ASSESSMENT OF LINUX' DATA PATH IMPLEMENTATIONS FOR DOWNLOAD AND STREAMING

2007 ◽  
Vol 17 (04) ◽  
pp. 465-481 ◽  
Author(s):  
PÅL HALVORSEN ◽  
TOM ANDERS DALSENG ◽  
CARSTEN GRIWODZ
Keyword(s):  
Operating Systems ◽  
Comprehensive Evaluation ◽  
Streaming Data ◽  
High Rate ◽  
Data Path ◽  
Power Budget ◽  
Data Movement ◽  
Technological Advances ◽  
Reduced Power Consumption ◽  
Streaming Systems

Distributed multimedia streaming systems are increasingly popular due to technological advances, and numerous streaming services are available today. On servers or proxy caches, there is a huge scaling challenge in supporting thousands of concurrent users that request delivery of high-rate, time-dependent data like audio and video, because this requires transfers of large amounts of data through several sub-systems within a streaming node. Unnecessary copy operations in the data path can therefore contribute significantly to the resource consumption of streaming operations. Despite previous research, off-the-shelf operating systems have only limited support for data paths that have been optimized for streaming. Additionally, system call overhead has grown with newer operating systems editions, adding to the cost of data movement. Frequently, it is argued that these issues can be ignored because of the continuing growth of CPU speeds. However, such an argument fails to take problems of modern streaming systems into account. The dissipation of heat generated by disks and high-end CPUs is a major problem of data centers, which would be alleviated if less power-hungry CPUs could be used. The power budget of mobile devices, which are increasingly used for streaming as well, is tight, and reduced power consumption an important issue. In this paper, we prove that these operations consume a large amount of resources, and we therefore revisit the data movement problem and provide a comprehensive evaluation of possible streaming data I/O paths in the Linux 2.6 kernel. We have implemented and evaluated several enhanced mechanisms and show how to provide support for more efficient memory usage and reduction of user/kernel space switches for content download and streaming applications. In particular, we are able to reduce the CPU usage by approximately 27% compared to the best approach without kernel modifications, by removing copy operations and system calls for a streaming scenario in which RTP headers must be added to stored data for sequence numbers and timing.

Outcomes of untreated frequent premature ventricular complexes with normal left ventricular function

Heart ◽  
2019 ◽  
Vol 105 (18) ◽  
pp. 1408-1413 ◽  
Author(s):  
Andrea K Y Lee ◽  
Jason Andrade ◽  
Nathaniel M Hawkins ◽  
George Alexander ◽  
Matthew T Bennett ◽  
...  
Keyword(s):  
Comprehensive Evaluation ◽  
Left Ventricular Systolic Dysfunction ◽  
Systolic Dysfunction ◽  
Ambulatory Monitoring ◽  
Left Ventricular ◽  
High Rate ◽  
Suppressive Therapy ◽  
Left Ventricular Ejection ◽  
Ventricular Ejection Fraction ◽  
Premature Ventricular Complexes

ObjectiveThe natural history of frequent premature ventricular complexes (PVCs) in association with preserved left ventricular ejection fraction (LVEF) is uncertain. The optimal management of this population is thus undefined. We studied the outcomes of untreated patients with frequent PVCs and preserved LVEF.MethodsThis cohort study prospectively evaluated consecutive patients from 2012 to 2017, with asymptomatic or minimally symptomatic frequent idiopathic PVCs (≥5% PVCs in 24 hours; normal LVEF; no cause identified on comprehensive evaluation). No suppressive therapy (ablation or antiarrhythmic drugs) were used and patients were followed with serial ambulatory ECG monitoring and echocardiography. The primary arrhythmic outcome was reduction in PVC burden to <1% on serial ambulatory monitoring. The primary echocardiographic outcome was a reduction of LVEF to <50%.ResultsOne hundred patients met inclusion criteria (mean age 51.8 years, 57% female) with a median PVC burden of 18.4%. Reduction to <1% PVCs occurred in 44 of 100 patients (44.0%) at a median of 15.4 months (range 2.6 to 64.3). Recurrence was uncommon (4/44, 9.1%). Four patients (4.3%) with a persistently elevated PVC burden developed left ventricular dysfunction (LVEF <50%) during the follow-up period at a range of 53–71 months. The initial PVC burden did not predict subsequent resolution (HR 1.00(0.97, 1.03); p=0.86).ConclusionsA strategy of active surveillance is appropriate for the majority of patients with frequent idiopathic PVCs in association with preserved LVEF, owing to the low risk of developing left ventricular systolic dysfunction and the high rate of spontaneous resolution.

