Adaptive energy minimization of OpenMP parallel applications on many-core systems

Energy minimization of parallel applications is an emerging challenge for current and future generations of many-core computing systems. In this paper, we propose a novel and scalable energy minimization approach that suitably applies DVFS in the sequential part and jointly considers DVFS and dynamic core allocations in the parallel part. Fundamental to this approach is an iterative learning based control algorithm that adapt the voltage/frequency scaling and core allocations dynamically based on workload predictions and is guided by the CPU performance counters at regular intervals. The adaptation is facilitated through performance annotations in the application codes, defined in a modified OpenMP runtime library. The proposed approach is validated on an Intel Xeon E5-2630 platform with up to 24 CPUs running NAS parallel benchmark applications. We show that our proposed approach can effectively adapt to different architecture and core allocations and minimize energy consumption by up to 17% compared to the existing approaches for a given performance requirement

A fitoplankton produktivitása és diverzitása pikoplankton dominanciájú vízterekben = Productivity and diversity of phytoplankton in picoplankton dominated water bodies

Feltártuk a bakteriális méretű algákban (pikoplankton) gazdag turbid vizekben a pikoplankton tömegének, összetételének, dinamikájának és fotoszintézisének alapvető jellemzőit. A fitoplankton tömege alapján ezek a tavak hipertrófok (a-klorofill>100 micro g/l), ugyanakkor a fénylimitáció miatt elsődleges termelésük a vártnál alacsonyabb volt. Vizükben a pikoplankton abundanciája egy-két nagyságrenddel nagyobb volt, mint más tavakban, télen a pikoeukarióták nyáron a pikocianobaktériumok domináltak a Balatonhoz hasonlóan. A Balatonban a pikoalgák részesedése az összes produkcióból 23-54% között volt. Megállapítottuk, hogy a pikoplankton abundancia folyó vizekben egy nagyságrenddel kisebb, mint tavakban, azonos fitoplankton biomassza esetén. A Balatonból egy új pikoeukarióta, a szikes tavakból egy új pikocianobaktérium morfotaxont írtunk le. A természetes pikoplankton együttesek molekuláris vizsgálata bizonyította, hogy a Pannon Biogeográfiai Régió víztereinek Synechococcus flórája igen gazdag és diverz. Az izolált pikocianobaktérium törzsek genetikai karakterizálása is ezt az eredményt támasztotta alá. A Böddi-székből izolált törzsek a pikofitoplankton klád egy új, eddig tenyésztésbe nem vont csoportját képviselik. Pikoeukarióta és pikocianobaktérium törzsek ökofiziológiai vizsgálatával bizonyítottuk, hogy a Duna-Tisza közi szikes tavakban (és minden bizonnyal más tavakban is) a fény és a hőmérséklet változása együtt szabályozza a pikoalgák szezonális szukcesszióját. | The abundance, composition, dynamics and photosynthesis of the bacterium-sized algae (picoplankton) has been studied in Hungarian turbid waters, where the abundance of picoplankton is one or two order of magnitude higher than in other lakes. Based on the phytoplankton biomass these ponds were hypertrophic (chlorophyll-a > 100 micro g/l), however the primary production was lower than expected due to light limitation. Picoeukaryotes dominate in winter, while picocyanobacteria dominate in summer, similarly to Lake Balaton, where the proportion of the picoplankton in the total primary production was 23 and 54%. It has been stated, that the picoplankton abundance in running waters was one order of magnitude lower, than in shallow lakes having the same trophic state. One new eukaryotic and one cyanobacterial picoalgal morphotaxa has been described. The molecular characterization of phytoplankton assemblages showed that the Synechococcus flora of the Pannon Biogeographic Region is very diverse. The molecular identification of isolated picocyanobacterial strains also confirmed these results. The picocyanobacterial strains isolated from Böddi-szék pond formed a new, distinct group inside the picophytoplankton clade. The ecophysiological studies of picoeukaryotic and picocyanobacterial strains showed that the light and temperature control together the seasonal succession of the picoplankton in turbid soda lakes in the Danube-Tisza Interfluve (and presumably in other shallow lakes)

