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 Modeling of complex, large, potentially heterogeneous computer systems and applications Methodology development Architecture and application complexity Accuracy Concurrency Dynamic/runtime performance model Enable model-driven design and implementation of software Enable model-based steering Better informed, lower risk procurements Better application / architecture mappings Higher sustained performance Summary of research direction Potential impact on software component Potential impact on usability, capability, and breadth of community

Priority Research Direction (Performance Measurement and Analysis) Key challenges 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 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 Summary of research direction Potential impact on software component Potential impact on usability, capability, and breadth of community

Priority Research Direction (Autotuning) Key challenges 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 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 Summary of research direction Potential impact on software component Potential impact on usability, capability, and breadth of community

4.x Performance Performance modeling, simulation, measurement and analysis Handle heterogeneous HW Support for hybrid programming models Predictive exascale system design Handle Billon-way concurrency Processing Rate Characterize performance of exascale HW + SW for app enablement Handle millon-way concurrency Handle 300 millon-way concurrency

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)