Graph algorithms are critical components of the big-data analysis workflow. The graph kernel performance is highly dependent on the input data graphs. The inherently sparse nature of the input graphs often results in irregular memory access patterns, which may not suit the data-locality based cache optimizations featured in current high-performance processors. Much prior research has identified several optimization opportunities by characterizing graph kernels on existing hardware. However, current graph workload characterization studies focus on performance-related observations and optimizations, overlooking the energy implications. In this paper, we address this technology gap by presenting an exhaustive and systematic energy characterization study of graph kernels. We characterize the six GAP benchmark suite kernels with a variety of input graphs on a dual-socket x86-based system. We then analyze how the algorithms, graph characteristics (like graph scale and degree), and system effects (like parallelism, simultaneous multithreading, and multiprocessing) impact the energy. Based on our analysis, we derive observations and insights to develop a basic energy model. The discussions in the paper can enable researchers to advance new models and energy-efficient architectures for graph workloads.

图算法是大数据分析工作流程的关键组成部分。图形内核性能高度依赖于输入数据图形。输入图的固有稀疏性质通常会导致不规则的内存访问模式，这可能不适合当前高性能处理器中基于数据局部性的缓存优化。通过对现有硬件上的图形内核进行特征分析，许多先前的研究已经确定了几种优化机会。但是，当前的图形工作负载表征研究集中在与性能相关的观察和优化上，而忽略了能量的影响。在本文中，我们通过对图形内核进行详尽而系统的能量表征研究来解决这一技术差距。我们在基于x86的双插槽系统上，通过各种输入图来表征六个GAP基准套件内核。然后，我们分析算法，图形特征（如图形比例和度）和系统效果（如并行性，同时多线程和多处理）如何影响能量。基于我们的分析，我们得出观察和见解以开发基本的能源模型。本文中的讨论可以使研究人员能够为图形工作负载开发新的模型和节能架构。