PerfBound: Conserving Energy with Bounded Overheads in On/Off-Based HPC Interconnects

Energy and power are key challenges in high-performance computing. System energy efficiency must be significantly improved, and this requires greater efficiency in all subcomponents. An important target of optimization is the interconnect, since network links are always on, consuming power even during idle periods. A large number of HPC machines have a primary interconnect based on Ethernet (about 40 percent of TOP500 machines), which, since 2010, has included support for saving power via Energy Efficient Ethernet (EEE). Nevertheless, it is unlikely that HPC interconnects would use these energy saving modes unless the performance overhead is known and small. This paper presents PerfBound, a self-contained technique to manage on/off-based networks such as EEE, minimizing interconnect link energy consumption subject to a bound on the performance degradation. PerfBound does not require changes to the applications and it uses only local information already available at switches and NICs without introducing additional communication messages, and is also compatible with multi-hop networks. PerfBound is evaluated using traces from a production supercomputer. For twelve out of fourteen applications, PerfBound has high energy savings, up to 70 percent for only 1 percent performance degradation. This paper also presents DynamicFastwake, which extends PerfBound to exploit multiple low-power states. DynamicFastwake achieves an energy-delay product 10 percent lower than the original PerfBound techniqueThis research was supported by European Union’s 7th Framework Programme [FP7/2007-2013] under the Mont-Blanc-3 (FP7-ICT-671697) and EUROSERVER (FP7-ICT-610456) projects, the Ministry of Economy and Competitiveness of Spain (TIN2012-34557 and TIN2015-65316), Generalitat de Catalunya (FI-AGAUR 2012 FI B 00644, 2014-SGR-1051 and 2014-SGR-1272), the European Union’s Horizon2020 research and innovation programme under the HiPEAC-3 Network of Excellence (ICT-287759), and the Severo Ochoa Program (SEV-2011-00067) of the Spanish Government.Peer ReviewedPostprint (author's final draft

Detection and Analysis of Lysozyme Activity in some Tuberous Plants and Calotropis Procera’s Latex

Tuber extract of all the plant species used in the study exhibited lysozyme activity confirming the ubiquitous presence of lysozyme in plants. Among the different plants screened for protein content the tuber extract of Solanum tuberosum showed highest buffer soluble protein while tuber extract of Raphanus sativus showed the lowest protein content in sodium acetate buffer (50 Mm; pH 5.0). Tuber extract of Raphanus sativus showed highest lysozyme activity among all the plant species tested in this study and the activity was increased when the tuber was extracted with sodium phosphate buffer (50mM; pH 7.0). The lowest lysozyme was observed with tuber extract of Daucus carota in phosphate buffer (50mM; pH 7.0). The latex of the tropical species Calotropis procera is well known for being a rich source of the lysozyme. Lysozyme of Calotropis procera latex is not thermo labile. It did not lose much of its activity when the latex was incubated at different temperatures for 24 hours. A positive pointer for purification of this enzyme in future. Calotropis procera lysozyme can be was specifically isolated and purified from the whole latex with ammonium sulphate precipitation with 95% saturation. Calotropis procera lysozyme retained its activity even after precipitation with ammonium sulphate and dialysis and could hydrolyse the cell wall of Micrococcus lysodeikticus.Key words: Micrococcus lysodeikticus, Tuberous Plants, Latex, Ammonium sulphate precipitation, Polyacrylamide gel electrophoresis M. Sakthivel et al. Detection and Analysis of Lysozyme Activity in some Tuberous Plants and Calotropis Procera’s Latex. J Phytol 2/11 (2010) 65-72

Screening, Identifying of Penicillium K-P Strain and Its Cellulase Producing Conditions

