了解PARSEC在当代cmp上的性能

M. Bhadauria, Vincent M. Weaver, S. Mckee
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引用次数: 86

摘要

PARSEC是工业界和学术界用于评估新的芯片多处理器(CMP)设计的参考应用程序套件。到目前为止,还没有研究在实际硬件上分析PARSEC,以便更好地理解伸缩特性和瓶颈。这种理解对于指导针对这些新兴工作负载的未来CMP设计至关重要。我们使用硬件性能计数器,采用系统级方法和不同的公共体系结构参数:乱序核的数量、内存层次结构配置、多个并发线程的数量、内存通道的数量和处理器频率。我们发现这些程序在很大程度上受计算约束,因此受到核心数量、微架构资源和缓存到缓存传输的限制,而不是芯片外内存或系统总线带宽。一半的套件不能随着线程数量的增加而线性扩展,并且在所有测试的平台上,一些应用程序在几个线程时就会使性能饱和。利用线程级并行性比利用指令级并行性带来更大的回报。为了降低功耗和提高性能,我们建议增加每个核心的算术单元数,增加对TLP的支持,减少对ILP的支持。
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Understanding PARSEC performance on contemporary CMPs
PARSEC is a reference application suite used in industry and academia to assess new Chip Multiprocessor (CMP) designs. No investigation to date has profiled PARSEC on real hardware to better understand scaling properties and bottlenecks. This understanding is crucial in guiding future CMP designs for these kinds of emerging workloads. We use hardware performance counters, taking a systems-level approach and varying common architectural parameters: number of out-of-order cores, memory hierarchy configurations, number of multiple simultaneous threads, number of memory channels, and processor frequencies. We find these programs to be largely compute-bound, and thus limited by number of cores, micro-architectural resources, and cache-to-cache transfers, rather than by off-chip memory or system bus bandwidth. Half the suite fails to scale linearly with increasing number of threads, and some applications saturate performance at few threads on all platforms tested. Exploiting thread level parallelism delivers greater payoffs than exploiting instruction level parallelism. To reduce power and improve performance, we recommend increasing the number of arithmetic units per core, increasing support for TLP, and reducing support for ILP.
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