1. My Research Experiences on Computer Performance Optimization
Shih-Hao Hung, Ph.D.
Sun Microsystems Inc.
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2. Confidential - Do Not Forward
Computer Performance
Demands for high performance:
Getting jobs done faster
Getting more jobs done at the same time
Getting complex jobs done in time
Price-performance trade-offs:
Getting jobs done efficiently
Getting jobs done with limited resources
Capacity planning
2018/11/20
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3. Performance Optimization
2018/11/20
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4. My Research Background
High-Performance/Technical Computing Parallel Performance Project University of Michigan,
Parallelization for high-performance applications
Performance characterization tools
Performance optimization methodologies
Commercial Applications Optimization Performance and Availability Engineering Sun Microsystems, Inc., 2000-present
Database server optimization
Network stack optimization
Webserver optimization
Security Infrastructure optimization
2018/11/20
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5. Parallel Performance Project
Started by Prof. Edward S. Davidson of U of Michigan in 1990, funded by NSF, Ford Motor Co., UM Center of Parallel Computing, IBM, DoD, etc.
Produced 11 Ph.D.’s in 10 years.
Work on state-of-the-art parallel supercomputers and realistic applications
Covers many aspects of computer architecture, from CPU pipelines to clustered systems.
Optimization by all means: instruction scheduling, memory locality, parallelization, etc. via compiler techniques and hand-tuning.
2018/11/20
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6. Confidential - Do Not Forward
Parallel Computing
Very hot in the 90’s:
People rushed to build large MPP’s.
Looks good in theory, but lack of practical tools and experiences.
Most existing apps are difficult to parallelize.
Failed to race with Moore’s Law. R&D cycle too expensive and too long to catch up with increase of CPU Mhz-Ghz.
Looking ahead:
Throughput computing and commercial workload drive MP.
Chip density and area favors SMT & CMP designs.
Struggling to find ways to keep the same growth of Ghz.
Multiple-core processors, multiple-processors systems are becoming the norm in the coming years.
2018/11/20
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7. Optimizing Parallel Applications
Very complex, difficult problems:
Program parallelization
Load balance
Scheduling
Minimize interprocessor communications
Architecture-dependent optimization
Today:
Still lots of open problems.
Parallelizing compilers are far from automatic solutions.
Tomorrow:
Further research and practical solutions will be in high demand as MP systems becomes popular at all levels.
2018/11/20
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8. Hierarchical Performance Bounds
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9. Confidential - Do Not Forward
Example: FCRASH
Vehicle crash simulation at Ford.
Finite-element code contains over 10,000 Fortran lines and 14 parallel loops.
Profiled on a NUMA system (HP/Convex SPP-1000).
P-gap: imperfect parallelization
C’-gap: inter-cluster communications
L & M’-gaps: Load balancing issues
2018/11/20
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10. Goal-directed Optimization
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Performance Tuning
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12. Modeling a Parallel Application
2018/11/20
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13. Model-Driven Simulation
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14. Performance Tuning Results
SP - initial parallel version
SD - changing domain decomposition to reduce load imbalance (L-gap) and communications (C’-gap)
SD2 - SD + array padding to reduce false-sharing communications (Unmodeled-gap)
SD3 - SD2 + eliminating thread migration to reduce communications (Unmodeled-gap)
SD4 - SD3 + eliminating unnecessary synchronization barriers (S’-gap)
2018/11/20
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15. Confidential - Do Not Forward
Sun Microsystems
Proud of visions and innovative technologies.
Face fierce competitions in the server business
OS: Microsoft, Linux
CPU: Intel, IBM
High-end market: IBM, HP
Low-end market: Dell and other x86 vendors
Still going for the next big thing
Network computing (Java, JDS, JES, GridEngine)
Throughput computing (Niagara 1 & 2, Rock)
Solaris 10 & x86 support
2018/11/20
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16. Performance Engineering
Performance problems everywhere…
Deal with important commercial applications:
Database
Network infrastructure & applications
Throughput computing
Security Infrastructure
Solve problems by:
Identifying issues
Improving products
Influencing future development
2018/11/20
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17. Networking Infrastructure
Gigabit Ethernet driver optimization
TCP/IP stack optimization
Multi-data transmission and Jumbo Frames
TCP Offloading Engine (TOE)
Infiniband vs 10GE
On-chip high-speed Ethernet support
2018/11/20
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18. Networking Applications
Optimizing SunOne servers
Webserver
Directory server
Application server
Portal server
Tweaking benchmarks
SPECweb99 & 2004
SPECweb99_SSL
TPC-W (W = Web commerce)
2018/11/20
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19. Security Infrastructure
Crypto accelerators
On-chip crypto support
Secure Socket Layer (SSL) & HTTPS acceleration
IPsec & VPN acceleration
Crypto optimization
Solaris Cryptographic Framework
2018/11/20
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Crypto Acceleration
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HTTP/SSL Performance
HTTP, 100% Keep Alive
HTTP, 0% Keep Alive
HTTPS, 100% Keep Alive, no encryption, SHA1 hashing
HTTPS, 100% Keep Alive, RC4 encryption, SHA1hashing
HTTPS, 0% Keep Alive, 100% session creation (RSA), RC4, SHA1
HTTPS, 0% Keep Alive, 100% session resumption (RSA-reuse), RC4, SHA1
http
tcp
sha1
rc4
rsa_reuse
rsa
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IPsec Performance
2018/11/20
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24. Solaris Cryptographic Framework
2018/11/20
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25. Throughput Computing - Niagara
2018/11/20
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26. Niagara-2 4-Core Server Competition – Nov. 2007
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Rock
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Conclusion
Will see radical changes in computer systems in the near future, and system-wide hardware-software co-optimization is key to unleash their potentials.
High density chips
Multi-core CPUs
Highly scalable systems
Enormous network & I/O capacity
Built-in security support
Performance is an expertise that is best acquired from experiences.
Methodology and collaboration are our formulas for success.
2018/11/20
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