET
Serif
Yahoo
CyberShift
Saba
Softscape
Sonar6
Ariba
Yahoo!
Quadrem
Elemica
Kinaxis
CCC
DCC
SCM
Cost Management
Order Entry
Product Configurator
Bills of Material Engineering
Claim Processing
Inventory
Manufacturing Projects
Quality Control
Business
Education
Entertainment
Games
Lifestyle
Music
Navigation
News
Photo & Video
Productivity
Reference
Social Networking
Sport
Travel
Utilities
every 60 seconds
400,710 ads requests
2000 lyrics played on Tunewiki
1,500 pings sent on PingMe
34,597 people are using Zinio
208,333 minutes Angry Birds played
23,148 apps downloaded
Unisys
Burroughs
Hitachi
NEC
Bull
Fijitsu
ADP VirtualEdge
Cornerstone onDemand
CyberShift
Workbrain
Kenexa Saba
Softscape
Sonar6
SuccessFactors
Taleo
Workday
Workscape
Exact Online
FinancialForce.com
Intacct NetSuite
SAP
NetSuite
Plex Systems
Cash Management
Accounts Receivable
Fixed Assets Costing
Billing
Time and Expense
Activity Management
Payroll
Training
Time & Attendance
Rostering Sales tracking &
Marketing
Commissions Service
Data Warehousing
98,000 tweets
Finance
box.net
TripIt
Zynga
Zynga
Baidu
Twitter Yammer
Atlassian
Atlassian
MobilieIron
SmugMug
SmugMug
Atlassian
Amazon
Amazon
iHandy
PingMe
PingMe
Associatedcontent
Flickr
Snapfish
YouTube
Answers.com
Tumblr.
Urban
Scribd.
Pandora
MobileFrame.com
Mixi
CYworld
Qzone
Renren
Yandex
Yandex
Heroku
RightScale
New Relic
AppFog
Bromium
Splunk
CloudSigma
cloudability
kaggle
nebula
Parse
ScaleXtreme
SolidFire
Zillabyte
dotCloud
BeyondCore
Mozy
Viber
Fring Toggl
MailChimp
Quickbooks
Hootsuite
Foursquare
buzzd
Dragon Diction
eBay SuperCam
UPS Mobile
Fed Ex Mobile
Scanner Pro
DocuSign
HP ePrint
iSchedule
Khan Academy
BrainPOP
myHomework
Cookie Doodle
Ah! Fasion Girl
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Speed in Business is Increasing Dramatically
9
107 106 105 104 1,000 100 10 1 Seconds
Trading analytics
Airline operations
Call center inquiries
Track financial position
Supply chain updates
Mail/express/fax/e-mail
Document transfer
Phone activation
Refresh data warehouse
Trade settlement
Build-to-order PC
30 minutes 5 seconds
20 minutes 30 seconds
8 hours 10 seconds
1 day 10 seconds
1 day 5 minutes
30 seconds 3 days
3 days 45 seconds
1 month
1 day 5 days
6 weeks 24 hours
3 days 1 hour
1 hour
Server Provisioning 8 weeks 5 Minutes
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HPC in the Clouds
• Use of clouds for HPC growing, limited to small scale, test&dev
• Amazon built a top-500 supercomputer in its cloud
−7 k cores, 41.82 teraflops, 231st fastest supercomputer (at the time)
−with Linux on Intel Xeon X5570 with a 10 Gig Ethernet interconnect.
−de-provisioned soon after, demonstrated supercomputer at $1.60/node/hour
• At high-end HPC, US Department of Energy preparing Exascale program, and so are governments in Europe, China and Japan
−these are boundaries of high-end HPC, evolving as high-end data centers
−major differences slower interconnects, less powerful computation nodes
−similarity is in power, cooling, and packaging
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Characteristic Year
2010 2015 2018
Power 6MW 15MW 20MW
Nodes # 18,700 5,000 100,000
Node concurrency 12 ~1,000 ~10,000
Interconnect BW 1.5GB/s 1TB/s 2TB/s
MTTI Day ~Day ~Day
HPC Evolution towards Exascale
FLAVORS OF HPC
• “ELITE SUPERCOMPUTING”
• Will never move to the cloud
• “MPC” (MEDIUM PERFORMANCE COMPUTING)
• Has already moved (or is quickly moving) to the cloud
• EVERYTHING IN BETWEEN AND THE “NEW HPC”
• It will all eventually move to the cloud
• But, the cloud will have to RADICALLY change before that happens
Cloud
Processor HPC optimized Commodity
Network
HPC specific IBM BlueWaters,
BlueGene, Cray
XMT
Cray XT5
Infiniband x86 + GPGPU +
QDR IB
x86 + QDR IB
Commodity N/A x86 + Gbit
Ethernet clusters
High end HPC
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Motivation: Why Clouds for HPC ?
• No startup/maintenance cost, cluster create time
• Time-to-solution - job wait time
• Elastic Resources, no risk e.g. in under-provisioning
• Power savings, prevents underutilization
• Benefits of virtualization
− Flexibility and Customization
− Security and Isolation
− Migration
− Resource Control
• Hence, a cost-effective and timely solution
− e.g. substitute/addition when Supercomputers are heavily loaded
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How is Cloud Different than HPC System?
