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Related Concept Videos

Parallel Resonance01:23

Parallel Resonance

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The parallel RLC circuit is an arrangement where the resistor (R), inductor (L), and capacitor (C) are all connected to the same nodes and, as a result, share the same voltage across them. The parallel RLC circuit is analyzed in terms of admittance (Y), which reflects the ease with which current can flow. The admittance is given by:
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Parallel Processing01:20

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The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
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Resistors are in parallel when one end of all the resistors are connected to a continuous wire of negligible resistance and the other end of all the resistors are also connected to one another through a continuous wire of negligible resistance. In the case of a parallel configuration, the potential drop across each resistor is the same. Current through each resistor can be found using Ohm’s law, I = V/R, where the voltage is constant across each resistor. The sum of the individual currents...
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Series and Parallel Capacitors01:14

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Capacitors, fundamental components in electronic circuits, can be connected in series and/or parallel configurations. Each configuration has different impacts on the overall behavior of the circuit.
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The parallel-axis theorem provides a convenient and quick method of finding the moment of inertia of an object about an axis parallel to the axis passing through its center of mass. Consider a thin rod as an example. There is a striking similarity between the process of finding the moment of inertia of a thin rod about an axis through its middle, where the center of mass lies, and about an axis through its end using the conventional method. In the conventional method, the concept of linear mass...
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Ubiquitin Chain Analysis by Parallel Reaction Monitoring
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Parallel MapReduce: Maximizing Cloud Resource Utilization and Performance Improvement Using Parallel Execution

Ahmed Abdulhakim Al-Absi1,2, Najeeb Abbas Al-Sammarraie2, Wael Mohamed Shaher Yafooz2

  • 1Department of Smart Computing, Kyungdong University, Global Campus, 46 4-gil, Gosung, Gangwondo 24764, Republic of Korea.

Biomed Research International
|November 13, 2018
PubMed
Summary
This summary is machine-generated.

Parallel MapReduce (PMR) improves cloud computing performance by enabling parallel processing for large data analysis. This novel framework significantly reduces processing time and enhances resource utilization compared to traditional MapReduce.

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Area of Science:

  • Cloud Computing
  • Big Data Analytics
  • Parallel Processing

Background:

  • Traditional MapReduce frameworks suffer performance issues due to sequential processing, leading to underutilized cloud resources.
  • Existing systems often fail to leverage multicore environments efficiently for large-scale data tasks.

Purpose of the Study:

  • Introduce a Parallel MapReduce (PMR) framework to enhance performance and cost-effectiveness in cloud computing.
  • Design and implement a novel parallel execution strategy for Map and Reduce worker nodes.

Main Methods:

  • Developed a parallel execution strategy for Map and Reduce workers to utilize multicore architectures.
  • Detailed makespan modeling and explained the working principles of the PMR framework.
  • Conducted comparative experiments using three biomedical applications: BLAST, CAP3, and DeepBind.

Main Results:

  • The PMR framework achieved significant makespan reductions: 38.92% for BLAST, 18.00% for CAP3, and 34.62% for DeepBind, compared to Hadoop.
  • Demonstrated efficient utilization of cloud resources through parallel execution of Map and Reduce functions.
  • Validated the robustness, cost-effectiveness, and scalability of the PMR platform.

Conclusions:

  • The PMR framework offers a robust, cost-effective, and scalable solution for diverse applications on cloud platforms.
  • Experimental results align well with theoretical makespan modeling, confirming the framework's effectiveness.
  • PMR enhances cloud computing efficiency by optimizing parallel processing for big data analysis.