D. Richins, T. Ahmed, R. Clapp, and V. J. Reddi, “
Amdahl's Law in Big Data Analytics: Alive and Kicking in TPCx-BB (BigBench),” in
IEEE International Symposium on High Performance Computer Architecture (HPCA), 2018, pp. 630–642.
Publisher's VersionAbstractBig data, specifically data analytics, is responsible for driving many of consumers’ most common online activities, including shopping, web searches, and interactions on social media. In this paper, we present the first (micro)architectural investigation of a new industry-standard, open source benchmark suite directed at big data analytics applications—TPCx-BB (BigBench). Where previous work has usually studied benchmarks which oversimplify big data analytics, our study of BigBench reveals that there is immense diversity among applications, owing to their varied data types, computational paradigms, and analyses. In our analysis, we also make an important discovery generally restricting processor performance in big data. Contrary to conventional wisdom that big data applications lend themselves naturally to parallelism, we discover that they lack sufficient thread-level parallelism (TLP) to fully utilize all cores. In other words, they are constrained by Amdahl’s law. While TLP may be limited by various factors, ultimately we find that single-thread performance is as relevant in scale-out workloads as it is in more classical applications. To this end we present core packing: a software and hardware solution that could provide as much as 20% execution speedup for some big data analytics applications.
Paper Presentation B. Boroujerdian, H. Genc, S. Krishnan, W. Cui, A. Faust, and V. J. Reddi, “
MAVBench: Micro Aerial Vehicle Benchmarking,” in
Proceedings of the International Symposium on Microarchitecture (MICRO), 2018.
AbstractUnmanned Aerial Vehicles (UAVs) are getting closer to becoming ubiquitous in everyday life. Among them, Micro Aerial Vehicles (MAVs) have seen an outburst of attention recently, specifically in the area with a demand for autonomy. A key challenge standing in the way of making MAVs autonomous is that researchers lack the comprehensive understanding of how performance, power, and computational bottlenecks affect MAV applications. MAVs must operate under a stringent power budget, which severely limits their flight endurance time. As such, there is a need for new tools, benchmarks, and methodologies to foster the systematic development of autonomous MAVs. In this paper, we introduce the “MAVBench” framework which consists of a closed-loop simulator and an end-to-end application benchmark suite. A closed-loop simulation platform is needed to probe and understand the intra-system (application data flow) and inter-system (system and environment) interactions in MAV applications to pinpoint bottlenecks and identify opportunities for hardware and software co-design and optimization. In addition to the simulator, MAVBench provides a benchmark suite, the first of its kind, consisting of a variety of MAV applications designed to enable computer architects to perform characterization and develop future aerial computing systems. Using our open source, end-to-end experimental platform, we uncover a hidden, and thus far unexpected compute to total system energy relationship in MAVs. Furthermore, we explore the role of compute by presenting three case studies targeting performance, energy and reliability. These studies confirm that an efficient system design can improve MAV’s battery consumption by up to 1.8X.
Paper V. J. Reddi, H. Yoon, and A. Knies, “
Two Billion Devices and Counting,”
IEEE Micro, vol. 38, no. 1, pp. 6–21, 2018.
Publisher's VersionAbstractMobile computing has grown drastically over the past decade. Despite the rapid pace of advancements, mobile device understanding, benchmarking, and evaluation are still in their infancies, both in industry and academia. This article presents an industry perspective on the challenges facing mobile computer architecture, specifically involving mobile workloads, benchmarking, and experimental methodology, with the hope of fostering new research within the community to address pending problems. These challenges pose a threat to the systematic development of future mobile systems, which, if addressed, can elevate the entire mobile ecosystem to the next level.
Mobile devices have come a long way from the first portable cellular phone developed by Motorola in 1973. Most modern smartphones are good enough to replace desktop computers. A smartphone today has enough computing power to be on par with the fastest supercomputers from the 1990s.
For instance, the Qualcomm Adreno 540 GPU found in the latest smartphones has a peak compute capability of more than 500 Gflops, putting it in competition with supercomputers that were on the TOP500 list in the early to mid-1990s. Mobile computing has experienced an unparalleled level of growth over the past decade. At the time of this writing, there are more than 2 billion mobile devices in the world.1 But perhaps even more importantly, mobile phones are showing no signs of slowing in uptake. In fact, smartphone adoption rates are on the rise. The number of devices is rising as mobile device penetration increases in markets like India and China. It is anticipated that the number of mobile subscribers will grow past 6 billion in the coming years.2 As Figure 1 shows, while the Western European and North American markets are reaching saturation, the vast majority of growth is coming from countries in Asia. Given that only 35 percent of the world’s population has thus far adopted mobile technology, there is still significant room for growth and innovation.
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