• Interconnect: GigE, at best 10GigE vs Infiniband
• Virtualization vs tuned Operating System
• Cost vs grants and quotas
• VM instances (at best clusters) vs job submission system
• Software Architecture
• Security, trust, governance, ..….
• Reliability, availability, support, ……
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Bottlenecks in the Cloud: Micro-benchmarks
• Inferior network performance
• Virtualization overhead on network
• Noise (non-tuned OS, presence of hypervisors)
• Optimized for running business/web apps NOT HPC
• Slow Interconnect major bottleneck: 1-2 orders of magnitude
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What are the bottlenecks
NAMD CPU% for 60 cores on Eucalyptus Cloud, white portion is idle time
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HPC apps sensitivity (Noise/Virtualization)
Difference in core 0 and core 1 performance
– Network emulating driver process runs on core 0 of VM – consumes more CPU cycles as application communicates more
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Economics: Why HPC in the Cloud?
• Variable usage in time (resulting in lower utilization)
• Trading CAPEX for OPEX
• Shift towards a delivery model of Software as a Service
• Cloud users perspective
− Use cloud when their applications fit the profile above
• Cloud providers perspective
− Aggregated resource utilization of all tenants can sustain a profitable pricing model compared to substantial infrastructure investments required to offer a cloud
© Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 19
Economics: Why not HPC in the Cloud?
• HPC systems typically highly utilized
− Queue-based approach keeps supercomputer busy 24x7
• Mismatch between HPC demands and cloud offerings
− HPC application performance sensitive to interconnect but typical deployment in cloud is commodity Ethernet (1Gbps moving to 10Gbps)
− Noise caused by virtualization and multi-tenancy can significantly affect HPC applications in terms of performance predictability and scalability.
• CAPEX/OPEX argument is less clear-cut for HPC users
• Software-as-a-Service offering rare in HPC to date
Cloud Challenge #3: The Cost Of Energy
MUST ADDRESS ENERGY ACROSS THE BOARD: COMPUTE, COMMUNICATION, STORAGE
56X in seven years 16X
Moore’s law
“Physics of data, Myers, Google”
Cloud Challenge #2: Explosion Of Data
Data on higher exponential growth than compute
Online data: 5EB280EB in last 7 years (95%/yr) vs. 40% Moore
Cloud challenge #1: Commodity Interconnects
• HPC apps are latency bound
• Cloud commodity interconnects are inadequate
• Low-latency interconnects are not financially viable in the cloud
• Photonics to the rescue! Some of these issues may diminish as we move to optical interconnects
• The question remains whether we’ll see HPC specialized clouds…
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Outline
• Evaluating HPC in the Cloud
− Interconnects, virtualization, economics, power
• Use Case Genome Sequencing, Science as a Service
• Use Case in Financial Services
• Future trends
− Storage (NVRAM), photonics
• Summary
• What I will not be talking about
− Big data, GPGPU, Exascale, power, reliability
© Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
Interconnect
Abhishek Gupta and Dejan Milojicic, Proceedings of the 6th Open Cirrus Summit, Fall 2011, Atlanta, Georgia, best student paper award
Abhishek Gupta, Paolo Faraboschi, Bu-Sung Lee, Dejan Milojicic, Filippo Gioachin, Verdi March, and Chun Hui Suen, HPDC, to appear
Gupta, et al., “High Performance Computing Applications in the Cloud,” in submission.
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Past Research
• Focus on performance alone as the metric
• Mostly on small scale (up to few 10’s of cores)
• Focus on Amazon EC2
• MPI based
• Pessimistic results
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Research Goals/Questions
• What are the performance bottlenecks in the cloud?
• What application characteristics are crucial to identifying the suitable platform for an execution?
• Is it beneficial to run some applications on supercomputer and some on cloud rather than all on a single platform?
• What are economical aspects & use cases for HPC in cloud?
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Experimental Testbed
Virtualization Testbed
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Hypothesis
Run on Cloud
SC Cloud VM
Cluster
App’s Latency Sensitivity
GFLOPS/sec
Cost
$ vs Performance and Latency Sensitivity
Cloud suitable for some and not all HPC applications
• Application Characteristics
• Scale
• User preference – performance, cost
Run on SC
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Slowdown vs. Parallel Efficiency
0.5
1
2
4
8
16
32
0 0.2 0.4 0.6 0.8 1 1.2
Slow-down
Eucalyptus vs
Taub
Parallel Efficiency on Taub
Candidates for Cloud
0.5
1
2
4
8
16
32
0 0.5 1 1.5
Slo
w
do
wn
i… Parallel …
Efficiency E = S/P, where
P is the number of processors,
Speedup (S) is defined as: S = Ts/Tp where
Ts is the sequential execution time and
Tp is the parallel execution time.
