Programmable hardware accelerators (e.g., vector processors, GPUs) have been extremely successful at targeting algorithms with regular control and memory patterns to achieve order-of-magnitude performance and energy efficiency improvements. However, they perform far under the peak on important irregular algorithms, like those from graph processing, database querying, genomics, advanced machine learning, and others. This work posits that the primary culprit is specific forms of irregular control flow and memory access. By capturing the problematic behavior at a domain-agnostic level, we propose an accelerator that is sufficiently general, matches domain-specific accelerator performance, and significantly outperforms traditional CPUs and GPUs.

中文翻译：

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迈向通用加速：在不规则中寻找结构

可编程硬件加速器（例如，矢量处理器、GPU）在针对具有规则控制和内存模式的算法以实现数量级的性能和能效改进方面非常成功。然而，它们在重要的不规则算法上的表现远远低于峰值，例如图形处理、数据库查询、基因组学、高级机器学习等。这项工作假定主要罪魁祸首是不规则控制流和内存访问的特定形式。通过在域无关级别捕获有问题的行为，我们提出了一种足够通用的加速器，该加速器与特定领域的加速器性能相匹配，并且显着优于传统的 CPU 和 GPU。