TensorFlow作为DSL用于Cerebras晶圆级引擎上的基于模板的计算
TensorFlow as a DSL for stencil-based computation on the Cerebras Wafer Scale Engine
摘要：
Cerebras Wafer Scale Engine（WSE）是一个加速器，它将数十万个AI核心结合到单个芯片上。虽然这项技术是为机器学习工作负载而设计的，但大量可用的原始计算意味着它也是加速传统HPC计算代码的一个非常有趣的潜在目标。这些算法中的许多是基于模板的，其中更新操作涉及来自相邻元素的贡献，在本文中，我们从技术早期采用者的角度探讨了这种技术对此类代码的适用性，与CPU和GPU相比。使用TensorFlow作为接口，我们探索了性能，并证明了尽管在向用户公开编程接口方面仍有工作要做，但WSE的性能令人印象深刻，因为在我们的实验中，它的性能是四个V100 GPU的2.5倍，两个英特尔至强白金CPU的114倍。因此，该技术在加速未来亿亿次超级计算机上的HPC代码方面发挥重要作用的潜力巨大。
英文摘要：
The Cerebras Wafer Scale Engine (WSE) is an accelerator that combines hundreds of thousands of AI-cores onto a single chip. Whilst this technology has been designed for machine learning workloads, the significant amount of available raw compute means that it is also a very interesting potential target for accelerating traditional HPC computational codes. Many of these algorithms are stencil-based, where update operations involve contributions from neighbouring elements, and in this paper we explore the suitability of this technology for such codes from the perspective of an early adopter of the technology, compared to CPUs and GPUs. Using TensorFlow as the interface, we explore the performance and demonstrate that, whilst there is still work to be done around exposing the programming interface to users, performance of the WSE is impressive as it out performs four V100 GPUs by two and a half times and two Intel Xeon Platinum CPUs by around 114 times in our experiments. There is significant potential therefore for this technology to play an important role in accelerating HPC codes on future exascale supercomputers.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