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Benchmarks and Applications
• NAS Parallel Benchmarks class B (NPB3.3-MPI)
• NAMD - Highly scalable molecular dynamics
• ChaNGa - Cosmology, N-body
• Sweep3D - A particle in ASCI code
• Jacobi2D - 5-point stencil computation kernel
• Nqueens - Backtracking state space search
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Performance
31
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Performance Variability
• Coefficient of variation = Standard deviation/Mean
• Significant variability on cloud compared to supercomputers
• Variability increases as we scale up
© Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
Virtualization and Affinity
Abhishek Gupta, Paolo Faraboschi, Bu-Sung Lee, Dejan Milojicic, Filippo Gioachin, Verdi March, and Chun Hui Suen, HPDC, to appear
© Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 34
Simplified Diagram of Virtualization Techniques
No virtualization
OS
Network Interface
OS Host OS
Network Interface
Network Interface
Host OS
Network Interface
Guest OS Guest OS
Container Plain VM Thin VM
User process
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Impact of Virtualization on App Performance
Ping-Pong NAMD ChaNGa
• Lightweight virtualization
−Thin VMs configured with PCI pass-through for I/O
−Containers (i.e., OS-level virtualization).
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CPU Affinity in Physical Infrastructure
Affinity No Affinity
User process
CPU
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CPU Affinity in Cloud
Application-level Affinity Full Affinity No Affinity
User process
vCPU
CPU
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Impact of CPU Affinity in Cloud Platform
Physical Machine (No CPU Affinity)
Thin VM (App-level Affinity) & Physical Machine (CPU Affinity)
All Configurations (CPU Affinity)
• Effect of CPU Affinity
− Application level (bind processes to the virtual CPUs)
− Hypervisor level (bind virtual CPUs to physical CPUs).
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App Performance with various CPU Affinities Thin VM: Impact of CPU Affinity on NAMD Thin VM: Impact of CPU Affinity on ChaNGa
Plain VM: Impact of CPU Affinity on NAMD Plain VM: Impact of CPU Affinity on ChaNGa
© Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
Economics
Avetisyan, A., Campbell, R., Gupta, I., Heath, M., Ko, S., Ganger, G., Kozuch, M., O’Hallaron, D., Kunze, M., Kwan, T., Lai, K., Lyons, M., Milojicic, D., Lee, H.Y., Soh, Ming., N.K., Luke, J.Y., Namgong, H., “Open Cirrus A Global Cloud Computing Testbed,” IEEE Computer, vol 43, no 4, pp 42-50, April 2010.
Campbell, R., Gupta, I., Heath, M., Ko, S., Kozuch, M., Kunze, M., Kwan, T., Lai, K., Yan Lee, H., Lyons, M., Milojicic, D., O’Hallaron, D., and Chai Soh Y., “Open CirrusTM Cloud Computing Testbed: Federated Data Centers for Open Source Systems and Services Research,” Proceedings of the USENIX HotCloud’09.
© Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 41
Single site Cloud: to Outsource or Own?
• Medium-sized organization: wishes to run a service for M months
− Service requires 128 servers (1024 cores) and 524 TB
− Same as UIUC cloud site
• Outsource (e.g., via AWS): monthly cost
− Storage ~ $62 K
− Total ~ $136 K (using 0.45:0.0.4:0.15 split for hardware:power:network)
• Own: monthly cost
− Storage ~ $349 K / M
− $ 1555 K / M + 7.5 K (includes 1 sysadmin / 100 nodes)
• Breakeven analysis: more preferable to own if:
− M > 5.55 months (storage)
• Not surprising: Cloud providers benefit monetarily most from storage
− M > 12 months (overall)
• With underutilization of x%, still more preferable to own if:
− x > 33.3%
− Even with CPU util of 20%, storage > 47% makes owning preferable
41
© Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 42
Economics: Why Cloud for HPC? (Revisited)
• HPC users in small-medium enterprises much more sensitive to the CAPEX/OPEX argument
− Startups with HPC requirements (e.g., simulation, modeling)
− Small medium enterprises with growing business and existing HPC infrastructure
• Ability to take advantage of a large variety of different architectures
− Better utilization at global scale
− Potential savings for consumers running HPC applications on most economical architecture while meeting performance expectations
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HPC Economics in the Cloud
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Cloud Bursting and Mapping
• Applications behave differently on different platforms,
− interesting cross-over points when considering cost
• Mapping Applications to platforms
− which application to burst
− which cloud to burst to
• Benefits
− Performance and cost
− Increased resource utilization
− Match between user expectations and application execution
© Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 45
Related work • Studies on HPC in cloud
− Walker, He et al., Ekanayake et al.
• US DoE’s Magellan project
− compared conventional HPC platforms to Amazon EC2 and used real applications
− concluded that interconnect and I/O performance on commercial cloud severely limits performance and causes significant variability in performance across different executions.
− more cost-effective to run DOE applications on in-house supercomputers rather than on current public cloud offerings.
