Download Performance

4.x Performance
• Technology drivers
– Exascale systems will consist of complex
configurations with a huge number of potentially
heterogeneous components
– Deep software hierarchies of large, complex
software components will be required to make
use of such systems
– Sophisticated integrated performance
measurement, analysis, and optimization
capabilities will be required to efficiently operate
an exascale system
4.x Performance
• Alternative R&D strategies
– Performance-aware design and implementation
– Stronger emphasis on modeling and auto-tuning
– Self-optimizing frameworks and runtime systems
– Optimization for power or resiliency
Priority Research Direction (Performance Modeling)
Key challenges
• Architecture and application complexity
• Accuracy
• Concurrency
• Dynamic/runtime performance model
Potential impact on software component
• Enable model-driven design and
implementation of software
• Enable model-based steering
Summary of research direction
• Modeling of complex, large, potentially
heterogeneous computer systems and
applications
• Methodology development
Potential impact on usability, capability,
and breadth of community
• Better informed, lower risk procurements
• Better application / architecture mappings
• Higher sustained performance
Priority Research Direction (Performance Measurement and Analysis)
Key challenges
• Perturbation and data volume
• Concurrency
• Heterogeneity
• Drawing insight from measurements
• Quality information sources
Potential impact on software component
• More scalable, capable, easier-to-use tool
environments
• Improved interoperability and standards
• More modular and reusable tools
Summary of research direction
• Develop scalable collection (online reduction and filtering, clustering), analysis
(clustering, data mining), and visualization (hierarchical)
• Support for heterogeneous hardware and hybrid programming models
• Automated / automatic diagnosis
• Vertical integration across software layers (OS, compilers, runtime systems,
middleware, application)
• Performance analysis in presence of noise and faults
• Performance optimization for other metrics than time (e.g. power and resiliency)
• Engage vendors to improve performance information streams
Potential impact on usability, capability,
and breadth of community
• Higher sustained performance
• Boosting value of HPC investments
• Increase scientific productivity
Priority Research Direction (Autotuning)
Key challenges
• Wider applicability
• Impractical search spaces
• Dynamic adaptation
• Heterogeneity
Potential impact on software component
• Common frameworks for autotuning speeds
adoption and progress by application software
Summary of research direction
• Methodology development for runtime adaptivity
•Common methods and harnesses for implementing
autotuning
• Coordination of heterogeneous resources by OS
• Using parallelization of performance experiments
to speed searches
Potential impact on usability, capability,
and breadth of community
• Increase the value of investments in HPC by
keeping performance closer to optimality
• Lowered costs for performance engineering
done automatically in the field rather than by
specialists
4.x Performance
Performance modeling, simulation,
measurement and analysis
Handle
Billon-way
concurrency
Characterize performance of
exascale HW + SW for app enablement
Processing Rate
Support for hybrid
programming models
Handle
millon-way
concurrency
Handle
300 millon-way
concurrency
Predictive exascale
system design
Handle
heterogeneous HW
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4.x Performance
• Recommended research agenda
– Develop scalable performance measurement collection (online
reduction and filtering, clustering), analysis (clustering, data mining),
and visualization (hierarchical)
– Support for heterogeneous hardware and hybrid programming models
– Automated / automatic diagnosis / autotuning
– Vertical integration across software layers (OS, compilers, runtime
systems, middleware, application)
– Performance analysis in presence of noise and faults
– Performance optimization for other metrics than time (e.g. power)
– Engage vendors to improve performance information streams
4.x Performance
• Crosscutting considerations
– Performance-aware design, development and
deployment of hard- and software
– Integration with OS, compilers and runtime
systems
– Support for performance observability in HW and
SW (runtime)