Cellulase production by Penicillium species are of greater interest in microbial enzyme technology. Penicillium sp. was cultivated in liquid culture medium at different carbon sources, nitrogen sources in different pH and temperature conditions. The strain of K-P with high cellulase activity was screened. The cellulase activity was 198 U/mL in the presence of fructose on day fifth. Maximum activity was recorded 154 U/mL in with the presence of ammonium nitrate on the fourth day. And maximum cellulase activity was obtained when the pH was 3.0 (129 U/mL) on day fourth. But the highest cellulase activity recorded (274 U/mL) in the presence of fructose, ammonium nitrate, pH 3.0 on the fifth day. The results showed the profiles of cellulase were produced maximum level according to which enzyme is most active in that particular environment

Cloud based multicasting using fat tree data confidential recurrent neural network

With the progress of cloud computing, more users are attracted by its strong and cost-effective computation potentiality. Nevertheless, whether Cloud Service Providers can efficiently protect Cloud Users data confidentiality (DC) remains a demanding issue. The CU may execute several applications with multicast needs. In Cloud different techniques were used to provide DC with multicast necessities. In this work, we aim at ensuring DC in the cloud. This is achieved using a two-step technique, called Fat Tree Data Confidential Recurrent Neural Network (FT-DCRNN) in a cloud environment. The first step performs the construction of Fat Tree based on Multicast model. The aim to use Fat Tree with Multicast model is that the multicast model propagates traffic on multiple links. With the Degree Restrict Multicast Fat Tree construction algorithm using a reference function, the minimum average between two links is measured. With these measured links, multicast is said to be performed that in turn improves the throughput and efficiency of cloud service. Then, with the objective of providing DC for the multi-casted data or messages, DCRNN model is applied. With the Non-linear Recurrent Neural Network using Logistic Activation Function, by handling complex non-linear relationships, average response time is said to be reduced

AN EFFECTIVE APPROACH OF BILATERAL FILTER IMPLEMENTATION IN SPARTAN-3 FIELD PROGRAMMABLE GATE ARRAY

This paper presents the Field Programmable Gate Array (FPGA) implementation of Bilateral Filter, in order to achieve high performance and low power consumption. Bilateral filtering is a technique to smooth images while preserving edges by means of a nonlinear combination of nearby image values. This method is nonlinear, local, and simple. We give an idea that bilateral filtering can be accelerated by bilateral grid scheme that enables fast edge-aware image processing. Nowadays, most of the applications require real time hardware systems with large computing potentiality for which fast and dedicated Very Large Scale Integration (VLSI) architecture appears to be the best possible solution. While it ensures high resource utilization, that too in cost effective platforms like FPGA, designing such architecture does offers some flexibilities like speeding up the computation by adapting more pipelined structures and parallel processing possibilities of reduced memory consumptions. Here we have developed an effective approach of bilateral filter implementation in Spartan-3 FPGA

Optimization of Culture Conditions for the Production of Extracellular Cellulase from Corynebacterium lipophiloflavum

Cellulolytic enzyme producing bacteria were isolated from decaying vegetables on nutrient agar plates. As many as fourteen different bacterial strains were isolated and they were screened for the production of cellulase enzyme on a medium containing carboxymethyl cellulose (0.5% w/v). A Congo red dye based qualitative assay was performed to identify cellulase producing bacteria isolated from the vegetables. Among the difference strains, after screening on CMC agar, one strain exhibited relatively higher cellulase activity and was identified as Cornybacterium lipophiloflavum by a bacteriologist. Culture conditions were optimized for the above bacterium in the production medium supplemented with different carbon and nitrogen sources, different pH and different concentrations of CMC. Glucose and yeast extract proved to be the suitable carbon and nitrogen sources while pH 7.0 was ideal for cellulase production by the bacterium. Carboxymethyl cellulose at 1% in the medium induced relatively higher cellulase activity. Electrophoretic analysis of the ammonium sulfate precipitated proteins showed three prominent bands and whose molecular masses were estimated as 60, 69 and 75 kDa.  Cellulase produced by Cornybacterium lipophiloflavum could efficiently remove dyes or inks from the pulp incubated either in the form of intact growing cell or with clarified supernatant. The enzyme also removed starch from the fabric incubated with culture supernatant suggesting the potential use of Cornybacterium lipophiloflavum cellulase in industrial applications

