The ubiquity of accelerators in high-performance computing has driven programming complexity beyond the skill-set of the average domain scientist. To maintain performance portability in the future, it is imperative to decouple architecture-specific programming paradigms from the underlying scientific computations. We present the Stateful DataFlow multiGraph (SDFG), a data-centric intermediate representation that enables separating program definition from its optimization. By combining fine-grained data dependencies with high-level control-flow, SDFGs are both expressive and amenable to program transformations, such as tiling and double-buffering. These transformations are applied to the SDFG in an interactive process, using extensible pattern matching, graph rewriting, and a graphical user interface. We demonstrate SDFGs on CPUs, GPUs, and FPGAs over various motifs --- from fundamental computational kernels to graph analytics. We show that SDFGs deliver competitive performance, allowing domain scientists to develop applications naturally and port them to approach peak hardware performance without modifying the original scientific code.

中文翻译：

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有状态数据流多图：一种以数据为中心的异构架构性能可移植性模型

高性能计算中无处不在的加速器推动了编程复杂性超出了普通领域科学家的技能组合。为了在未来保持性能可移植性，必须将特定于架构的编程范式与底层科学计算分离。我们提出了有状态数据流多图 (SDFG)，这是一种以数据为中心的中间表示，可以将程序定义与其优化分开。通过将细粒度数据依赖与高级控制流相结合，SDFG 既具有表现力，又适合程序转换，例如平铺和双缓冲。这些转换在交互过程中应用到 SDFG，使用可扩展模式匹配、图形重写和图形用户界面。我们在 CPU、GPU、和基于各种主题的 FPGA——从基本的计算内核到图分析。我们展示了 SDFG 提供了具有竞争力的性能，允许领域科学家自然地开发应用程序并将它们移植到接近峰值硬件性能，而无需修改原始科学代码。