{"title":"Performance, energy characterizations and architectural implications of an emerging mobile platform benchmark suite - MobileBench","authors":"D. Pandiyan, Shin-Ying Lee, Carole-Jean Wu","doi":"10.1109/IISWC.2013.6704679","DOIUrl":null,"url":null,"abstract":"In this paper, we explore key microarchitectural features of mobile computing platforms that are crucial to the performance of smart phone applications. We create and use a selection of representative smart phone applications, which we call MobileBench that aid in this analysis. We also evaluate the effectiveness of current memory subsystem on the mobile platforms. Furthermore, by instrumenting the Android framework, we perform energy characterization for MobileBench on an existing Samsung Galaxy S III smart phone. Based on our energy analysis, we find that application cores on modern smart phones consume significant amount of energy. This motivates our detailed performance analysis centered at the application cores. Based on our detailed performance studies, we reach several key findings. (i) Using a more sophisticated tournament branch predictor can improve the branch prediction accuracy but this does not translate to observable performance gain. (ii) Smart phone applications show distinct TLB capacity needs. Larger TLBs can improve performance by an avg. of 14%. (iii) The current L2 cache on most smart phone platform experiences poor utilization because of the fast-changing memory requirements of smart phone applications. Using a more effective cache management scheme improves the L2 cache utilization by as much as 29.3% and by an avg. of 12%. (iv) Smart phone applications are prefetching-friendly. Using a simple stride prefetcher can improve performance across MobileBench applications by an avg. of 14%. (v) Lastly, the memory bandwidth requirements of MobileBench applications are moderate and well under current smart phone memory bandwidth capacity of 8.3 GB/s. With these insights into the smart phone application characteristics, we hope to guide the design of future smart phone platforms for lower power consumptions through simpler architecture while achieving high performance.","PeriodicalId":365868,"journal":{"name":"2013 IEEE International Symposium on Workload Characterization (IISWC)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"68","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 IEEE International Symposium on Workload Characterization (IISWC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IISWC.2013.6704679","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 68
Abstract
In this paper, we explore key microarchitectural features of mobile computing platforms that are crucial to the performance of smart phone applications. We create and use a selection of representative smart phone applications, which we call MobileBench that aid in this analysis. We also evaluate the effectiveness of current memory subsystem on the mobile platforms. Furthermore, by instrumenting the Android framework, we perform energy characterization for MobileBench on an existing Samsung Galaxy S III smart phone. Based on our energy analysis, we find that application cores on modern smart phones consume significant amount of energy. This motivates our detailed performance analysis centered at the application cores. Based on our detailed performance studies, we reach several key findings. (i) Using a more sophisticated tournament branch predictor can improve the branch prediction accuracy but this does not translate to observable performance gain. (ii) Smart phone applications show distinct TLB capacity needs. Larger TLBs can improve performance by an avg. of 14%. (iii) The current L2 cache on most smart phone platform experiences poor utilization because of the fast-changing memory requirements of smart phone applications. Using a more effective cache management scheme improves the L2 cache utilization by as much as 29.3% and by an avg. of 12%. (iv) Smart phone applications are prefetching-friendly. Using a simple stride prefetcher can improve performance across MobileBench applications by an avg. of 14%. (v) Lastly, the memory bandwidth requirements of MobileBench applications are moderate and well under current smart phone memory bandwidth capacity of 8.3 GB/s. With these insights into the smart phone application characteristics, we hope to guide the design of future smart phone platforms for lower power consumptions through simpler architecture while achieving high performance.
在本文中,我们探讨了移动计算平台的关键微架构特征,这些特征对智能手机应用程序的性能至关重要。我们创建并使用了一系列具有代表性的智能手机应用程序,我们称之为MobileBench,这有助于我们的分析。我们还评估了当前存储子系统在移动平台上的有效性。此外,通过测量Android框架,我们在现有的三星Galaxy S III智能手机上对MobileBench进行能量表征。根据我们的能量分析,我们发现现代智能手机上的应用程序内核消耗了大量的能量。这促使我们以应用程序核心为中心进行详细的性能分析。基于我们详细的性能研究,我们得出了几个关键发现。(i)使用更复杂的比赛分支预测器可以提高分支预测的准确性,但这并不能转化为可观察的性能增益。(ii)智能手机应用显示出不同的TLB容量需求。较大的tlb可以使性能平均提高14%。(iii)由于智能手机应用对内存需求的快速变化,目前大多数智能手机平台上的二级缓存利用率较低。使用更有效的缓存管理方案可将二级缓存利用率提高29.3%,平均提高12%。(iv)智能手机应用程序支持预取。使用简单的步幅预取器可以将MobileBench应用程序的性能平均提高14%。(v)最后,MobileBench应用程序的内存带宽要求适中,并且在当前8.3 GB/s的智能手机内存带宽容量下运行良好。通过这些对智能手机应用特性的洞察,我们希望指导未来智能手机平台的设计,通过更简单的架构实现更低的功耗,同时实现高性能。