• Cost Evaluation for HPC in Cloud
− Napper and Bientinesi, Gupta and Milojicic
© Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 46
Lessons and Conclusions
• A hybrid cloud-supercomputer platform environment can outperform its individual constituents
• Lightweight virtualization is important to remove overheads for HPC in cloud
• Application characterization in the HPC-cloud space is challenging but the benefits are substantial
© Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
Nigel Cook, Dejan Milojicic, Richard Kaufmann, and Joel Sevinsky
Proc. 7th Open Cirrus Summit, Beijing, China, 2012, best student paper award
n3phele: Open Science-as-a-Service Workbench for Cloud-based Scientific Computing
© Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 48
100 GB/ run
21st Century BioScience Challenges
“To define the normal human microbiome, HMP researchers sampled 242 healthy U.S. volunteers, collecting tissues from 15 body sites in men and 18 body sites in women, [and processing] through all 3.5 terabases of genomic data”
Human Microbiome Project press release June 14, 2012 Processing
pipeline
innovation
© Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 49
Existing Approaches
• Packaged Software builds
− BioLinux, QIIME
• Integrated Software & Workflow platforms
− CLOVR, GALAXY, YABI
• Software as a Service
− CLOTU, GALAXY, YABI, NCBI
• Research Clouds
− Magellan, DIAG
Main (Galaxy hosted )
Local Galaxy Install
Cloud
Your data sets are moderately sized Yes Yes Yes
Your computational requirements are moderate
Yes Yes Yes
You want to share your Galaxy objects with others
Yes Yes Yes
All needed Tools are installed on Main.
Yes ? Yes
Your data sets are very large No ? Yes
Your computational requirements are very large
No ? Yes
You have absolute data security requirements
No Yes Yes
Choice of Approach
from: Galaxy Web Site http://wiki.g2.bx.psu.edu/Big%20Picture/Choices
© Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 50
n3phele (nef-el’) approach
• All information content and computing on cloud
• Access via browser, nothing to install on laptop or tablet
• Existing software runs without being re-written
• No progr. language/OS constraints; No platform integration
• Independently develop, process based on cloud publishing
Decouple UI, orchestration from software publication, execution
Cloud paradigm not virtualization
IaaS Compute Clouds
meta-data
n3phele
requests
Directed execution & data transfer
commands history activity
orchestration
users
Independently published software
Cloud repository
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Cloud repositories
https:// n3phele .appspot .com
Execution Clouds/grids
S3
HP Cloud
/Open CIrrus
ec2Factory Open Stack Factory
qiime
agent
ami-24858
qiime
ami-1234
File copy
Requests xfer, execute, status
vm create/delete/monitor
agent
Amazon
EC2
commands
accounts
activities repositories
Java servlet instance
Google object store (replicated)
Instance VMs
Google App Engine Components
queue
Swift
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Demo
52
© Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 53
Command Definition & Primitives
• JSON definition published by any user
• Defines command parameters, required I/O files, and sequence of actions
• Actions execute based on availability of their dependencies: values from other actions, and input files
• UI and orchestration automatically derived from JSON definition
• Commands execute inside a container on VM; n3phele ensures all command dependencies are available before execution commences
Actions
• Create n VMs
• Execute shell on VM
• Fork n copies of series of action
• Wait for all fork components to finish
• Copy a file to/from Repo and VM
Command: “qiime”
Inputs: “map”, “fasta” Outputs: “otu_table”
Params: “limit” default=0.9
Cloud: “ec2” Input: “map” is “~ubuntu/wf_da/map.txt” Actions:
createVM name=x, ami-24858 runShell name=init, agentURI=“$<x>.ip”, cmd=“cd wf_da; process.py –l $<limit> map.txt ”
© Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 54
Microbial Study of Gas Field • Using QIIME open source toolset
published by CU Boulder Knight lab
• Complete analysis <$200
• Multi-core data set Roche 454 denoising
• QIIME core analysis of community
Orchestration Class (# instances/run)
Run (ms)
1 2 3 4 5 Average
Activity(1) 76.8 64.6 62.8 73.0 63.9 68.2
createVM(1) 11.8 3.90 3.9 3.6 3.58 5.4
fileXfer (7) 14.3 19.1 10.3 11.7 26.7 16.4
Iterator(1) 4.2 3.5 3.5 3.4 9.2 4.7
Join(1) .6 .4 .6 .4 .5 .5
shellExecution(8) 19.3 28.7 11.8 21.8 10.1 18.3
Total (s) 127.0 120.2 92.9 114.0 114.0 113.6
N3phele orchestration & delegated worker lifecycle management CPU Consumption (ms) for Small (4 Amazon instance/30 minute) Denoising Run (600 Mhz GAE CPU)
© Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 55
Conclusion
• Demonstrated cloud-based workbench Architecture, Design and Implementation with separation of application publishing, workload execution & orchestration
• Cloud computing is cost effective for scientific analysis, providing on-demand access from anywhere
• Google app engine provides a good prototype, but more control over delivered performance may be required
• Acknowledgements: R. Knight, G. Caporaso, A. Gonzalez, P. Marshall, H. Tufo CU Boulder
• Cloud resources from: F. Meyer (ANL), Amazon, HP
• Work funded in part by NIH HG004872
© Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
Financial Vertical
“Cloud Computing, Implications on the Financial Industry,”
Presentation at the CIAB, a Financial Services Congress in Brazil, June 2010
© Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 57
Financial Applications
• Key applications going forward − Risk management (driven by the demand to remedy past failures)
− Trading systems (driven by fluctuating resource demand)
• Key benefits − Flexibility of dynamic resource allocation (eg Monte Carlo simulation)
− Enables some of the smaller financial firms (eg hedge funds)
• State of adoption − Evolution of Grids already deployed by many banks
− Perceived security risks, but experiments underway (ML, BA)
− Grid software vendors also moving to Cloud (Platform, Data Synapse)
− North and Latin America leading in IT spending
© Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 58
Risks and Opportunities in Financial Clouds
• Security (confidentiality, integrity, availability)
− No access to physical resources, multitenancy, different security models
− Data in Cloud reduces exposure, homogeneity simplifies auditing, automated security management, etc.
