多线程应用采样模拟的较短在线预热

Chuntao Jiang, Zhibin Yu, Hai Jin, Xiaofei Liao, L. Eeckhout, Yonggang Zeng, Chengzhong Xu
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引用次数: 1

摘要

在采样微建筑模拟中,预热是避免性能偏差的关键问题,可以在每个采样单元之前为微建筑结构构建准确的状态。直到最近,研究人员才提出了基于时间的采样(TBS)来对多线程应用程序进行采样模拟。然而,TBS中的预热是具有挑战性和复杂性的,因为(i) TBS中的全功能预热会导致非常高的开销,限制了整体模拟速度,(ii)传统的单线程应用程序采样的自适应功能预热不能轻易应用于TBS,以及(iii)由于巨大的存储需求和多线程应用程序不同运行之间的变化,检查指向是不灵活的(甚至无效)。在这项工作中,我们提出了缩短在线(SOL)预热,它采用两阶段策略,在第一阶段使用“初始”预热,在第二阶段使用扩展的“无状态损失(NSL)”方法。SOL是一种单次在线预热技术,用于解决并行模拟器中TBS所带来的预热挑战。SOL非常精确和高效,在仿真精度和速度之间提供了很好的权衡,并且很容易部署到不同的TBS技术中。对于模拟8核系统上的PARSEC基准测试,两种最先进的TBS技术与SOL预热相比,在详细模拟中分别提供了7.2倍和37倍的模拟加速,而在完全预热下分别为3.1倍和4.5倍。SOL平均只牺牲了0.3%的绝对执行时间预测精度。
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Shorter On-Line Warmup for Sampled Simulation of Multi-threaded Applications
Warm up is a crucial issue in sampled micro architectural simulation to avoid performance bias by constructing accurate states for micro-architectural structures before each sampling unit. Not until very recently have researchers proposed Time-Based Sampling (TBS) for the sampled simulation of multi-threaded applications. However, warm up in TBS is challenging and complicated, because (i) full functional warm up in TBS causes very high overhead, limiting overall simulation speed, (ii) traditional adaptive functional warm up for sampling single-threaded applications cannot be readily applied to TBS, and (iii) check pointing is inflexible (even invalid) due to the huge storage requirements and the variations across different runs for multi-threaded applications. In this work, we propose Shorter On-Line (SOL) warm up, which employs a two-stage strategy, using 'prime' warm up in the first stage, and an extended 'No-State-Loss (NSL)' method in the second stage. SOL is a single-pass, on-line warm up technique that addresses the warm up challenges posed in TBS in parallel simulators. SOL is highly accurate and efficient, providing a good trade-off between simulation accuracy and speed, and is easily deployed to different TBS techniques. For the PARSEC benchmarks on a simulated 8-core system, two state-of-the-art TBS techniques with SOL warm up provide a 7.2× and 37× simulation speedup over detailed simulation, respectively, compared to 3.1× and 4.5× under full warm up. SOL sacrifices only 0.3% in absolute execution time prediction accuracy on average.
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