The multiprocessor effect refers to the loss of computing cycles due to processing overhead. Amdahl's law and the Multiprocessing Factor (MPF) are two scaling models used in industry and academia for estimating multiprocessor capacity in the presence of this multiprocessor effect. Both models express different laws of diminishing returns. Amdahl's law identifies diminishing processor capacity with a fixed degree of serialization in the workload, while the MPF model treats it as a constant geometric ratio. The utility of both models for performance evaluation stems from the presence of a single parameter that can be determined easily from a small set of benchmark measurements. This utility, however, is marred by a dilemma. The two models produce different results, especially for large processor configurations that are so important for today's applications. The question naturally arises: Which of these two models is the correct one to use? Ignoring this question merely reduces capacity prediction to arbitrary curve-fitting. Removing the dilemma requires a dynamical interpretation of these scaling models. We present a physical interpretation based on queueing theory and show that Amdahl's law corresponds to synchronous queueing in a bus model while the MPF model belongs to a Coxian server model. The latter exhibits unphysical effects such as sublinear response times hence, we caution against its use for large multiprocessor configurations.
翻译:多处理器效果指计算周期因处理间接费用而损失的计算周期。 Amdahl的法律和多处理系数( MPF) 是工业和学术界在多处理器效果下用于估算多处理能力的两个规模模型。 两种模型都表达了不同的递减回报法。 Amdahl的法律确定了递减处理能力, 并规定了工作量的固定序列化程度, 而 MPF 模型则将其视为一个不变的几何比。 两种模型对于绩效评估的效用都来自一个单一参数的存在, 该参数可以从一套小的基准测量中轻易确定。 然而, 这套工具受到两难的困扰。 这两种模型产生不同的结果, 特别是对于今天的应用非常重要的大型处理器配置。 问题自然产生: 这两种模型中的哪个是正确使用的? 忽略这个问题只是将能力预测降低到任意的曲线调整。 消除困境需要对这些缩放模型进行动态解释。 我们根据排队理论提出一个物理解释, 并显示Amdahl 的法律与一个同步的排队列法相匹配, 特别是对于今天应用程序非常重要的大型处理器配置。 。 问题自然产生多式服务器模型, 而我们使用多式服务器则使用这种模型, 我们使用这种模型作为后方程式作为后方程式的催化模型, 。