scholarly journals Debugging of Performance Degradation in Distributed Requests Handling Using Multilevel Trace Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Naser Ezzati-Jivan ◽  
Houssem Daoud ◽  
Michel R. Dagenais
Keyword(s):  
Distributed Systems ◽  
High Performance ◽  
Performance Metrics ◽  
Response Times ◽  
Performance Degradation ◽  
Multidimensional Data ◽  
Trace Data ◽  
Root Cause ◽  
User Space ◽  
Performance Problems

Root cause identification of performance degradation within distributed systems is often a difficult and time-consuming task, yet it is crucial for maintaining high performance. In this paper, we present an execution trace-driven solution that reduces the efforts required to investigate, debug, and solve performance problems found in multinode distributed systems. The proposed approach employs a unified analysis method to represent trace data collected from the user-space level to the hardware level of involved nodes, allowing for efficient and effective root cause analysis. This solution works by extracting performance metrics and state information from trace data collected at user-space, kernel, and network levels. The multisource trace data is then synchronized and structured in a multidimensional data store, which is designed specifically for this kind of data. A posteriori analysis using a top-down approach is then used to investigate performance problems and detect their root causes. In this paper, we apply this generic framework to analyze trace data collected from the execution of the web server, database server, and application servers in a distributed LAMP (Linux, Apache, MySQL, and PHP) Stack. Using industrial level use cases, we show that the proposed approach is capable of investigating the root cause of performance issues, addressing unusual latency, and improving base latency by 70%. This is achieved with minimal tracing overhead that does not significantly impact performance, as well as O log   n query response times for efficient analysis.

Nanoprobe Characterization of Soft SRAM bit Fails in Advanced Technologies

2019 ◽  
Author(s):  
Satish Kodali ◽  
Chen Zhe ◽  
Chong Khiam Oh
Keyword(s):  
Yield Loss ◽  
Performance Metrics ◽  
Root Cause ◽  
Advanced Technologies ◽  
Capacitance Voltage ◽  
Defect Localization ◽  
Cv Measurements ◽  
Lightly Doped Drain ◽  
Dc Characterization

Abstract Nanoprobing is one of the key characterization techniques for soft defect localization in SRAM. DC transistor performance metrics could be used to identify the root cause of the fail mode. One such case report where nanoprobing was applied to a wafer impacted by significant SRAM yield loss is presented in this paper where standard FIB cross-section on hard fail sites and top down delayered inspection did not reveal any obvious defects. The authors performed nanoprobing DC characterization measurements followed by capacitance-voltage (CV) measurements. Two probe CV measurement was then performed between the gate and drain of the device with source and bulk floating. The authors identified valuable process marginality at the gate to lightly doped drain overlap region. Physical characterization on an inline split wafer identified residual deposits on the BL contacts potentially blocking the implant. Enhanced cleans for resist removal was implemented as a fix for the fail mode.

Exposure aboard high-performance Marine craft increases musculoskeletal pain and lowers contemporary work capacity of the occupants

2020 ◽  
pp. 147509022098146
Author(s):  
Manudul Pahansen de Alwis ◽  
Riccardo LoMartire ◽  
Björn O Äng ◽  
Karl Garme
Keyword(s):  
Neck Pain ◽  
Musculoskeletal Pain ◽  
High Performance ◽  
Occupational Exposures ◽  
Performance Degradation ◽  
Individual Risk ◽  
Work Related ◽  
Lower Back ◽  
And Performance ◽  
Exposure Conditions

High-Performance Marine Craft (HPMC) occupants are currently being investigated for various psychophysical impairments degrading work performance postulating that these deteriorations are related to their occupational exposures. However, scientific evidence for this is lacking and the association of exposure conditions aboard HPMC with adverse health and performance effects is unknown. Therefore, the study estimates the prevalence of musculoskeletal pain (MSP) among HPMC occupants and the association of their work exposure with MSP and performance degradation. It also presents a criterion for evaluating the self-reported exposure severity aboard three different types of mono-hull HPMC; displacement, semi-displacement and planing, on a par with the available standard criteria for objectively measurable exposures. Furthermore, another criterion is proposed to assess the performance-degradation of HPMC occupants based on self-reported fatigue symptoms and MSP. Swedish Coast Guard HPMC occupants were surveyed for MSP, fatigue symptoms as well as for work-related and individual risk indicators using a validated web-based questionnaire. Prevalence of MSP and performance-degradation during the past 12 months were assessed and presented as a percentage of the sample. Associations of exposure conditions aboard HPMC with MSP and performance-capacity were systematically evaluated using multiple logistic regression models and expressed as odds ratio (OR). Prevalence of MSP was 72% among which lower back pain was the most prevalent (46%) followed by neck pain (29%) and shoulder pain (23%) while 29% with degraded performance. Exposure to severe conditions aboard semi-displacement craft was associated with lower back (OR = 2.3) and shoulder (OR = 2.6) pain while severe conditions aboard planing craft with neck pain (OR = 2.3) and performance-degradation (OR = 2.6). MSP is common among Swedish coast guards. Severe exposure conditions aboard HPMC are significantly associated with both MSP and performance-degradation. The spine and shoulders are the most susceptible to work-related MSP among HPMC occupants which should be targeted in work-related preventive and corrective measures.