An adaptive plan-based approach to integrating semantic streams with remote RDF data

2016 ◽  
Vol 43 (6) ◽  
pp. 852-865 ◽  
Author(s):  
Sejin Chun ◽  
Jooik Jung ◽  
Seungmin Seo ◽  
Wonwoo Ro ◽  
Kyong-Ho Lee
Keyword(s):  
Large Scale ◽  
Streaming Data ◽  
High Rate ◽  
Execution Plan ◽  
Adaptive Process ◽  
Multiple Strategies ◽  
Static Data ◽  
Rdf Data ◽  
Description Framework ◽  
Remote Data

To satisfy a user’s complex requirements, Resource Description Framework (RDF) Stream Processing (RSP) systems envision the fusion of remote RDF data with semantic streams, using common data models to query semantic streams continuously. While streaming data are changing at a high rate and are pushed into RSP systems, the remote RDF data are retrieved from different remote sources. With the growth of SPARQL endpoints that provide access to remote RDF data, RSP systems can easily integrate the remote data with streams. Such integration provides new opportunities for mixing static (or quasi-static) data with streams on a large scale. However, the current RSP systems do not offer any optimisation for the integration. In this article, we present an adaptive plan-based approach to efficiently integrate sematic streams with the static data from a remote source. We create a query execution plan based on temporal constraints among constituent services for the timely acquisition of remote data. To predict the change of remote sources in real time, we propose an adaptive process of detecting a source update, forecasting the update in the future, deciding a new plan to obtain remote data and reacting to a new plan. We extend a SPARQL query with operators for describing the multiple strategies of the proposed adaptive process. Experimental results show that our approach is more efficient than the conventional RSP systems in distributed settings.

scholarly journals Comparative analysis of the essential CPU scheduling algorithms

2021 ◽  
Vol 10 (5) ◽  
pp. 2742-2750
Author(s):  
Hoger K. Omar ◽  
Kamal H. Jihad ◽  
Shalau F. Hussein
Keyword(s):  
Comparative Analysis ◽  
Operating Systems ◽  
Waiting Times ◽  
Stable State ◽  
Scheduling Algorithms ◽  
High Rate ◽  
Cpu Scheduling ◽  
Significant Function ◽  
Cpu Utilization ◽  
Maximum Utilization

CPU scheduling algorithms have a significant function in multiprogramming operating systems. When the CPU scheduling is effective a high rate of computation could be done correctly and also the system will maintain in a stable state. As well as, CPU scheduling algorithms are the main service in the operating systems that fulfill the maximum utilization of the CPU. This paper aims to compare the characteristics of the CPU scheduling algorithms towards which one is the best algorithm for gaining a higher CPU utilization. The comparison has been done between ten scheduling algorithms with presenting different parameters, such as performance, algorithm’s complexity, algorithm’s problem, average waiting times, algorithm’s advantages-disadvantages, allocation way, etc. The main purpose of the article is to analyze the CPU scheduler in such a way that suits the scheduling goals. However, knowing the algorithm type which is most suitable for a particular situation by showing its full properties.

scholarly journals BINA SUASANA SEBAGAI UPAYA PENCEGAHAN PENYALAHGUNAAN NARKOBA DI SMA SEDERAJAT KOTA SURABAYA

IKESMA ◽  
2019 ◽  
Vol 15 (1) ◽  
pp. 12
Author(s):  
Jeni Martiyanti Fitriana
Keyword(s):  
Drug Abuse ◽  
Development Strategy ◽  
Teaching And Learning ◽  
High Rate ◽  
Human Beings ◽  
Promotion Strategy ◽  
Peer Counselors ◽  
Technological Advances ◽  
Health Promotion Strategy ◽  
High Level

The high rate of drug abuse in Indonesia has spread to high school adolescents. 183 million people consume marijuana, 35 million opioids, 37 million amphetamines and drug stimuli, 22 million ecstasy, 18 million opiates, 17 million kokai (BNN, 2017). East Java is one of the provinces where the population is at risk of experiencing drug abuse. Surabaya occupies a position as a metrapolitan city which has a high level of modernity both from technology and infrastructure. The high level of population and technological advances have made this region vulnerable to drug abuse. The role of the School is to develop capabilities and shape dignified national character and civilization in order to educate the life of the nation and the state aimed at developing potential students to become human beings who believe and fear God Almighty, noble, healthy, knowledgeable, capable, creative , be independent, and become a democratic and responsible citizen. For this reason, schools have an important role related to the prevention of drug abuse. Based on the data and policies obtained, the researchers aimed to identify the atmosphere development strategy in schools in an effort to prevent drug abuse in schools. This research was conducted using in-depth interviews with 14 informants who held positions as teachers. The research conducted will later use the WHO 1984 Health Promotion Strategy approach by using atmosphere development variables. The results of the study stated that the community development activities carried out in North Surabaya's high schools were in the form of peer counselors, teaching and learning activities, inspection, urine tests and counseling facilities.

