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Crossing the Performance Chasm with
OpenPOWER
Dr. Srini Chari
Cabot Partners/IBM
[email protected]
#OpenPOWERSummit
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Disclosure
Copyright® 2015. Cabot Partners Group. Inc. All rights reserved. Other companies’ product names,
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Please read accompanying Cabot Partners Whitepaper for Additional Detail,
References and Notes on information presented in this document.
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Agenda
 Key Technology Trends
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•
Cloud, HPC, Analytics, Social, Mobile and Internet of Things
Centered Around Data
 Open Innovation Vital for Value Creation
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Data-Centric HPC Growing Rapidly
 Client Considerations in HPC Systems Evaluations
 System Attributes Impacting Real Life Performance
•
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Why the LINPACK Benchmark is Inadequate
IBM Data Centric Approach and Solutions
Why OpenPOWER
Examples of Performance Gains
Key Takeaways
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Key Intertwined Technology Trends
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Key Intertwined Technology Trends
 Cloud, High Performance Computing, Analytics,
Social, Mobile and IoT
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Enterprise cloud growth - $70B (2014) to $250B (2017)
Annual growth: Smart phones 20% . Mobile data 81%
IoT at 12B today reaching over 1 Trillion in a decade
Social media users - 1.79B (2014) to 2.44B (2018)
 DATA
•
•
2.5 exabytes (1018 bytes) created daily. Individuals create
70% and enterprises manage 80%
Annual spending 30% to reach $114B in 2018
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What IT Must Consider to Deal with Data
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
Volume
Variety
Velocity
Veracity
Vulnerability
Visualize
Virtualize
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V
 Value
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Extracting Value From Data with HPC
Requires Open Innovation across the stack
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HPC Drives Value Across Many Industries
 Overall HPC servers growing ~ 6.4% annually
 Traditional HPC
Risk Analytics
Life Sciences
Oil and Gas
 Data-Centric HPC growing ~ 23.5%
Emergency Response
Fraud and Threat Detection
Enhance Customer Experience
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Considerations to Evaluate HPC Systems
 Not Just Point Benchmarks
 But Workflows Across the HPC Data Life Cycle
 Example in Seismic Processing
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Total Value of Ownership Framework
Holistic Cost Benefit Analysis for Entire HPC Workflow

Value Delivered
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•
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Business Value: e.g. customer revenues, new business models, compliance
regulations, better products, increased business insight, faster time to market, and
new breakthrough capability
Operational Value: e.g. faster time to results, more accurate analyses, more users
supported, improved user productivity, better capacity planning
IT Value: e.g. improved system utilization, manageability, administration, and
provisioning, scalability, reduced downtime, access to robust proven technology and
expertise.
Costs Incurred
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IT /Data Center Capital e.g. new servers, storage, networks, power distribution units,
chillers, etc.
Data Center Facilities e.g. land, buildings, containers, etc.
Operational Costs: e.g. labor, energy, maintenance, software license, applications, etc.
Other Costs: e.g. system management, deployment and training, downtime,
migration, etc.
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What Impacts Traditional HPC Performance
Life Sciences
Computer Aided Engineering
Structures
Flops/Core
Flops/Core
Crash
Fluids
Network Bandwidth
Cores
Network Bandwidth
0
0
Network Latency
Memory Capacity
I/O Performance
Memory Bandwidth
Financial Services
Flops/Core
Quantum Chemistry
Molecular Modeling
Bioinformatics
Cores
Network Latency
Memory Capacity
I/O Performance
Memory Bandwidth
Energy and Environmental Sciences
Low Latency Trading
Reservoir
Seismic
Weather
Flops/Core
Monte Carlo
Risk Analytics
Network Bandwidth
Cores
Network Bandwidth
0
0
Network Latency
I/O Performance
Cores
Memory Capacity
