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4.x 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

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4.x Performance

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  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 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

  3. Priority Research Direction (Performance Modeling) Key challenges Summary of research direction • Architecture and application complexity • Accuracy • Concurrency • Dynamic/runtime performance model • Modeling of complex, large, potentially heterogeneous computer systems and applications • Methodology development 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 • Perturbation and data volume • Concurrency • Heterogeneity • Drawing insight from measurements • Quality information sources • 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 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 • Wider applicability • Impractical search spaces • Dynamic adaptation • Heterogeneity • 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 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 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019

  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)

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