• Regulatory compliance
− Export rules, privacy rules, global coverage (across region)
− Automation already in place (single control point), awareness raised
• Ilities: performance, availability, business continuity
− Lack of QoS, SLA standards/enforcement, will providers go away
− Marketplace of service providers, competition, geographical distribution
• Data lock-in
− Network performance lacking, proprietary I/F, semantic models
− Promising optical networking research, standardization in progress
© Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 59
Financial Services Predictions and Cloud Implications
• Banks will focus on delivering information to user
− Analytics, data warehousing in the Cloud for processing & storage
• Risk compliance and regulations will be important
− Huge consumer of processing
• New business models seeking profitability
− Long tail of services: prepaid cards for teens; interbank protocols; unstructured data community; mobile banking platforms, etc.
• Lower tier capital firms will rise
− Startup equivalent, ideal for the use of Cloud
• Driving efficiency out of IT
− Ultimate consolidation through the use of Cloud
© Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
HPC in the Cloud Research and Testbeds
© Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 61
Open Cirrus™ Cloud Computing Testbed Shared: research, applications, infrastructure (20K cores), data sets
Global services: sign on, monitoring, store. Open source stack (prs, tashi, hadoop)
Sponsored by HP, Intel, and Yahoo! (with additional support from NSF)
• 15 sites currently, target of around 20 in the next two years.
61
GaTech
China Telecom CESGA
Univ.
Univ. Ind. Research
University
China Mobile
HP Confidential
Chinese Academy of Sciences
Ind. Research
Ind. Research
© Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 62
What kinds of research projects are Open Cirrus sites looking for?
• Open CirrusTM is seeking research in the following areas (different centers will weight these differently) •Datacenter federation
•Datacenter management
•Web services
•Data-intensive applications and systems
•Hadoop map-reduce applications
• The following kinds of projects are of less interest •Traditional HPC application development
•Production applications that just need lots of cycles
•Closed source system development
62
© Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 63
Cloud Sustainability Dashboard (CSD)
Open Cirrus
Site
Economical ($) Ecological Social
IT cooling ntwk support econo.
overall
CO2
(tonnes-eq)
water
(mill. Gal)
Resource
Use (GJ-eq)
ecolog.
overall
State of
devt.
Risk of
instability
social
overall
Site 1 $0.72 $0.35 $0.16 $0.43 6.0 2.6 83 High Low
Site 2 $1.27 $0.59 $0.21 $1.11 6.8 3.3 96 High Very Low
Site 3 $1.05 $0.47 $0.12 $1.07 5.9 2.3 81 High Low
Site 4 $0.75 $0.35 $0.12 $0.61 6.1 2.7 85 High Very Low
Site 5 $0.27 $0.13 $0.05 $0.09 4.3 2.4 59 Low High
Site 6 $1.82 $0.77 $0.11 $1.17 10.2 4.3 142 High Low
Site 7 $1.23 $0.54 $0.11 $0.98 15.0 4.4 192 High Low
Site 8 $0.55 $0.26 $0.10 $0.16 6.9 2.6 95 Med. Low
Site 9 $1.01 $0.44 $0.10 $0.83 5.3 2.5 74 High Very Low
Bricks-and-
Mortar (US) $0.58 $0.70 $0.12 $0.83 9.0 2.1 127 High Very Low
© Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 64
CSD Summary
•A systematic approach for representing and assessing sustainability of Clouds
• Derived from a comprehensive model (economical, ecological, social)
• Automated, real-time Cloud Sustainability Dashboard
• Express, assess and display run-time sustainability of Cloud & Cloud services
• Preference-based customization
• Opportunities for integration with different enterprise tools
EvaluationSustainability Models
The CloudHP
Yahoo
UIUC
Intel
KIT
IDA
MIMOS
RAS
ETRI
internet
Compute, Network, Storage, Application, Power, Cooling
Data Center or Cloud
Visualization
Dashboard
Sustainability-aware
Management
Sustainability Metrics(Energy, Cost, CO2, Water, Risk…)
© Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 65
Workshop on Cloud Services, Federation, and the 8th Open Cirrus Summit In conjunction with “ICAC’2012, San Jose, 18-20 Sep. 2012"
− http://fedcloud.cyberaide.org/, contact: [email protected]
Deadlines:
− Submission (Jul 14, ‘12); Notification (Jul 31, ‘12); Final (Aug 14, ’12)
Summary: This workshop will build upon the success of the prior Open Cirrus events and the prior Open Cloud Consortium events. The goal is to help building a community for those responsible for operating clouds and cloud testbeds, as well as those interested in designing new cloud services Co-sponsors: Open Cirrus, Open Cloud Consortium, and FutureGrid
Goals:
− Bring together researchers and practitioners to discuss the newest ideas and challenges in cloud services and federated cloud computing
− bring together those responsible for designing, managing, and operating clouds services so that they can share experiences with each other
− The workshop also welcomes users with requirements for new cloud services
− We are particularly interested in cloud services for federating clouds
© Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 66
Topics of interest
• Experiences, best practices, lessons learned from operating cloud services
• Testbeds for designing new cloud services
• Cloud services for federating clouds
• Management and provisioning of cloud services
• Health and status monitoring of cloud services
• Security of cloud services
• Requirements for new cloud services
• Reliability and fault tolerance of cloud services
• Cloud services that span public and private clouds
• Cloud services design: Intercloud; Federation; Identity; Cloud bursting; etc.
