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Exascale computer architecture adjusting to the “New normal” for computing

机译：万亿级计算机体系结构适应计算的“新常态”

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The current MPI+Fortran ecosystem has sustained HPC application software development for the past decade, but was architected for coarse-grained concurrency largely dominated by bulk-synchronous algorithms. The trends in computer architecture have turned our model for how to get good performance from computing systems upside-down, and will require rethinking our entire programming environment and algorithm design to be better aligned with the new cost metrics for these emerging hardware architectures. There are already promising avenues of exploration underway to mitigate these effects. Future hardware constraints on bandwidth and memory capacity, together with exponential growth in explicit on-chip parallelism as shown in Figure 1 will likely require a mass migration to new algorithms and software architecture that is as broad and disruptive as the migration from vector to parallel computing systems that occurred 15 years go. The challenge is to efficiently express massive parallelism and hierarchical data locality without subjecting the programmer to overwhelming complexity.
机译：当前的MPI + Fortran生态系统在过去的十年中一直在支持HPC应用程序软件的开发，但其设计是为粗粒度并发设计的，该并发主要由批量同步算法控制。计算机体系结构的发展趋势已经使我们的模型颠倒了如何从计算系统中获得良好的性能，并且将需要重新考虑我们的整个编程环境和算法设计，以更好地与这些新兴硬件体系结构的新成本指标保持一致。已经有一些有希望的探索途径可以减轻这些影响。未来对带宽和内存容量的硬件限制以及显式的片上并行性的指数增长，如图1所示，可能需要大规模迁移到新算法和软件体系结构，这种新算法和软件体系结构与从向量到并行计算的迁移一样广泛且具有破坏性。发生了15年的系统。面临的挑战是如何有效地表达大量的并行性和分层数据局部性，而又不会使程序员感到压倒性的复杂性。

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