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4.2.1 Programming Models Technology drivers – Node count, scale of parallelism within the node – Heterogeneity – Complex memory hierarchies – Failure rates.

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Presentation on theme: "4.2.1 Programming Models Technology drivers – Node count, scale of parallelism within the node – Heterogeneity – Complex memory hierarchies – Failure rates."— Presentation transcript:

1 4.2.1 Programming Models Technology drivers – Node count, scale of parallelism within the node – Heterogeneity – Complex memory hierarchies – Failure rates – Adaptive computations, massively parallel I/O – New application domains; new programming languages

2 4.2.1 Programming Models Alternative R&D strategies – Uniform vs. hybrid programming models – MPI 7.0, OpenMP 5.0, or revolutionary approaches – Domain specific vs. general PMs Recommended research agenda – Explore enhancements to existing models – Revolutionary approach with interoperability to existing models

3 4.2.1 Programming Models Crosscutting considerations – Architectures – Algorithms and applications – Libraries – Massively parallel I/O – Compilers and their runtime systems – Tools: performance, debugging, application creation – User annotations: information to support translation, drive tools

4 Priority Research Direction: Programming Models (PM) Key challenges Multiple PMs: uniform, hybrid; high-level; new, PGAS, MPI, OpenMP, other; application-area specific PMs Expressive ways to describe parallelism and locality in PMs Fault tolerance/awareness at PM level Implementation (compiler, runtime) technology Application development tools Extreme scale of parallelism (Exascale and beyond) Diversity, heterogeneity of architectures/hardware Complex memory hierarchy Productivity of the programmer vs. performance Interoperability (with legacy code) Enabler of application development, library creation on new systems Productive programming models are essential for uptake of exascale systems Enhancements to existing models may have impact in a few years Implementation and support tools needed for this to have major Impact Summary of research direction Potential impact on software component Potential impact on usability, capability, and breadth of community

5 4.X Programming Models Interoperability among existing programming models Fault-tolerant MPI Standard programming model for heterogeneous nodes System-wide high-level programming model Exascale programming models implemented Exascale programming model(s) adopted 2010201120122013201420152016201720182019 Your Metric Candidate exascale programming models defined

6 Priority Research Direction: Compilers Key challenges Translate new languages Powerful optimization frameworks; new optimizations, including power Automatic parallelization Enable interactions between compiler and development and execution environment; standard interfaces Dynamic (re-)optimization; support for autotuning New programming languages, Interoperability (with old legacy code) Productivity and performance Hardware: heterogeneity, complex memory hierarchy, hardware faults Adaptive extreme-scale parallelism Higher level of automation will reduce program development effort and help exploit architecture Adaptive applications can be efficiently run Compiler can make tools more powerful; tools may improve compiler translation Some impact in near term as compiler-tools interactions are defined; continuous improvements in technology are possible Summary of research direction Potential impact on software component Potential impact on usability, capability, and breadth of community

7 4.x Compilers Compiler support for hybrid programming model Compiler supports MPI implementation Standard heterogeneous programming model implemented Initial compiler – tools interfaces defined 2010201120122013201420152016201720182019 Your Metric Exascale programming models implemented System-wide high-level programming model implementation Exascale programming environment deployted

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