Memory Bandwidth
Network Latency
I/O Performance
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Memory Capacity
Memory Bandwidth
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Why LINPACK is Inadequate
Most HPC Analytics Involve Sparse Matrices
but LINPACK Solves Dense Matrix Problems
LINPACK vs. HPC Analytics
Flops/Core
LINPACK
HPC Analytics
Network Bandwidth
Cores
0
Network Latency
I/O Performance
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Memory Capacity
Memory Bandwidth
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Data Centric System
Traditional System Design
IBM’s Data Centric Approach and Solutions
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Major HPC Win for OpenPOWER
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Key Benchmarks*: POWER8 2 - 2.5X Better
SPECint_rate2006
(greater is better)
1.8 x Performance
60
70
Performance / Core
Performance / Core
80
60
50
40
30
20
10
0
Dell PowerEdge
T620
2s/36c/72t
Intel Xeon Haswell
POWER S824
2s/24c/192t
IBM POWER8
SPECfp_rate2006
(greater is better)
2.1 x Performance
50
40
30
20
10
0
Dell PowerEdge
T620
2s/36c/72t
Intel Xeon Haswell
POWER S824
2s/24c/192t
IBM POWER8
Terasort Big Data Hadoop
(greater is better)
Stream Triad
(greater is better)
2.9 x Performance
Relative System Performance
350
3.0
300
2.5
250
2.0
200
GB/s
2.5x
1.5
150
100
1.0
50
0.5
0
Intel Xeon Haswell
2s/24c/48t
IBM POWER8
2s/24c/192t
0.0
POWER8
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Cisco
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Application Performance with POWER8* 1.4 – 2.6 X better
Nanoseconds / day
Molecular Dynamics - NAMD apoa1
(greater is better)
1.4 x Performance
3
3
2.5
2.5
2
2
1.5
1.5
1
1
0.5
0.5
0
Intel E5-2690 V3
2s/24c/2.6GHz
Intel Xeon Haswell
POWER S824L
2s/24c/3.6GHz
IBM POWER8
STAC A2 – Options Pricing
(greater is better)
2.07 x Performance
0
Seismic – RTM
(greater is better)
2.6 x Performance
2.6x
Intel E5-2690 V3
2s/24c/2.6GHz
Intel Xeon Haswell
POWER S822L
2s/24c/3.358GHz
IBM POWER8
PostGreSQL
(higher is better)
Max Paths (10 min)
3.00E+07
2.50E+07
2.00E+07
1.50E+07
1.00E+07
5.00E+06
0.00E+00
4 x Intel E7-4890 v2
2.80 GHz /1TB
Intel Ivy Bridge EX
Power S824
2s/24c/3.52GHz/1TB
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Key Takeaways
 HPC is becoming more data-centric
 Traditional system evaluations based on point
benchmarks such as LINPACK are inadequate
 Focus evaluation on cost-benefit analysis of workflow
across HPC data lifecycle
 Many system features impact HPC performance
 Benefits of OpenPOWER HPC Offerings:
•
•
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Deliver Choice and Flexibility
Minimize Costly Data Motion for Entire Workflow
Accelerate Compute and Data Intensive Tasks with Lower TCO
Provide Investment Protection
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*Appendix - Additional Benchmark Detail
SPECcpu (int_rate & fp_rate)
SPECcpu2006 results are based on best published results on E5-2699 v3 from the top 5 Intel system vendors (HP, Oracle, Lenovo, Dell, Fujitsu)
submitted as of 9/8/2014. For more information go to http://www.specbench.org/cpu2006/results/ . The IBM POWER8 published data is
based on Power S824 2s/24c/3.5GHz POWER8. The x86 Xeon published data is based on Dell PowerEdge T620 2s/36c/2.3GHz E5-2699 v3.
Hadoop Tersort
IBM Analytics Stack: IBM Power System S822L; 8 nodes each with 24 cores / 192 threads, POWER8; 3.0GHz, 512 GB memory, RHEL 6.5,
InfoSphere BigInsights 3.0
Cisco Stack: 16 high-density Cisco UCS C240 M3 Rack Servers each with 16 cores / 32 threads, Intel Xeon E5-2665; 2.4 GHz, 256 GB of
memory, Cisco UCS VIC 1225, and LSI 9266 8i with 24 1-TB SATA 7200-rpm disk running Apache Hadoop open source distribution.
Stream Triad
The Stream Triad results are based on results reported in published papers.
IBM POWER8:
http://www.dcs.warwick.ac.uk/~sdh/pmbs14/PMBS14/Workshop_Schedule_files/2-PerformancePower8.pdf
Intel Xeon E5-2600 v3
http://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&ved=0CB8QFjAA&url=http%3A%2F%2Fdownload.boston.co.uk%2Fd
ownloads%2F9%2F3%2Fc%2F93c022fd-0d6d-46a4-9124-28c9e32f2533%2FIntelWhitepaper.pdf&ei=mLgBVbysL8KrggT774CICw&usg=AFQjCNFal5q5Vz2ly6ZbsaKZ2QPPad1fg&sig2=3LzktTXeKPvS2QW9ndXgfQ&bvm=bv.87920726,d.eXY
STAC and all STAC names are trademarks or registered trademarks of Securities Technology Analysis Center LLC.
https://stacresearch.com/system/files/asset/files/STAC-A2%20Intel%20Composer%20on%204%20x%20IVT%20EX%20-%20INTC140509.pdf
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