Enabling efficient graph computing with near-data processing techniques

Processing-in-memory;Graph computing;Near-data processing

Nai, Lifeng ; Qureshi, Moinuddin K. Electrical and Computer Engineering Kim, Hyesoon Vuduc, Richard Pande, Santosh Lim, Sung Kyu ; Qureshi, Moinuddin K.

University:Georgia Institute of Technology

Department:Electrical and Computer Engineering

关键词: Processing-in-memory;    Graph computing;    Near-data processing;   

Others  :  https://smartech.gatech.edu/bitstream/1853/59789/1/NAI-DISSERTATION-2017.pdf

美国|英语

来源: SMARTech Repository

【 摘 要 】

With the emergence of data science, graph computing is becoming a crucial tool for processing big connected data. However, when mapped to modern computing systems, graph computing typically suffers from poor performance because of inefficiencies in memory subsystems. At the same time, emerging technologies, such as Hybrid Memory Cube (HMC), enable processing-in-memory (PIM) functionality, a promising technique of near-data processing (NDP), by integrating compute units in the 3D-stacked logic layer. The PIM units allows operation offloading at an instruction level, which has considerable potential to overcome the performance bottleneck of graph computing. Nevertheless, studies have not fully explored this functionality for graph workloads or identified its applications and shortcomings. The main objective of this dissertation is to enable NDP techniques for efficient graph computing. Specifically, it investigates the PIM offloading at instruction level. To achieve this goal, it presents a graph benchmark suite for understanding graph computing behaviors, and then proposes architectural techniques for PIM offloading on various host platforms. This dissertation first presents GraphBIG, a comprehensive graph benchmark suite. To cover major graph computation types and data sources, GraphBIG selects representative data representations, workloads, and datasets from 21 real-world use cases of multiple application domains. This dissertation characterized the benchmarks on real machines and observed extremely irregular memory patterns and significant diverse behaviors across various computation types. GraphBIG helps users understand the behavior of modern graph computing on hardware architectures and enables future architecture and system research for graph computing. To achieve better performance of graph computing, this dissertation proposes GraphPIM, a full-stack NDP solution for graph computing. This dissertation performs an analysis on modern graph workloads to assess the applicability of PIM offloading and presents hardware and software mechanisms to efficiently make use of the PIM functionality. Following the real-world HMC 2.0 specification, GraphPIM provides performance benefits for graph applications without any user code modification and ISA changes. In addition, GraphPIM proposes an extension to PIM operations that can further bring performance benefits for more graph applications. The evaluation results show that GraphPIM achieves up to a 2.4X speedup with a 37% reduction in energy consumption. To effectively utilize NDP systems with GPU-based host architectures that can fully utilize hundreds of gigabytes of bandwidth, this dissertation explores managing the thermal constraints of 3D-stacked memory cubes. Based on the real experiment with an HMC prototype, this study observes that the operating temperature of HMC is much higher than conventional DRAM, which can even cause thermal shutdown with a passive cooling solution. In addition, it also shows that even with a commodity-server cooling solution, HMC can fail to maintain the temperature of the memory dies within the normal operating range when in-memory processing is highly utilized, thereby resulting in higher energy consumption and performance overhead. To this end, this dissertation proposes CoolPIM, a thermal-aware source throttling mechanism that controls the intensity of PIM offloading on runtime. The proposed technique keeps the memory dies of HMC within the normal operating temperature using software-based techniques. The evaluation results show that CoolPIM achieves up to 1.4X and 1.37X speedups compared to non-offloading and naïve offloading scenarios.