Persistent memory hash indexes

2021 ◽  
Vol 14 (5) ◽  
pp. 785-798
Author(s):  
Daokun Hu ◽  
Zhiwen Chen ◽  
Jianbing Wu ◽  
Jianhua Sun ◽  
Hao Chen
Keyword(s):  
Future Development ◽  
High Performance ◽  
Performance Metrics ◽  
Comprehensive Evaluation ◽  
State Of The Art ◽  
Hash Tables ◽  
Trade Offs ◽  
Depth Analysis ◽  
Persistent Memory ◽  
Memory Modules

Persistent memory (PM) is increasingly being leveraged to build hash-based indexing structures featuring cheap persistence, high performance, and instant recovery, especially with the recent release of Intel Optane DC Persistent Memory Modules. However, most of them are evaluated on DRAM-based emulators with unreal assumptions, or focus on the evaluation of specific metrics with important properties sidestepped. Thus, it is essential to understand how well the proposed hash indexes perform on real PM and how they differentiate from each other if a wider range of performance metrics are considered. To this end, this paper provides a comprehensive evaluation of persistent hash tables. In particular, we focus on the evaluation of six state-of-the-art hash tables including Level hashing, CCEH, Dash, PCLHT, Clevel, and SOFT, with real PM hardware. Our evaluation was conducted using a unified benchmarking framework and representative workloads. Besides characterizing common performance properties, we also explore how hardware configurations (such as PM bandwidth, CPU instructions, and NUMA) affect the performance of PM-based hash tables. With our in-depth analysis, we identify design trade-offs and good paradigms in prior arts, and suggest desirable optimizations and directions for the future development of PM-based hash tables.

scholarly journals Biosensing with optical fiber gratings

Nanophotonics ◽  
2017 ◽  
Vol 6 (4) ◽  
pp. 663-679 ◽  
Author(s):  
Francesco Chiavaioli ◽  
Francesco Baldini ◽  
Sara Tombelli ◽  
Cosimo Trono ◽  
Ambra Giannetti
Keyword(s):  
Optical Fiber ◽  
Single Molecule ◽  
High Performance ◽  
Performance Metrics ◽  
Surrounding Medium ◽  
Remote Measurement ◽  
Fiber Gratings ◽  
Practical Applications ◽  
Technology Platforms ◽  
Optical Fiber Gratings

AbstractOptical fiber gratings (OFGs), especially long-period gratings (LPGs) and etched or tilted fiber Bragg gratings (FBGs), are playing an increasing role in the chemical and biochemical sensing based on the measurement of a surface refractive index (RI) change through a label-free configuration. In these devices, the electric field evanescent wave at the fiber/surrounding medium interface changes its optical properties (i.e. intensity and wavelength) as a result of the RI variation due to the interaction between a biological recognition layer deposited over the fiber and the analyte under investigation. The use of OFG-based technology platforms takes the advantages of optical fiber peculiarities, which are hardly offered by the other sensing systems, such as compactness, lightness, high compatibility with optoelectronic devices (both sources and detectors), and multiplexing and remote measurement capability as the signal is spectrally modulated. During the last decade, the growing request in practical applications pushed the technology behind the OFG-based sensors over its limits by means of the deposition of thin film overlays, nanocoatings, and nanostructures, in general. Here, we review efforts toward utilizing these nanomaterials as coatings for high-performance and low-detection limit devices. Moreover, we review the recent development in OFG-based biosensing and identify some of the key challenges for practical applications. While high-performance metrics are starting to be achieved experimentally, there are still open questions pertaining to an effective and reliable detection of small molecules, possibly up to single molecule, sensing in vivo and multi-target detection using OFG-based technology platforms.

scholarly journals ROA-CONS: Raccoon Optimization for Job Scheduling

Symmetry ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 2270
Author(s):  
Sina Zangbari Koohi ◽  
Nor Asilah Wati Abdul Hamid ◽  
Mohamed Othman ◽  
Gafurjan Ibragimov
Keyword(s):  
User Satisfaction ◽  
High Performance ◽  
Job Scheduling ◽  
Response Times ◽  
Scheduling Algorithm ◽  
Desktop Computer ◽  
Network Heterogeneity ◽  
Parallel Job Scheduling ◽  
Scheduling Method ◽  
And Performance