• Cloud services for emerging applications
• Applications utilizing such services
© Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 67
Organizers
General Chair: Michael Kozuch (Intel, Open Cirrus)
Program Chairs: von Laszewski, Gregor (Indiana U, FG) Grossman, Robert (U Chicago, OCC)
Steering Committee
Grossman, Robert (U. Chicago, OCC) Keahey, Kate (ANL, Nimbus) Kozuch, Michael (Intel, Open Cirrus) Milojicic, Dejan (HP Labs) von Laszewski, Gregor (Indiana U, FG)
Program Committee
Brandic, Ivona (TU Vienna) Desai, Narayan (ANL, Magellan) Desprez, Frédéric (INRIA, Grid5000) Diaz, Javier (Indiana U, FutureGrid) Fitzgerald, Steve (Eucalyptus) Fox, Geoffrey (Indiana U, FutureGrid) Gavrilovska, Ada (GaTech) Grossman, Robert (U. Chicago, OCC) Keahey, Kate (ANL, Nimbus) Kozuch, Michael (Intel, Open Cirrus) Llorente, Ignacio M. (OpenNebula) McGeer, Rick (HP Labs) Milojicic, Dejan (HP Labs) Riedel, Morris (FZ Juelich, EMI) Toews, Everett (Cybera) von Laszewski, Gregor (Indiana U, FG)
© Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
Some of the Trends
“Memristos and Photonics,” Norm Jouppi’s presentation at ISC
NEW Non-Volatile Memories: Memristors
© Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 70
The Prediction of a New Circuit Element: The Memristor
L. O. Chua, IEEE Trans. Circuit Theory 18, 507 (1971)
RESISTOR
dv = R di
CAPACITOR
dq = C dv
INDUCTOR
dφ = L di
MEMRISTOR
dφ = M dq
dq /dt = i
dφ
/dt =
v
i q
v
φ
1827
1831
1745
1971 )](,[
)(tiwf
dt
tdw
)()](,[)( titiwRtv rigorous
definition
Ohm
Faraday Chua
Von Kleist
Quasi-static conduction eq.-
R depends on state variable w
Dynamical equation –
Evolution of state in time
Memristors
• Class of Resistive RAM (RRAM)
• Crosspoint memory allows 4F2 cell
• 2.8nm junctions demonstrated
• 36X denser than today
• Can fab with multiple layers
• 4 layers -> 1F2 cell
• Die can be stacked too Xbar layer
wiring layer
CMOS layer
New Main Memory Technologies Don’t Come Along Every Day
• Core memory (1960)
• DRAM (1973)
Memory Hierarchies with NVMs
On-chip memory
(SRAM)
Off-chip memory
(DRAM)
Secondary Storage
(HDD)
Solid State Disk
(Flash Memory)
How to leverage NVMs in memory hierarchies?
New NVRAMs Are Disruptive Technologies
• We could just use NVRAM to replace disks & main memory
• Keep software the same
• But
• Storage I/O can speed up by 1000X and become byte addressable
• Memory can be huge
• Memory can be nonvolatile
Don’t let a disruptive
technology go to waste!
© Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
Improving HPC I/O in Cloud with NVRAM
Using active NVRAM for I/O staging. Sudarsun Kannan, Ada Gavrilovska, Karsten Schwan, Dejan Milojicic, and Vanish Talwar, In Proceedings of the 2nd international workshop on Petascal data analytics: challenges and opportunities (PDAC '11).
