4.x Performance

1
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

2
4.x Performance

• Alternative RD strategies
• Performance-aware design and implementation
• Stronger emphasis on modeling and auto-tuning
• Self-optimizing frameworks and runtime systems
• Optimization for power or resiliency

3
Priority Research Direction (Performance Modeling)
Key challenges
Summary of research direction

• Modeling of complex, large, potentially
  heterogeneous computer systems and applications
• Methodology development

• Architecture and application complexity
• Accuracy
• Concurrency
• Dynamic/runtime performance model

Potential impact on software component
Potential impact on usability, capability, and
breadth of community

• Enable model-driven design and implementation of
  software
• Enable model-based steering

• Better informed, lower risk procurements
• Better application / architecture mappings
• Higher sustained performance

4
Priority Research Direction (Performance
Measurement and Analysis)
Key challenges
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

• Perturbation and data volume
• Concurrency
• Heterogeneity
• Drawing insight from measurements
• Quality information sources

Potential impact on software component
Potential impact on usability, capability, and
breadth of community

• More scalable, capable, easier-to-use tool
  environments
• Improved interoperability and standards
• More modular and reusable tools

• Higher sustained performance
• Boosting value of HPC investments
• Increase scientific productivity

5
Priority Research Direction (Autotuning)
Key challenges
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

• Wider applicability
• Impractical search spaces
• Dynamic adaptation
• Heterogeneity

Potential impact on software component
Potential impact on usability, capability, and
breadth of community

• Common frameworks for autotuning speeds adoption
  and progress by application software

• 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

6
4.x Performance
Performance modeling, simulation,measurement and
analysis
Handle Billon-way concurrency
Characterize performance of exascale HW SW for
app enablement
Handle millon-way concurrency
Handle 300 millon-way concurrency
Processing Rate
Support for hybridprogramming models
Predictive exascalesystem design
Handleheterogeneous HW
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7
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

8
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)