EAGER: Assessment of the Numerical Reproducibility in Large-Scale Scientific Simulations on Multicore Architectures

执行并发性的趋势为开发能够自动有效地建模和减轻千万亿次架构之外的不可再现性并进入亿亿次的方法提供了一个令人信服的案例。预计艾级的高性能计算机将表现出巨大的并发水平-比当前平台大10，000倍-这将使计算机模拟从批量同步执行转向多线程方法和异步I/O。模拟计算和分析例程也将在亿级平台上紧密耦合，这需要这两个工作流组件以极高的并发水平工作。随着并发级别的增加，舍入误差对数值再现性的影响也会增加，最终影响科学模拟再现程序执行和数值结果的能力。在这种情况下，不可重现的结果可能不被期望可重现行为的科学界所信任，任何追求可重现性的尝试都可能以过高的性能为代价。这个“高风险-高回报”项目研究了在尖端多核架构中，当并发执行突发和工作流确定性消失时，舍入误差对结果可重现性的影响。为此，该项目采用一种称为“复合精度浮点运算”的数学方法对科学应用中的舍入误差进行建模，并展示了这种方法如何减轻误差漂移。 在前期工作中使用的基准测试套件进行了扩展，以涵盖更大范围的应用程序的模式，并用于评估新一代多核架构的复合精度的缓解影响。最后，该项目量化了所提出的方法的成本和缓解因素，以缓解不同基准和平台的误差传播。该项目将通过开发和推广有效的软件解决方案，为广泛的应用程序及其代码在大规模并行环境下以高度并发执行时的舍入误差传播问题，提高对艾级数值再现性的认识和理解系统.

Trends in execution concurrency make a compelling case for the development of methods able to automatically and efficiently model and mitigate irreproducibility beyond petascale architectures and into the exascale. It is expected that high performance computers at the exascale will exhibit a massively large level of concurrency - a factor of 10,000 greater than on current platforms - which will move computer simulations from bulk-synchronous executions to multithreading approaches and asynchronous I/O. Simulation calculations and analysis routines will also be tightly coupled on exascale platforms, requiring these two workflow components to work at extremely high levels of concurrency. As concurrency levels increase, the impact of rounding errors on numerical reproducibility also increases, ultimately affecting the ability of scientific simulations to reproduce program executions and numerical results. Under these circumstances, irreproducible results may not be trusted by a scientific community expecting reproducible behaviors and any attempt to pursue reproducibility may come at a cost in performance that is too high.This "high risk-high payoff" project studies the impact of rounding errors on result reproducibility when concurrent executions burst and workflow determinism vanishes in cutting-edge multicore architectures. To this end, the project models rounding-errors in scientific applications with a mathematical method called "composite precision floating-point arithmetic" and shows how this method can mitigate error drifting. A benchmark suite used in preliminary work is extended to cover a larger range of applications' patterns and used to assess the mitigating impact of the composite precision on new generations of multicore architectures. Lastly, the project quantifies the cost and mitigation factors of the proposed method to mitigate error propagations for the diverse benchmarks and platforms.The project will advance knowledge and understanding in numerical reproducibility at the exascale by developing and disseminating effective software solutions to the rounding error propagation problem for a broad set of applications and their codes when executed with high degrees of concurrency on massively parallel systems.