Chip multicore processors (CMPs) constitute the architecture of choice for a wide spectrum of computing systems, ranging from power-efficient mobile devices to high-performance server platforms. Despite their benefits, the contention that appears when multiple applications compete for the use of shared resources among cores, such as the last-level cache (LLC), may lead to substantial performance degradation. This may have a negative impact on key system metrics such as throughput and fairness. Partitioning of the LLC (i.e., assigning a separate cache partition with a certain size to each application) has been proven effective to mitigate contention-related effects.

In this article we propose a parallel simulator that makes it possible to quickly compare the effectiveness of different cache-partitioning policies with the optimal solution for different optimization objectives. The simulator can obtain the optimal solution for any point during the execution of a multi-program workload where each application goes through a certain program phase. Our proposal leverages a slowdown-prediction model that accounts for degradation due to cache sharing and memory-bandwidth contention, which constitute the major factors of shared-resource contention on current CMPs. To determine the optimal solution for two optimization objectives (throughput and fairness optimization), we leverage a novel distributed-memory parallel branch-and-bound strategy specifically designed to efficiently distribute the computation across multiple processing cores.

芯片多核处理器（CMP）构成了从节能型移动设备到高性能服务器平台等众多计算系统的首选架构。尽管有它们的好处，但是当多个应用程序争用内核之间的共享资源（例如最后一级缓存（LLC））时出现的争用可能会导致性能大幅下降。这可能会对关键系统指标（例如吞吐量和公平性）产生负面影响。事实证明，对LLC进行分区（即为每个应用程序分配具有一定大小的单独的缓存分区）可有效缓解与竞争相关的影响。

在本文中，我们提出了一种并行模拟器，该模拟器可以快速比较不同缓存分配策略的有效性和针对不同优化目标的最佳解决方案。在执行多程序工作负载期间，每个应用程序都要经过某个程序阶段，模拟器可以为任何一点获得最佳解决方案。我们的建议利用了一种慢速预测模型，该模型可以解决由于缓存共享和内存带宽争用而造成的性能下降，而缓存共享和内存带宽争用是当前CMP上共享资源争用的主要因素。为了确定两个优化目标（吞吐量和公平性优化）的最佳解决方案，