Machine Learning for Run-Time Energy Optimisation in Many-Core Systems

In recent years, the focus of computing has moved away from performance-centric serial computation to energy-efficient parallel computation. This necessitates run-time optimisation techniques to address the dynamic resource requirements of different applications on many-core architectures. In this paper, we report on intelligent run-time algorithms which have been experimentally validated for managing energy and application performance in many-core embedded system. The algorithms are underpinned by a cross-layer system approach where the hardware, system software and application layers work together to optimise the energy-performance trade-off. Algorithm development is motivated by the biological process of how a human brain (acting as an agent) interacts with the external environment (system) changing their respective states over time. This leads to a pay-off for the action taken, and the agent eventually learns to take the optimal/best decisions in future. In particular, our online approach uses a model-free reinforcement learning algorithm that suitably selects the appropriate voltage-frequency scaling based on workload prediction to meet the applications’ performance requirements and achieve energy savings of up to 16% in comparison to state-of-the-art-techniques, when tested on four ARM A15 cores of an ODROID-XU3 platform

Learning-based runtime management of energy-efficient and reliable many-core systems

This paper highlights and demonstrates our research works to date addressing the energy-efficiency and reliability challenges of many-core systems through intelligent runtime management algorithms. The algorithms are implemented through cross-layer interactions between the three layers: application, runtime and hardware, forming our core theme of working together. The annotated application tasks communicate the performance, energy or reliability requirements to the runtime. With such requirements, the runtime exercises the hardware through various control knobs and gets the feedback of these controls through the performance monitors. The aim is to learn the best possible hardware controls during runtime to achieve energy-efficiency and improved reliability, while meeting the specified application requirements

Thermal-aware adaptive energy minimization of open MP parallel applications

Energy minimization of parallel applications considering thermal distributions among the processor cores is an emerging challenge for current and future generations of many-core computing systems. This paper proposes an adaptive energy minimization approach that hierarchically applies dynamic voltage\slash frequency scaling (DVFS), thread-to-core affinity and dynamic concurrency controls (DCT) to address this challenge. The aim is to minimize the energy consumption and achieve balanced thermal distributions among cores, thereby improving the lifetime reliability of the system, while meeting a specified power budget requirement. Fundamental to this approach is an iterative learning-based control algorithm that adapts the VFS and core allocations dynamically based on the CPU workloads and thermal distributions of the processor cores, guided by the CPU performance counters at regular intervals. The adaptation is facilitated through modified OpenMP library-based power budget annotations. The proposed approach is extensively validated on an Intel Xeon E5-2630 platform with up to 12 CPUs running NAS parallel benchmark applications

A lactate and formate transporter in the intraerythrocytic malaria parasite, Plasmodium falciparum

The intraerythrocytic malaria parasite relies primarily on glycolysis to fuel its rapid growth and reproduction. The major byproduct of this metabolism, lactic acid, is extruded into the external medium. In this study, we show that the human malaria parasite Plasmodium falciparum expresses at its surface a member of the microbial formate-nitrite transporter family (PfFNT), which, when expressed in Xenopus laevis oocytes, transports both formate and lactate. The transport characteristics of PfFNT in oocytes (pH-dependence, inhibitor-sensitivity and kinetics) are similar to those of the transport of lactate and formate across the plasma membrane of mature asexual-stage P. falciparum trophozoites, consistent with PfFNT playing a major role in the efflux of lactate and hence in the energy metabolism of the intraerythrocytic parasite.This work was supported by grants from the Australian National Health and Medical Research Council (NHMRC; 316933 and 525428 to K.K. and 1007035 to R.E.M.), and by the L’Ore´al Australia For Women in Science programme (R.E.M.). A.M.L. was supported by an NHMRC Overseas Biomedical Fellowship (585519) and R.E.M. was supported by NHMRC Australian Biomedical Fellowships (520320 and 1053082)

