Prediction-Based Power-Performance Adaptation of Multithreaded Scientific Codes

Computing has recently reached an inflection point with the introduction of multicore processors. On-chip thread-level parallelism is doubling approximately every other year. Concurrency lends itself naturally to allowing a program to trade performance for power savings by regulating the number of active cores; however, in several domains, users are unwilling to sacrifice performance to save power. We present a prediction model for identifying energy-efficient operating points of concurrency in well-tuned multithreaded scientific applications and a runtime system that uses live program analysis to optimize applications dynamically. We describe a dynamic phase-aware performance prediction model that combines multivariate regression techniques with runtime analysis of data collected from hardware event counters to locate optimal operating points of concurrency. Using our model, we develop a prediction-driven phase-aware runtime optimization scheme that throttles concurrency so that power consumption can be reduced and performance can be set at the knee of the scalability curve of each program phase. The use of prediction reduces the overhead of searching the optimization space while achieving near-optimal performance and power savings. A thorough evaluation of our approach shows a reduction in power consumption of 10.8 percent, simultaneous with an improvement in performance of 17.9 percent, resulting in energy savings of 26.7 percent.

Neuropeptides in Helminths: Occurrence and Distribution

Nematode neuropeptide systems comprise an exceptionally complex array of similar to 250 peptidic signaling molecules that operate within a structurally simple nervous system of similar to 300 neurons. A relatively complete picture of the neuropeptide complement is available for Caenorhabditis elegans, with 30 flp, 38 ins and 43 nlp genes having been documented; accumulating evidence indicates similar complexity in parasitic nematodes from clades I, III, IV and V. In contrast, the picture for parasitic platyhelminths is less clear, with the limited peptide sequence data available providing concrete evidence for only FMRFamide-like peptide (FLP) and neuropeptide F (NPF) signaling systems, each of which only comprises one or two peptides. With the completion of the Schmidtea meditteranea and Schistosoma mansoni genome projects and expressed sequence tag datasets for other flatworm parasites becoming available, the time is ripe for a detailed reanalysis of neuropeptide signaling in flatworms. Although the actual neuropeptides provide limited obvious value as targets for chemotherapeutic-based control strategies, they do highlight the signaling systems present in these helminths and provide tools for the discovery of more amenable targets such as neuropeptide receptors or neuropeptide processing enzymes. Also, they offer opportunities to evaluate the potential of their associated signaling pathways as targets through RNA interference (RNAi)-based, target validation strategies. Currently, within both helminth phyla, the flp signaling systems appear to merit further investigation as they are intrinsically linked with motor function, a proven target for successful anti-parasitics; it is clear that some nematode NLPs also play a role in motor function and could have similar appeal. At this time, it is unclear if flatworm NPF and nematode INS peptides operate in pathways that have utility for parasite control. Clearly, RNAi-based validation could be a starting point for scoring potential target pathways within neuropeptide signaling for parasiticide discovery programs. Also, recent successes in the application of in planta-based RNAi control strategies for plant parasitic nematodes reveal a strategy whereby neuropeptide encoding genes could become targets for parasite control. The possibility of developing these approaches for the control of animal and human parasites is intriguing, but will require significant advances in the delivery of RNAi-triggers.

Power Capping: What Works, What Does Not

Peak power consumption is the first order design constraint of data centers. Though peak power consumption is rarely, if ever, observed, the entire data center facility must prepare for it, leading to inefficient usage of its resources. The most prominent way for addressing this issue is to limit the power consumption of the data center IT facility far below its theoretical peak value. Many approaches have been proposed to achieve that, based on the same small set of enforcement mechanisms, but there has been no corresponding work on systematically examining the advantages and disadvantages of each such mechanism. In the absence of such a study,it is unclear what is the optimal mechanism for a given computing environment, which can lead to unnecessarily poor performance if an inappropriate scheme is used. This paper fills this gap by comparing for the first time five widely used power capping mechanisms under the same hardware/software setting. We also explore possible alternative power capping mechanisms beyond what has been previously proposed and evaluate them under the same setup. We systematically analyze the strengths and weaknesses of each mechanism, in terms of energy efficiency, overhead, and predictable behavior. We show how these mechanisms can be combined in order to implement an optimal power capping mechanism which reduces the slow down compared to the most widely used mechanism by up to 88%. Our results provide interesting insights regarding the different trade-offs of power capping techniques, which will be useful for designing and implementing highly efficient power capping in the future. 

Simultaneous Measurements of the Channel Response for Multiple Eavesdroppers Operating in the Vicinity of a Body Area Network at 2.45 GHz

Channel randomness can be exploited to generate secret keys. However, to ensure secrecy, it is necessary that the channel response of any eavesdropping party remain sufficiently de-correlated with that of the legitimate users'. In this paper, we investigate whether such de-correlation occurs for a body area network (BAN) operating in an indoor environment at 2.45 GHz. The hypothetical BAN configuration consisted of two legitimate transceivers, one situated on the user's left wrist and the other on the user's waist. The eavesdroppers were positioned in either a co-located or distributed manner in the area surrounding the BAN user. Using the simultaneous channel response measured at the legitimate BAN nodes and the eavesdropper positions for stationary and mobile scenarios, we analyze the localized correlation coefficient. This allows us to determine if it is possible to generate secret keys in the presence of multiple eavesdroppers in an indoor environment. Our experimental results show that although channel reciprocity was observed for both the stationary and the mobile scenarios, a higher de-correlation between the legitimate users' channels was observed for the stationary case. This indicates that mobile scenarios are better suited for secret key generation.

Minimising the Execution of Unknown Bag-of-Task Jobs with Deadlines on the Cloud

Scheduling jobs with deadlines, each of which defines the latest time that a job must be completed, can be challenging on the cloud due to incurred costs and unpredictable performance. This problem is further complicated when there is not enough information to effectively schedule a job such that its deadline is satisfied, and the cost is minimised. In this paper, we present an approach to schedule jobs, whose performance are unknown before execution, with deadlines on the cloud. By performing a sampling phase to collect the necessary information about those jobs, our approach delivers the scheduling decision within 10% cost and 16% violation rate when compared to the ideal setting, which has complete knowledge about each of the jobs from the beginning. It is noted that our proposed algorithm outperforms existing approaches, which use a fixed amount of resources by reducing the violation cost by at least two times.