Using Active NVRAM for Cloud I/O, Sudarsun Kannan, Dejan Milojicic, Ada Gavrilovska, Karsten Schwan, Hasan Abbasi and Vanish Talwar (GT/HP Labs/ORNL), 6th Open Cirrus Summit, 2011
© Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 76
• Need for efficient HPC I/O in cloud: • HPC applications demand high I/O throughput
• I/O needs for checkpoint, output, and diagnostic data
• Low network bandwidth in cloud compared to supercomputers
• Substantial I/O data movement cost across network
• I/O storage in traditional HPC is distributed (e.g., Lustre)
• Further, for efficient use of I/O, data needs to be post processed, for example, application diagnosis, visualization, data compression
• State of the Art: Data Staging • Main focus to reduce impact due to slow disks
• Intermediate I/O Staging nodes and in situ data processing: I/O Staging - ‘a partition’ of nodes service I/O needs of large number of compute nodes
• Other ideas: replacing disks with SSDs
• Problems with above approach in Cloud? • Network data movement to staging nodes still a problem
• Huge memory requirements from intermediate staging nodes
Motivation
© Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 77
Network
Interconnect
HDD HDD
File System (e.g. Lustre)
Compute
VMs C1 C3
SSD
Node local NVRAM
Node local NVRAM
C2
Approach : Node local NVRAMs
• Node local NVRAM for app I/O
• NVRAMs 100X faster than SSDs
• NVRAM used like an additional heap
• Fast I/O to node local NVRAM follow by async I/O to dedicated storage
• VM unused compute cycles used for I/O data post processing before network transfer (e.g., compression and data reorganization)
© Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 78
0
500
1000
1500
2000
20 22 30 35
Tim
e (
Sec
)
I/O Data (GB)
Post processing - NVRAM(Sec)
Post processing- Staging(Sec)
For smaller data sizes, I/O
staging performs better
For larger data sizes,
active NVRAM performs
better
Analysis and Initial Results
• High I/O performance gains compared to DataStaging
• DataStaging – I/O throughput -1.2 GB/sec
• Node local NVRAM approach – 8 – 10 GB/sec (predicted)
• I/O + data post processing performance compared to DataStaging
• I/O data compression used as an case study for GTC application
• Initial results show 60 % - 80% app performance improvement using node-local NVRAM for I/O and post processing approach
Photonics
80
Photonics Portfolio and Time Scales
meters
centimeters
millimeters
Now 1 Year 5 Years 10 Years 3 Years 7 Years
11 October 30, 2007 HP DARPA Exascale Study — HP Proprietary & Confidential
2017 Design: 256 cores
L1-I
Core 0
Core 1
Core 2
Core 3
L1-D
L1-D
L1-I
L1-I
L1-D
L1-D
L1-I
L1
↔ L
2 In
terf
ace
Arbitration
Data
&
Control
Broadcast
Off-Chip
ModulatorsModulators
ModulatorsModulators
Modulators
Modulators
ModulatorsModulators
Modulators
Detectors Detectors
0
123
N-1
N
N+1
6162
63
2 fixed drop
filters
64 variable
drop filters/
detectors
4-waveguide
bundles
Modulators Detectors
Splitters
Modulators Detectors
Splitters
Hub
MC
NI
Through Silicon Via Array
Direct
ory
L1
↔ L
2 In
terf
ace
My X
-ba
r
Co
nn
ectio
n
Pe
er
X-b
ar
Co
nn
ectio
n
L2
Cache
Laser
Star coupler 1 Star coupler 2
22 October 30, 2007 HP DARPA Exascale Study — HP Proprietary & Confidential
Optical DIMM Architecture
Processor Chip
2nd
Channel1st Channel
Optical
DIMMs
DRAM DRAM DRAM DRAM
OMB
DRAM electrical
signaling
Optical Waveguide/Fiber
Optical
DIMM
Splitter Modulator
Photo-
detector
Opto-
electronic
signal
conversion
DRAM electrical signaling
Optical
OMB
Near Term: Optical Multidrop Bus
• Replace electrical transmission line with optical waveguides
• Replace electrical stubs with optical taps
• Two Unidirectional buses: 12 bit wide @ 10Gb/s = 30GB/s
• Master broadcasts to each module on the bus
• Distribute optical power equally among modules
• Each module sends data back to master at full bus bandwidth
• Lower latency with reduced power
Master Module A Module B Module C Module D
M
Tx Rx Rx Tx Rx Tx Rx Tx Rx Tx
BS1
BS1
M BS2
BS2
BS3
BS3 BS4
BS4 12
12
1x8 Fanout
1 2 3 4 5 6 7 8 M
3cm
Light beams from taps.
bus Last tap exits bus end IR camera image
VCSEL driven from BERT thru bias-tee
Light input
30 cm
© Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 83
Injection molded 12 channel Hollow Metalized Waveguides
0.06dB/cm
12 channels
Coated waveguide
Optical Backplane Assembly
Tap Detail
window
guide pins
Tx input (12 ch)
Tap 1 Tap 2 Tap 3 Tap 4 Tap 5 Tap 6
12 waveguide output
Near field of HMWG
Waveguides
Eye diagrams from all 6 taps @ 10Gb/s
Tap 1
Tap 2
Tap 4
Tap 5
Tap 6
Tap 3
ch 1 ch 2 ch 3 ch 4 ch 5 ch 6 ch 7 ch 8 ch 9 ch 10 ch 11 ch 12
All 12 channels active
(after receiver) Optical eye @ 10Gbps
ER > 5dB
20 ps/div
PRBS 27 -1
BER < 10-15
Potential Applications of Optical Busses
• Network switch backplane
• Demonstrated at Interop ‘11
Long Term: Integrated Photonics
• The 2000 telecom bubble based on discrete optics
− Think pre-Noyce/Kilby era in electronics
− Components are measured in mm
− Hand alignment
− Expensive and not scalable
• Recent research is on integrated photonics
− Think post-Noyce/Kilby era in electronics
− Components are measured in a few mm
− Manufacture many thousands per die
− Advances in lithography -> better devices
Source: Newport Corp.,
Assembly Magazine,
September 2001
Ring Resonators
• A modulator – move in and out of resonance to modulate light on adjacent waveguide
• A switch – transfers light between waveguides only when the resonator is tuned
