论文标题
稀疏的抽象机器
The Sparse Abstract Machine
论文作者
论文摘要
我们提出了稀疏的抽象机（SAM），这是一种用于靶向稀疏张量代数来重新配置和固定功能空间数据流加速器的抽象机器模型。 SAM用稀疏的原始剂定义了流数据流的抽象，这些原始图包括大量的计划张量代数表达式。 SAM DataFlage自然将张量格式与算法分开，并且表现力足以合并任意迭代顺序和许多特定于硬件的优化。我们还提出了从高级语言到山姆的编译器Custard，展示了Sam作为中间表示的有用性。我们自动从SAM绑定到流数据流模拟器。我们评估了SAM的通用性和可扩展性，探索使用SAM稀疏张量代数优化的性能空间，并展示SAM代表数据流硬件的能力。
We propose the Sparse Abstract Machine (SAM), an abstract machine model for targeting sparse tensor algebra to reconfigurable and fixed-function spatial dataflow accelerators. SAM defines a streaming dataflow abstraction with sparse primitives that encompass a large space of scheduled tensor algebra expressions. SAM dataflow graphs naturally separate tensor formats from algorithms and are expressive enough to incorporate arbitrary iteration orderings and many hardware-specific optimizations. We also present Custard, a compiler from a high-level language to SAM that demonstrates SAM's usefulness as an intermediate representation. We automatically bind from SAM to a streaming dataflow simulator. We evaluate the generality and extensibility of SAM, explore the performance space of sparse tensor algebra optimizations using SAM, and show SAM's ability to represent dataflow hardware.