Optimization of Culture Conditions for the Production of Extracellular Cellulase from Enterococcus pseudoavium

Bacteria that inhabit decaying vegetables were isolated and screened for the production of the enzyme, amylase. One species identified as Enterococcus pseudoavium exhibited relatively higher amylase activity of the bacterial species tested. The bacterium produced the highest extracellular amylase at 72 h in a medium containing starch (1% w/v), galactose (0.5% w/v), and peptone (0.5% w/v), at pH 7.0. The extracellular enzyme was partially purified and its starch hydrolyzing potential was evaluated. The organism when grown with paper pulp deinked the pulp completely just after four days of growth. The bacterial cells immobilized in sodium alginate beads when cultured with paper pulp could decolorize it within 4 days. The extracellular amylase produced by Enterococcus pseudoavium effectively deinked and decolorized paper pulp within 4 days after incubation. The enzyme efficiently removed the starch present in fabric and thus it could be very well used as an ingredient in commercial detergent

COVID -19 Predictions using Transfer Learning based Deep Learning Model with Medical Internet of Things

Early detection of COVID-19 may help medical expert for proper treatment plan and infection control. Internet of Things (IoT) has vital improvement in many areas including medical field. Deep learning also provide tremendous improvement in the field of medical. We have proposed a Transfer learning based deep learning model with medical Internet of Things for predicting COVID-19 from X-ray images. In the proposed method, the X ray images of patient are stored in cloud storage using internet for wide access. Then, the images are retrieved from cloud and Transfer learning based deep learning models namely VGG-16, Inception, Alexnet, Googlenet and Convolution neural Network models are applied on the X-rays images for predicting COVID 19, Normal and pneumonia classes. The pre-trained models helps to the effectiveness of deep learning accuracy and reduced the training time. The experimental analysis show that VGG -16 model gives accuracy of 99% for detecting COVID19 than other models

IMPACT OF PRESSURE ON THE PERFORMANCE OF PROTON EXCHANGE MEMBRANE FUEL CELL

The Proton Exchange Membrane (PEM) fuel cell performance not only depends on temperature but also depends on the operating pressure, which will increase the performance of the PEM fuel cell. The PEM fuel cell with serpentine flow field was modeled using Solidworks software and analyzed using ANSYS software. By analysis of three different pressures on the PEM fuel cell, and came to know that the optimum pressure gives the best performance. The peak power density occurs in the constant temperature of 323 K with the pressure of 2 bar

GREEN SYNTHESIS AND CHARACTERIZATION OF SILVER NANOPARTICLES FROM WITHANIA SOMNIFERA (L.) DUNAL

The metal nanoparticle synthesis is highly explored the field of nanotechnology. The biological methods seem to be more effective because of slowreduction rate and polydispersity of the final products. The main aim of this study is too the rapid and simplistic synthesis of silver nanoparticlesby Withania somnifera Linn. at room temperature. The exposure of reaction mixtures containing silver nitrate and dried leaf powder of W. somniferaresulted in reduction of metal ions within 5 minutes. The extracellular synthesized silver nanoparticles were characterized by ultraviolet-visible,infrared (IR) spectroscopy, X-ray diffraction studies, zeta potential, Fourier transform IR, and scanning electron microscopy. The antibacterial andantifungal studies showed significant activity as compared to their respective standards. From the results, W. somnifera sliver nanoparticle has attainedthe maximum antimicrobial against clinical pathogens and also seen very good stability of nanoparticle throughput processing. As we concluded, thistype of naturally synthesized sliver nanoparticle could be a better green revolution in medicinal chemistry.Keywords: Antimicrobial activity, Silver nanoparticles, Withania somnifera