The low-power architecture approach towards exascale computing

Rajovic, Nikola; Puzovic, Nikola; Vilanova, Lluís; Villavieja, Carlos; Ramírez, Alex

doi:10.1145/2133173.2133175

Cited by 24 publications

(19 citation statements)

References 5 publications

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“…This has clearly shown the performance degradation caused by the linear scale of the underlying support hardware (ram bus speed, network, storage), which will hit on the system's performance under I/O Intensive workloads, as is the case with sequential database scans. As pointed in [21], a valid solution for these constraints is the integration of more cpu cores by board, which seems to be consistent with the current market direction.…”

Section: Discussionsupporting

confidence: 72%

A Study on the Energetic Viability of Single Board Computers for Cloud Computing Scenarios

Verdugo

Salvachúa

Huecas

2017

Proceedings XIII Jornadas De Ingenieria Telematica - JITEL2017

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Abstract-The following document explores the viability of the usage of consumer-grade, ARM-based single board computers as a power saving alternative to the traditional monolithic x64-full-server based approach. By taking advantage of several capabilities provided by such devices, such as low cost, low power consumption and low on-time, the authors finally propose a scalable, energy-efficient, ARMbased cloud infrastructure. To that end, we start analyzing the current offerings in terms of capabilities, net cost, processing power and power consumption, comparing them with the relevant server-oriented offerings. We subsequently explore the adequacy of several metrics to model on-budget raw data processing, considering full-system wattage under nominal usage conditions. The low initial investment and long-term affordability of this approach results in quite a relevant case of application to Edge Cloud computing scenarios.

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Section: Discussionsupporting

confidence: 72%

A Study on the Energetic Viability of Single Board Computers for Cloud Computing Scenarios

Verdugo

Salvachúa

Huecas

2017

Proceedings XIII Jornadas De Ingenieria Telematica - JITEL2017

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“…The first category is to seek energy proportionality with non-energyproportional servers [53,32,31,27,35]. The second category is to build more energy-efficient architecture based on low-power CPU [38,41,49,33,43,46,29].…”

Section: Related Workmentioning

confidence: 99%

An experimental evaluation of datacenter workloads on low-power embedded micro servers

Zhao

Shen

et al. 2016

Proc. VLDB Endow.

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This paper presents a comprehensive evaluation of an ultralow power cluster, built upon the Intel Edison based micro servers. The improved performance and high energy efficiency of micro servers have driven both academia and industry to explore the possibility of replacing conventional brawny servers with a larger swarm of embedded micro servers. Existing attempts mostly focus on mobile-class micro servers, whose capacities are similar to mobile phones. We, on the other hand, target on sensor-class micro servers, which are originally intended for uses in wearable technologies, sensor networks, and Internet-of-Things. Although sensor-class micro servers have much less capacity, they are touted for minimal power consumption (< 1 Watt), which opens new possibilities of achieving higher energy efficiency in datacenter workloads. Our systematic evaluation of the Edison cluster and comparisons to conventional brawny clusters involve careful workload choosing and laborious parameter tuning, which ensures maximum server utilization and thus fair comparisons. Results show that the Edison cluster achieves up to 3.5× improvement on work-done-per-joule for web service applications and data-intensive MapReduce jobs. In terms of scalability, the Edison cluster scales linearly on the throughput of web service workloads, and also shows satisfactory scalability for MapReduce workloads despite coordination overhead.

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“…For example, Barcelona Supercomputing Center is looking at using ARM-based systems as the basis for their exascale platform [23]. Key hardware vendors such as Dell, HP and AppliedMicro have launched server prototypes based on ARM processors [28], and a plethora of startups are looking into adopting ARM solutions in the enterprise computing landscape.…”

Section: Introductionmentioning

confidence: 99%

A performance study of big data on small nodes

et al. 2015

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The continuous increase in volume, variety and velocity of Big Data exposes datacenter resource scaling to an energy utilization problem. Traditionally, datacenters employ x86-64 (big) server nodes with power usage of tens to hundreds of Watts. But lately, low-power (small) systems originally developed for mobile devices have seen significant improvements in performance. These improvements could lead to the adoption of such small systems in servers, as announced by major industry players. In this context, we systematically conduct a performance study of Big Data execution on small nodes in comparison with traditional big nodes, and present insights that would be useful for future development. We run Hadoop MapReduce, MySQL and in-memory Shark workloads on clusters of ARM big.LITTLE boards and Intel Xeon server systems. We evaluate execution time, energy usage and total cost of running the workloads on selfhosted ARM and Xeon nodes. Our study shows that there is no one size fits all rule for judging the efficiency of executing Big Data workloads on small and big nodes. But small memory size, low memory and I/O bandwidths, and software immaturity concur in canceling the lower-power advantage of ARM servers. We show that I/O-intensive MapReduce workloads are more energy-efficient to run on Xeon nodes. In contrast, database query processing is always more energy-efficient on ARM servers, at the cost of slightly lower throughput. With minor software modifications, CPU-intensive MapReduce workloads are almost four times cheaper to execute on ARM servers.

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The low-power architecture approach towards exascale computing

Cited by 24 publications

References 5 publications

A Study on the Energetic Viability of Single Board Computers for Cloud Computing Scenarios

A Study on the Energetic Viability of Single Board Computers for Cloud Computing Scenarios

An experimental evaluation of datacenter workloads on low-power embedded micro servers

A performance study of big data on small nodes

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