Streaming Data Movement for Real-Time Image Analysis

2009 ◽  
Vol 62 (1) ◽  
pp. 29-42 ◽  
Author(s):  
Abelardo López-Lagunas ◽  
Sek Chai
Keyword(s):  
Image Analysis ◽  
Real Time ◽  
Streaming Data ◽  
Data Movement ◽  
Real Time Image ◽  
Time Image

scholarly journals A Survey of ReRAM-Based Architectures for Processing-In-Memory and Neural Networks

2018 ◽  
Vol 1 (1) ◽  
pp. 75-114 ◽  
Author(s):  
Sparsh Mittal
Keyword(s):  
Machine Learning ◽  
Random Access ◽  
Random Access Memory ◽  
Resistive Random Access Memory ◽  
Low Energy ◽  
Power Budget ◽  
Access Memory ◽  
Computing Systems ◽  
Data Movement ◽  
Similarities And Differences

As data movement operations and power-budget become key bottlenecks in the design of computing systems, the interest in unconventional approaches such as processing-in-memory (PIM), machine learning (ML), and especially neural network (NN)-based accelerators has grown significantly. Resistive random access memory (ReRAM) is a promising technology for efficiently architecting PIM- and NN-based accelerators due to its capabilities to work as both: High-density/low-energy storage and in-memory computation/search engine. In this paper, we present a survey of techniques for designing ReRAM-based PIM and NN architectures. By classifying the techniques based on key parameters, we underscore their similarities and differences. This paper will be valuable for computer architects, chip designers and researchers in the area of machine learning.

Psychiatric and psycho-social characteristics of suicide completers: a comprehensive evaluation of psychiatric case records and postmortem findings

2014 ◽  
Vol 32 (2) ◽  
pp. 167-176 ◽  
Author(s):  
J. Kielty ◽  
A. van Laar ◽  
M. Davoren ◽  
L. Conlon ◽  
A. Hillick ◽  
...  
Keyword(s):  
Mental Health ◽  
Mental Health Services ◽  
Health Services ◽  
Comprehensive Evaluation ◽  
High Rate ◽  
Social Characteristics ◽  
Common Diagnosis ◽  
History Of ◽  
Suicide Completers ◽  
History Of Depression

ObjectivesTo explore the demographic, psychosocial and clinical characteristics of individuals known to the mental health services, who died by probable suicide in the West of Ireland.MethodsPostmortem reports between January 2006 and May 2012 detailed 153 individuals who died by probable suicide, 58 of whom attended the mental health services. Relevant socio-demographic and clinical data was extracted from individuals’ lifetime case notes.ResultsRecurrent depressive disorder (44%) was the most common diagnosis and hanging the most common method of death (58%). Of individuals who died by hanging, 79% previously attempted suicide by the same method. For individuals with a documented history of depression, only 32% had antidepressants detected in their toxicology reports. Similarly, only one individual (20%) with schizophrenia had antipsychotics detected in their toxicology report.ConclusionsIndividuals who died by probable suicide, most commonly died by hanging and drowning; with previous attempts of hanging particularly prevalent in the group who subsequently died by hanging. At the time of death, less than one-third of individuals according to toxicology reports were taking the medication that was last prescribed to them by the mental health services suggesting a high rate of treatment non-concordance in individuals who died by probable suicide.

scholarly journals Post-Intensive Care Syndrome Associated With Coronavirus Disease: A Single Institutional Study In Tokyo

2021 ◽  
Author(s):  
Aya Banno ◽  
Toru Hifumi ◽  
Yuta Takahashi ◽  
Mitsuhito Soh ◽  
Ayako Sakaguchi ◽  
...  
Keyword(s):  
Intensive Care ◽  
Comprehensive Evaluation ◽  
Invasive Mechanical Ventilation ◽  
Depression Scale ◽  
High Rate ◽  
Impact Of Event Scale ◽  
Event Scale ◽  
Icu Discharge ◽  
The Impact ◽  
Post Intensive Care Syndrome