Learning transfer-based adaptive energy minimization in embedded systems

Embedded systems execute applications with different performance requirements. These applications exercise the hardware differently depending on the types of computation being carried out, generating varying workloads with time. We will demonstrate that energy minimization with such workload and performance variations within (intra) and across (inter) applications is particularly challenging. To address this challenge we propose an online energy minimization approach, capable of minimizing energy through adaptation to these variations. At the core of the approach is an initial learning through reinforcement learning algorithm that suitably selects the appropriate voltage/frequency scalings (VFS) based on workload predictions to meet the applications’ performance requirements. The adaptation is then facilitated and expedited through learning transfer, which uses the interaction between the system application, runtime and hardware layers to adjust the power control levers. The proposed approach is implemented as a power governor in Linux and validated on an ARM Cortex-A8 running different benchmark applications. We show that with intra- and inter-application variations, our proposed approach can effectively minimize energy consumption by up to 33% compared to existing approaches. Scaling the approach further to multi-core systems, we also show that it can minimize energy by up to 18% with 2X reduction in the learning time when compared with a recently reported approach

Oropharyngeal Microbiome Profiled at Admission is Predictive of the Need for Respiratory Support Among COVID-19 Patients [preprint]

The clinical course of infection due to respiratory viruses such as Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV2), the causative agent of Coronavirus Disease 2019 (COVID-19) is thought to be influenced by the community of organisms that colonizes the upper respiratory tract, the oropharyngeal microbiome. In this study, we examined the oropharyngeal microbiome of suspected COVID-19 patients presenting to the Emergency Department and an inpatient COVID-19 unit with symptoms of acute COVID-19. Of 115 enrolled patients, 74 were confirmed COVID-19+ and 50 had symptom duration of 14 days or less; 38 acute COVID-19+ patients (76%) went on to require respiratory support. Although no microbiome features were found to be significantly different between COVID-19+ and COVID-19-patients, when we conducted random forest classification modeling (RFC) to predict the need of respiratory support for the COVID-19+ patients our analysis identified a subset of organisms and metabolic pathways whose relative abundance, when combined with clinical factors (such as age and Body Mass Index), was highly predictive of the need for respiratory support (F1 score 0.857). Microbiome Multivariable Association with Linear Models (MaAsLin2) analysis was then applied to the features identified as predicative of the need for respiratory support by the RFC. This analysis revealed reduced abundance of Prevotella salivae and metabolic pathways associated with lipopolysaccharide and mycolic acid biosynthesis to be the strongest predictors of patients requiring respiratory support. These findings suggest that composition of the oropharyngeal microbiome in COVID-19 may play a role in determining who will suffer from severe disease manifestations. Importance: The microbial community that colonizes the upper airway, the oropharyngeal microbiome, has the potential to affect how patients respond to respiratory viruses such as SARS-CoV2, the causative agent of COVID-19. In this study, we investigated the oropharyngeal microbiome of COVID-19 patients using high throughput DNA sequencing performed on oral swabs. We combined patient characteristics available at intake such as medical comorbidities and age, with measured abundance of bacterial species and metabolic pathways and then trained a machine learning model to determine what features are predicative of patients needing respiratory support in the form of supplemental oxygen or mechanical ventilation. We found that decreased abundance of some bacterial species and increased abundance of pathways associated bacterial products biosynthesis was highly predictive of needing respiratory support. This suggests that the oropharyngeal microbiome affects disease course in COVID-19 and could be targeted for diagnostic purposes to determine who may need oxygen, or therapeutic purposes such as probiotics to prevent severe COVID-19 disease manifestations

Current diagnosis and treatment algorithms for anal incontinence

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/75666/1/j.1464-410X.2006.06307.x.pd