22 research outputs found

    Remote Performance Monitor (RPM)

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    Mobile, resource-constrained, battery-powered devices have emerged as key access points to the world\u27s digital infrastructure. To enable our understanding of the performance of these devices, we must be able to efficiently collect accurate profile data from these devices after they are deployed in the field. Moreover, understanding the full-system power and energy behavior of these systems for real programs is vital if users are to accurately characterize, model, and develop effective techniques for extending battery life. Unfortunately, extant approaches to measuring and characterizing power and energy consumption focus on high-end processors, do not consider the complete device, employ inaccurate (program-only) simulation, rely on inaccurate, course-grained battery level data from the device, or employ expensive power measurement tools that are difficult to share across research groups and students. To address these issues, we developed remote performance monitor (RPM). The first component of RPM is an efficient technique for collecting accurate sample-based program profiles. The key to the efficacy of this technique is that we identify when to sample using the repeating patterns in program execution, phases. To enable fine-grained, full-system characterization of embedded computers, we couple and unify phase-aware profiling, hardware performance monitoring, and power and energy measurement within RPM. RPM consists of a tightly coupled set of components which (1) control lab equipment for power measurements and analysis, (2) configure target system characteristics at run-time (such as CPU and memory bus speed), (3) collect target system data using on-board hardware performance monitors (HPMs) and (4) provide a remote access interface. Users of RPM can submit and configure experiments that execute programs on the RPM target device (currently a Stargate sensor platform that is very similar to an HP iPAQ) to collect very accurate power, energy, and CPU performance data with high resolution

    Multiple novel prostate cancer susceptibility signals identified by fine-mapping of known risk loci among Europeans

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    Genome-wide association studies (GWAS) have identified numerous common prostate cancer (PrCa) susceptibility loci. We have fine-mapped 64 GWAS regions known at the conclusion of the iCOGS study using large-scale genotyping and imputation in 25 723 PrCa cases and 26 274 controls of European ancestry. We detected evidence for multiple independent signals at 16 regions, 12 of which contained additional newly identified significant associations. A single signal comprising a spectrum of correlated variation was observed at 39 regions; 35 of which are now described by a novel more significantly associated lead SNP, while the originally reported variant remained as the lead SNP only in 4 regions. We also confirmed two association signals in Europeans that had been previously reported only in East-Asian GWAS. Based on statistical evidence and linkage disequilibrium (LD) structure, we have curated and narrowed down the list of the most likely candidate causal variants for each region. Functional annotation using data from ENCODE filtered for PrCa cell lines and eQTL analysis demonstrated significant enrichment for overlap with bio-features within this set. By incorporating the novel risk variants identified here alongside the refined data for existing association signals, we estimate that these loci now explain ∼38.9% of the familial relative risk of PrCa, an 8.9% improvement over the previously reported GWAS tag SNPs. This suggests that a significant fraction of the heritability of PrCa may have been hidden during the discovery phase of GWAS, in particular due to the presence of multiple independent signals within the same regio

    Energy Consumption and Conservation in Mobile Peer-toPeer Systems

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    Today’s mobile devices are growing in number and computational resources. Devices capable of storing gigabytes of digital content are becoming ubiquitous, making them an ideal platform for peer-to-peer content delivery and sharing. However, the alwayson communication patterns of P2P networks is not a natural fit for energy-constrained mobile devices. In this paper, we perform a detailed study of energy consumption of a structured P2P overlay on a PDA device. Using actual energy measurements, we present energy consumption results for different type of operations in P2P overlays. Based on these observations, we implement an approach to improve energy conservation on P2P protocols and show some promising preliminary results

    A run-time, feedback-based energy estimation model for embedded devices

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    We present an adaptive, feedback-based, energy estimation model for battery-powered embedded devices such as sensor network gateways and hand-held computers. Our technique maps hardware and software counters to energy consumption values using a set of first order, linear regression equations. Our system is novel in that it combines online and offline techniques to enable runtime power prediction. Our system employs an offline instantiated model that it continuously updates using feedback from a readily available battery monitor within the device. We empirically evaluate our model and detail its robustness, accuracy, and computational cost. We also analyze the stability of the model in the presence of feedback errors. We demonstrate that our approach can achieve an error rate of 1 % (extant techniques: 2.6 % to 4%) for computationally bound tasks and 6.6 % (extant techniques: 11%) for communication bound tasks

    Measurements General Terms Measurement, Performance

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    We present AutoDVS, a dynamic voltage scaling (DVS) system for hand-held computers. Unlike extant DVS systems, AutoDVS distinguishes common, course-grain, program behavior and couples forecasting techniques to make accurate predictions of future behavior. AutoDVS uses these predictions in combination to guide dynamic voltage scaling. AutoDVS estimates periods of user interactivity, user non-interactivity (think time), and computation perprogram and system wide to ensure quality of service while reducing energy consumption. We describe our implementation of AutoDVS which consists of a set light-weight, Linux, kernel modules and user library routines for the iPAQ hand-held computer. We evaluate AutoDVS using real user workloads of iPAQ software that consist of interactive and soft-real time tasks executing alone and concurrently. Our results indicate that AutoDVS decreases energy consumption significantly without negatively impacting user perception of system performance

    Energy Characterization of the Stargate Sensor Network Gateway

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    We present a new energy estimation model for sensor network intermediate nodes (i.e. the Crossbow XScale Stargate). Such devices are battery powered and resource constrained and commonly employed as communication, processing, and gateway elements within sensor networks. Understanding and accurately characterizing the energy behavior of such devices is key to conserving the battery life of sensor systems. In this paper, we present a macro-model for estimating the energy consumption of the device as a whole that couples estimation techniques for computation and communication. We construct our model using empirical data that we collect via hardware performance monitors. Our contributions are two-fold. 1) We demonstrate that hardware performance monitors are effective in modeling the computational energy consumption of sensor network gateways, 2) we present an analysis of linear dependencies between various hardware performance events, discuss their effects on model stability, and show how we can use principal component analysis to remove the undesirable side effects of such dependencies.
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