We investigate the energy efficiency of a library designed for parallel computations with sparse matrices. The library leverages high-performance, energy-efficient Graphics Processing Unit (GPU) accelerators to enable large-scale scientific applications. Our primary development objective was to maximize parallel performance and scalability in solving sparse linear systems whose dimensions far exceed the memory capacity of a single node. To this end, we devised methods that expose a high degree of parallelism while optimizing algorithmic implementations for efficient multi-GPU usage. Previous work has already demonstrated the library's performance efficiency on large-scale systems comprising thousands of NVIDIA GPUs, achieving improvements over state-of-the-art solutions. In this paper, we extend those results by providing energy profiles that address the growing sustainability requirements of modern HPC platforms. We present our methodology and tools for accurate runtime energy measurements of the library's core components and discuss the findings. Our results confirm that optimizing GPU computations and minimizing data movement across memory and computing nodes reduces both time-to-solution and energy consumption. Moreover, we show that the library delivers substantial advantages over comparable software frameworks on standard benchmarks.

翻译：我们研究了一款专为稀疏矩阵并行计算设计的计算库的能效特性。该库利用高性能、高能效的图形处理单元（GPU）加速器，支持大规模科学应用。我们的主要开发目标是最大化求解维度远超单节点内存容量的稀疏线性系统时的并行性能与可扩展性。为此，我们设计了能暴露高度并行性的方法，同时优化算法实现以高效利用多GPU。先前工作已证明该库在包含数千个NVIDIA GPU的大规模系统上具备性能效率，并相较现有最优方案实现了提升。本文通过提供满足现代HPC平台日益增长的可持续性需求的能效概况，进一步拓展了这些成果。我们介绍了用于精确测量该库核心组件运行时能耗的方法与工具，并讨论了相关发现。研究结果证实，优化GPU计算并最小化跨内存与计算节点的数据迁移，既能缩短求解时间，也能降低能耗。此外，我们证明该库在标准基准测试中比同类软件框架具有显著优势。