Abstract Background: The occurrence of post-intensive care syndrome (PICS) in critically ill patients with coronavirus disease (COVID-19) remains unclear. This study aimed to investigate the physical, mental, and cognitive components of PICS in intensive care unit (ICU)-treated COVID-19 survivors.Methods: This prospective cohort study enrolled patients with COVID-19 who were treated in the ICU of a single institution between March 19, 2020 and April 30, 2020. A survey was sent by postal mail at 4 and 6 months after ICU discharge. The questionnaire comprised the post-COVID-19 functional status (PCFS) scale and the modified medical research council dyspnea scale (mMRC) for assessing physical PICS; the impact of event scale-revised (IES-R) and the hospital anxiety and depression scale (HADS) for assessing mental PICS; and self-assessment questions for concentration, memory, and forgetfulness for assessing cognitive PICS. Physical PICS was defined by a PCFS or mMRC score ≥1. Mental PICS was defined by an IES-R score ≥25 or if the HADS score for anxiety or depression components was ≥8. Cognitive PICS was defined according to patient complaints of deterioration in concentration, memory, or forgetfulness. The primary outcome was PICS occurrence at 4 months. Moreover, we assessed the co-occurrence of the three PICS components.Results: Twenty patients consented to participate in the study and responded to the survey. The median age was 57.5 years, and 80% of the patients were male; moreover, 50%, 55%, and 80% lived alone, were married, and were employed/self-employed before hospitalization, respectively. During ICU stay, 80%, 75%, and 25% received invasive mechanical ventilation, systemic steroids, and continuous benzodiazepine, respectively. Delirium occurred in 40% of patients. The median days of ICU and hospital stay were 6 and 21, respectively. Physical, mental, and cognitive PICS occurred in 14 (78%), 9 (45%), and 11 (55%) patients, respectively. There were 16 (80%) and 8 (40%) patients with at least one and all PICS components, respectively.Conclusions: Our findings revealed a high rate of PICS in COVID-19 survivors. Long-term and comprehensive evaluation of all three PICS components is crucial for providing appropriate care to these patients.

scholarly journals CGAcc: A Compressed Sparse Row Representation-Based BFS Graph Traversal Accelerator on Hybrid Memory Cube

Electronics ◽  
2018 ◽  
Vol 7 (11) ◽  
pp. 307 ◽  
Author(s):  
Cheng Qian ◽  
Bruce Childers ◽  
Libo Huang ◽  
Hui Guo ◽  
Zhiying Wang
Keyword(s):  
Comprehensive Evaluation ◽  
Random Access ◽  
Large Data ◽  
Main Memory ◽  
Graph Representation ◽  
Data Movement ◽  
Access Latency ◽  
Hybrid Memory ◽  
Graph Traversal ◽  
Compressed Sparse Row

Graph traversal is widely used in map routing, social network analysis, causal discovery and many more applications. Because it is a memory-bound process, graph traversal puts significant pressure on the memory subsystem. Due to poor spatial locality and the increasing size of today’s datasets, graph traversal consumes an ever-larger part of application execution time. One way to mitigate this cost is memory prefetching, which issues requests from the processor to the memory in anticipation of needing certain data. However, traditional prefetching does not work well for graph traversal due to data dependencies, the parallel nature of graphs and the need to move vast amounts of data from memory to the caches. In this paper, we propose a compressed sparse row representation-based graph accelerator on the Hybrid Memory Cube (HMC), called CGAcc. CGAcc combines Compressed Sparse Row (CSR) graph representation with in-memory prefetching and processing to improve the performance of graph traversal. Our approach integrates the prefetching and processing in the logic layer of a 3D stacked Dynamic Random-Access Memory (DRAM) architecture, based on Micron’s HMC. We selected HMC to implement CGAcc because it can provide quite high bandwidth and low access latency. Furthermore, this device has multiple DRAM layers connected to internal logic to control memory access and perform rudimentary computation. Using the CSR representation, CGAcc deploys prefetchers in the HMC to exploit the short transaction latency between the logic and DRAM layers. By doing this, it can also avoid large data movement costs. In the runtime, CGAcc pipelines the prefetching to fetch data from DRAM arrays to improve memory-level parallelism. To further reduce the access latency, several optimized internal caches are also introduced to hold the prefetched data to be Processed In-Memory (PIM). A comprehensive evaluation shows the effectiveness of CGAcc. Experimental results showed that, compared to a conventional HMC main memory equipped with a stream prefetcher, CGAcc achieved an average 3.51× speedup with moderate hardware cost.

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