标题： 快速运行到空闲：异构CPU和GPU架构上矩阵乘法的能量效率 显示英文标题

标题： Racing to Idle: Energy Efficiency of Matrix Multiplication on Heterogeneous CPU and GPU Architectures

摘要： 随着多核和异构计算的范式转变，这是由单核处理器的基本功耗和热限制所推动的，能源效率已成为高性能计算（HPC）中的一类设计约束。 异构系统，将传统的多核CPU与专用加速器如独立（dGPU）和集成（iGPU）图形处理单元相结合，提供了一条有吸引力的路径来权衡性能和功耗之间的关系。 然而，在广泛可访问的硬件上量化这些权衡仍然是一个关键的研究领域。 本文在一台消费级笔记本电脑中的三种不同的计算架构上，对一个典型的HPC工作负载——4096x4096矩阵相乘——进行了直接的实证测量：一个多核AMD Ryzen 7 5800H CPU，一个独立的NVIDIA GeForce GTX 1650 GPU，以及一个集成的AMD Radeon Vega GPU。 使用标准、经过验证且侵入性最小的工具，如Linux perf和nvidia-smi，我们发现独立GPU不仅是性能领先者，其性能比CPU快93.5倍，而且也是最节能的，仅消耗CPU所用能量的2%，从而在能源效率方面提高了50倍。 这些发现提供了“空闲竞赛”原则的实用演示，并为能源感知的软件开发提供了明确的定量指导。 显示英文摘要

摘要： The paradigm shift towards multi-core and heterogeneous computing, driven by the fundamental power and thermal limits of single-core processors, has established energy efficiency as a first-class design constraint in high-performance computing (HPC). Heterogeneous systems, integrating traditional multi-core CPUs with specialized accelerators like discrete (dGPU) and integrated (iGPU) graphics processing units, offer a compelling path to navigating the trade-offs between performance and power. However, quantifying these trade-offs on widely accessible hardware remains a critical area of study. This paper presents a direct, empirical measurement of the performance and energy-to-solution of a canonical HPC workload -- a 4096x4096 matrix-matrix multiplication -- on three distinct compute architectures within a single consumer-grade laptop: a multi-core AMD Ryzen 7 5800H CPU, a discrete NVIDIA GeForce GTX 1650 GPU, and an integrated AMD Radeon Vega GPU. Using standard, validated, and minimally intrusive tools such as Linux perf and nvidia-smi, we find that the discrete GPU is not only the performance leader, achieving a 93.5x speedup over the CPU, but is also the most energy-efficient, consuming only 2% of the energy used by the CPU, resulting in a 50-fold improvement in energy efficiency. These findings provide a practical demonstration of the "race to idle" principle and offer clear, quantitative guidance on architectural choices for energy-aware software development.