High-performance computing comprises thousands of processing powers in order to deliver higher performance computation than a typical desktop computer or workstation in order to solve large problems in science, engineering, or business. The scheduling of these machines has an important impact on their performance. HPC’s job scheduling is intended to develop an operational strategy which utilises resources efficiently and avoids delays. An optimised schedule results in greater efficiency of the parallel machine. In addition, processes and network heterogeneity is another difficulty for the scheduling algorithm. Another problem for parallel job scheduling is user fairness. One of the issues in this field of study is providing a balanced schedule that enhances efficiency and user fairness. ROA-CONS is a new job scheduling method proposed in this paper. It describes a new scheduling approach, which is a combination of an updated conservative backfilling approach further optimised by the raccoon optimisation algorithm. This algorithm also proposes a technique of selection that combines job waiting and response time optimisation with user fairness. It contributes to the development of a symmetrical schedule that increases user satisfaction and performance. In comparison with other well-known job scheduling algorithms, the simulation assesses the effectiveness of the proposed method. The results demonstrate that the proposed strategy offers improved schedules that reduce the overall system’s job waiting and response times.

scholarly journals Algorithm for auto annotation of scanned documents based on subregion tiling and shallow networks

2021 ◽  
Author(s):  
Komuravelli Prashanth ◽  
Kalidas Yeturu
Keyword(s):  
Neural Network ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
High Performance ◽  
Performance Metrics ◽  
Training Data ◽  
Mean Average Precision ◽  
Average Precision ◽  
Squared Error Loss Function ◽  
Scanned Documents

<div>There are millions of scanned documents worldwide in around 4 thousand languages. Searching for information in a scanned document requires a text layer to be available and indexed. Preparation of a text layer requires recognition of character and sub-region patterns and associating with a human interpretation. Developing an optical character recognition (OCR) system for each and every language is a very difficult task if not impossible. There is a strong need for systems that add on top of the existing OCR technologies by learning from them and unifying disparate multitude of many a system. In this regard, we propose an algorithm that leverages the fact that we are dealing with scanned documents of handwritten text regions from across diverse domains and language settings. We observe that the text regions have consistent bounding box sizes and any large font or tiny font scenarios can be handled in preprocessing or postprocessing phases. The image subregions are smaller in size in scanned text documents compared to subregions formed by common objects in general purpose images. We propose and validate the hypothesis that a much simpler convolution neural network (CNN) having very few layers and less number of filters can be used for detecting individual subregion classes. For detection of several hundreds of classes, multiple such simpler models can be pooled to operate simultaneously on a document. The advantage of going by pools of subregion specific models is the ability to deal with incremental addition of hundreds of newer classes over time, without disturbing the previous models in the continual learning scenario. Such an approach has distinctive advantage over using a single monolithic model where subregions classes share and interfere via a bulky common neural network. We report here an efficient algorithm for building a subregion specific lightweight CNN models. The training data for the CNN proposed, requires engineering synthetic data points that consider both pattern of interest and non-patterns as well. We propose and validate the hypothesis that an image canvas in which optimal amount of pattern and non-pattern can be formulated using a means squared error loss function to influence filter for training from the data. The CNN hence trained has the capability to identify the character-object in presence of several other objects on a generalized test image of a scanned document. In this setting some of the key observations are in a CNN, learning a filter depends not only on the abundance of patterns of interest but also on the presence of a non-pattern context. Our experiments have led to some of the key observations - (i) a pattern cannot be over-expressed in isolation, (ii) a pattern cannot be under-xpressed as well, (iii) a non-pattern can be of salt and pepper type noise and finally (iv) it is sufficient to provide a non-pattern context to a modest representation of a pattern to result in strong individual sub-region class models. We have carried out studies and reported \textit{mean average precision} scores on various data sets including (1) MNIST digits(95.77), (2) E-MNIST capital alphabet(81.26), (3) EMNIST small alphabet(73.32) (4) Kannada digits(95.77), (5) Kannada letters(90.34), (6) Devanagari letters(100) (7) Telugu words(93.20) (8) Devanagari words(93.20) and also on medical prescriptions and observed high-performance metrics of mean average precision over 90%. The algorithm serves as a kernel in the automatic annotation of digital documents in diverse scenarios such as annotation of ancient manuscripts and hand-written health records.</div>

Utilizing recycled LiFePO4 from batteries in combination with B@C3N4 and CuFe2O4 as sustainable nano-junctions for high performance degradation of atenolol

Chemosphere ◽  
2018 ◽  
Vol 209 ◽  
pp. 457-469 ◽  
Author(s):  
Amit Kumar ◽  
Anu Kumari ◽  
Gaurav Sharma ◽  
Mu. Naushad ◽  
Tansir Ahamad ◽  
...  
Keyword(s):  
High Performance ◽  
Performance Degradation
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