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Science in the Clouds and Beyond
Lavanya Ramakrishnan
Computational Research Division (CRD) &
National Energy Research Scientific Computing Center
(NERSC) Lawrence Berkeley National Lab
The goal of Magellan was to determine the appropriate role for cloud computing for science
Program Office
Advanced Scientific Computing Research 17%
Biological and Environmental Research 9%
Basic Energy Sciences -Chemical Sciences 10%
Fusion Energy Sciences 10%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90%
Access to additional resources
Access to on-demand (commercial) paid resources closer to deadlines
Ability to control software environments specific to my application
Ability to share setup of software or experiments with collaborators
Ability to control groups/users
Exclusive access to the computing resources/ability to schedule independently of other groups/users
Easier to acquire/operate than a local cluster
Cost associativity? (i.e., I can get 10 cpus for 1 hr now or 2 cpus for 5 hrs at the same cost)
MapReduce Programming Model/Hadoop
Hadoop File System
User interfaces/Science Gateways: Use of clouds to host science gateways and/or access to cloud resources through science
gateways
Program Office
High Energy Physics 20%
Nuclear Physics 13%
Advanced Networking Initiative (ANI) Project 3%
Other 14%
Magellan was architected for flexibility and to support research
3 3
QD
R InfiniB
and
Mgt Nodes (2)
Flash Storage Servers 10 Compute/Storage Nodes 8TB High-Performance FLASH 20 GB/s Bandwidth
Gateway Nodes (27)
Compute Servers 720Compute Servers Nehalem Dual quad-core 2.66GHz 24GB RAM, 500GB Disk Totals 5760 Cores, 40TF Peak 21TB Memory, 400 TB Disk
Global Storage (GPFS) 1 PB
ESNet 10Gb/s
Big Memory Servers 2 Servers 2TB Memory
ANI 100 Gb/s Future
Aggregation Sw
itch
Router
IO Nodes (9)
Archival Storage
Science + Clouds = ?
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Data Intensive Science Technologies from Cloud
Business model for Science
Performance and Cost
Scientific applications with minimal communication are best suited for clouds
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GAMESS GTC IMPACT fvCAM MAESTRO256
Run
time
Rel
ativ
e to
Mag
ella
n (n
on-
VM)
Carver
Franklin
Lawrencium
EC2-Beta-Opt
Amazon EC2
Amazon CC
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Performance Analysis of High Performance Computing Applications on the Amazon Web Services Cloud, CloudCom 2010
Better
Scientific applications with minimal communication are best suited for clouds
6 6
Better
0
10
20
30
40
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60
MILC PARATEC
Run
time
rela
tive
to M
agel
lan
Carver Franklin Lawrencium EC2-Beta-Opt Amazon EC2
The principle decrease in bandwidth occurs when switching to TCP over IB.
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0
0.5
1
1.5
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32 64 128 256 512 1024
Ping
Pong
Ban
dwid
th(G
B/s
)
Number of Cores
IB TCPoIB 10G - TCPoEth Amazon CC
Better
BEST 2X
5X
HPCC PingPong BW Evaluating Interconnect and Virtualization Performance for High Performance Computing, ACM Perf Review 2012
Ethernet connections are unable to cope with significant amounts of network connection
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0
0.05
0.1
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0.25
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0.35
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0.45
32 64 128 256 512 1024
Ran
dom
Ord
er B
andw
idth
(GB
/s)
Number of Cores
IB TCPoIB 10G - TCPoEth Amazon CC 10G- TCPoEth VM 1G-TCPoEth
Better
BEST
The principle increase in latency occurs for TCP over IB even at mid-range concurrency
9
0
50
100
150
200
250
32 64 128 256 512 1024
Ping
Pong
Lat
ency
(us)
Number of Cores
IB TCPoIB 10G - TCPoEth Amazon CC 10G- TCPoEth VM 1G-TCPoEth
Better
BEST
40X
HPCC: PingPong Latency
0
50
100
150
200
250
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500
32 64 128 256 512 1024
Ran
dom
Ord
er L
aten
cy (u
s)
Number of Cores
IB TCPoIB 10G - TCPoEth Amazon CC 10G- TCPoEth VM 1G-TCPoEth
Latency is affected by contention by a greater amount than the bandwidth
10
Better
BEST
10G VM shows 6X
HPCC: RandomRing Latency
Clouds require significant programming and system administration support
11
• STAR performed Real-time analysis of data coming from Brookhaven Nat. Lab
• First time data was analyzed in real-time to a high degree
• Leveraged existing OS image from NERSC system
• Started out with 20 VMs at NERSC and expanded to ANL.