• A wavelength specific detector - add a doped junction to perform the receive function
One basic structure, 3 applications
SiGe Doped
Long Term: The Corona Manifesto
• Take full advantage of nanophotonics
• Don’t just replace today’s wires with optics
• Redesign from the ground up
• No off-chip or cross-chip electrical wires
• Restore balance: memory bandwidth scales
• All memory readily reachable from all cores
OCM
OCM
OCM
OCM
OCM
OCM
OCM
OCM
OCM
Corona compute socket
Cluster
0
Cluster
1
Cluster
63
Optical Crossbar
Fiber I/O’s to
OCMs or
Network
Package
Memory Controller/Directory/L2 Die
Processor/L1 Die
Analog Electronics Die
Optical DiepgcTSVs
Face to
Face Bonds
Heat Sink
Laser
pgcTSVs
sTSVs
Performance (LMesh/ECM = 1)
Applications that don’t fit in cache show 4-6X improvements with Xbar
On-chip Network Power
Optics can reduce network power of aps that don’t fit in cache by 6X
Optics Summary
• Optical Bus
• Can build today
• Distance not an issue
• Provides good fan-in and fan-out (>8)
• Can combine to form crossbars
• Integrated Photonics
• Has great long term potential
• Bandwidth scales to 1,000 threads
• Coherent shared memory still possible
• Low and uniform memory latencies
Fiber I/O’s to
OCMs or
Network
Package
Memory Controller/Directory/L2 Die
Processor/L1 Die
Analog Electronics Die
Optical DiepgcTSVs
Face to
Face Bonds
Heat Sink
Laser
pgcTSVs
sTSVs
© Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 93
Outline
• Evaluating HPC in the Cloud
− Interconnects, virtualization, economics, power
• Use Case Genome Sequencing, Science as a Service
• Use Case in Financial Services
• Future trends
−Storage (NVRAM), photonics
• Summary
© Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 94
Summary
• HPC On the Cloud
− Clouds increasingly used for HPC, but trail high end supercomputers
− Challenges: Interconnects, power, scale of data
− New technologies offer promise: photonics, NVRAM
• HP Labs research in support of HPC in Clouds
− Basic architectural work: memristor, photonics
− Understanding performance, scale, power
− Moving up the stack: Science as a Service
− Open Cirrus Cloud computing testbed and related research
© Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
Thank you
© Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 96 96
Questions…?
© Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 97
Mapping Approach and cost benefits
Exploring the Performance and Mapping of HPC Applications to Platforms in the Cloud by Abhishek Gupta et al. in HPDC 2012
© Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 98
Benefits
• Intelligent decisions to determine mapping between applications and platforms
• Prevention of overloading of one infrastructure while others may be less loaded
− Better utilization
− Proper match between demand and supply
• Match between user expectation and application execution
• Reduced wait time for an application
− Incoming mix of applications scheduled to members in the set of platforms rather than getting concentrated on one.
− Increased overall throughput
© Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 99
Contributions
• We analyzed performance of HPC applications on cloud and compare it with a range of dedicated platforms
• We analyzes impact of virtualization on HPC apps and proposed techniques, such as thin hypervisors and OS-level containers, to mitigate performance overhead and noise (jitter)
• We discussed a few concrete scenarios of cloud deployments and show that small/medium-scale HPC users are the most likely candidates to benefit from an HPC cloud
© Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 100
The HP Cloud Architecture
100
HP Portal Experience Catalog, Service Request Management, Account Management, Aggregation, Performance, Cost, Billing, Service Reporting
HP Private Cloud Licensed Products HP Managed Cloud Services HP Cloud Services
HP Converged Management & Security Policy, Orchestration, Monitoring & Security
HP OpenStack
HP Converged IaaS Controller
Hybrid Cloud Solutions Hybrid Dev/Test, IaaS, Information as a Service
Hybrid Cloud Solutions
Standalone Cloud Products
& Services
Common Architectural
Elements
Differentiated & 3rd Party Infrastructure
HP Managed Cloud
HP CIaaS (converged Infrastructure as a service)
Converged Infrastructure
Servers Network Storage
HP Public Cloud
HP CIaaS (converged Infrastructure as a service)
Converged Infrastructure
Servers Network Storage
HP Private Cloud
HP CIaaS (converged Infrastructure as a service)
Converged Infrastructure
Servers Network Storage
Traditional
Traditional Heterogeneous
Hardware & Software
Servers Network Storage
3rd Party Clouds
Autonomy Protect & Promote
Management
Data Services
IaaS …… Dev/
Test
PaaS CRM
IaaS Mgmt Dev/
Test
Analytics Email, Collab, Unified Comms
Security Private Dev/Test
Private IaaS Private Cloud w/ Application
Lifecycle Mgmt Autonomy Protect & Promote
3rd Party
HP Converged Information Idol10 - manage data & metadata, from archive targets
© Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 101
Federation Economics
• Federation can help contain demand overflow within itself
• Cost of outsourcing overflow to public Cloud is higher than to federation of 6 sites
• Cost reduces with size of federation increasing to 50
101
10
100
1000
10000
100000
100% 120% 150% 200% 350% 600% 1200% 2500% 5000% 10000%
Mo
nth
ly C
ost
in
$K
Utilization
Existing DC
Open Cirrus 6
Open Cirrus 50