On-demand access for scientific applications might be difficult if not impossible
Number of cores required to run a job immediately upon submission to Franklin
12
Public clouds can be more expensive than in-house large systems
13
Component Cost
Compute Systems (1.38B hours) $180,900,000
HPSS (17 PB) $12,200,000
File Systems (2 PB) $2,500,000
Total (Annual Cost) $195,600,000
Assumes 85% utilization and zero growth in HPSS and File System data. Doesn’t include the 2x-10x performance impact that has been measured.
This still only captures about 65% of NERSC’s $55M annual budget. No consulting staff, no administration, no support.
Cloud is a business model and can be applied to HPC centers
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Traditional Enterprise IT HPC Centers
Typical Load Average 30% * 90%
Computational Needs Bounded computing requirements – Sufficient to meet customer demand or transaction rates.
Virtually unbounded requirements – Scientist always have larger, more complicated problems to simulate or analyze.
Scaling Approach Scale-in. Emphasis on consolidating in a node using virtualization
Scale-Out Applications run in parallel across multiple nodes.
Cloud HPC Centers
NIST Definition Resource Pooling, Broad network access, measured service, rapid elasticity, on-demand self service
Resource Pooling, Broad network access, measured service. Limited: rapid elasticity, on-demand self service
Workloads High throughput modest data workloads
High Synchronous large concurrencies parallel codes with significant I/O and communication
Software Stack Flexible user managed custom software stacks
Access to parallel file systems and low-latency high bandwidth interconnect. Preinstalled, pre-tuned application software stacks for performance
Science + Clouds = ?
15
Data Intensive Science Technologies from Cloud
Business model for Science
Performance and Cost
MapReduce shows promise but current implementations have gaps for scientific applications
16
High throughput workflows
Scaling up from desktops
File system: non POSIX Language: Java Input and output formats: mostly line-oriented text Streaming mode: restrictive i/p and o/p model Data locality: what happens when multiple inputs?
Streaming adds a performance overhead
17
Better
Evaluating Hadoop for Science, In submission
High performance file systems can be used with MapReduce at lower concurrency
0
2
4
6
8
10
12
0 500 1000 1500 2000 2500 3000
Tim
e (m
inut
es)
Number of maps
Teragen (1TB) HDFS
GPFS
Linear(HDFS) Expon.(HDFS) Linear(GPFS) Expon.(GPFS)
18
Better
Data operations impacts the performance differences
19
Better
Schemaless databases show promise for scientific applications
20
Schemaless database
manager.x manager.x manager.x
Brain
www.materialsproject.org Source: Michael Kocher, Daniel Gunter
Data centric infrastructure will need to evolve to handle large scientific data volumes
21
Joint Genome Institute, Advance Light Source, etc are all facing a data tsunami
Cloud is a business model and can be applied at DOE supercomputing centers
• Current day cloud computing solutions have gaps for science – performance, reliability, stability – programming models are difficult for legacy apps – security mechanisms and policies
• HPC centers can adopt some of the technologies and mechanisms – support for data-intensive workloads – allow custom software environments – provide different levels of service
22
Acknowledgements
• US Department of Energy DE-AC02-05CH11232 • Magellan
– Shane Canon, Tina Declerck, Iwona Sakrejda, Scott Campbell, Brent Draney • Amazon Benchmarking
– Krishna Muriki, Nick Wright, John Shalf, Keith Jackson, Harvey Wasserman, Shreyas Cholia
• Magellan/ANL – Susan Coghlan, Piotr T Zbiegiel, Narayan Desai, Rick Bradshaw, Anping
Liu • NERSC
– Jeff Broughton, Kathy Yelick • Applications
– Jared Wilkening, Gabe West, Ed Holohan, Doug Olson, Jan Balewski, STAR collaboration, K. John Wu, Alex Sim, Prabhat, Suren Byna, Victor Markowitz
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