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Scalar and Vector Triple Products01:06

Scalar and Vector Triple Products

Two vectors can be multiplied using a scalar product or a vector product. The resultant of a scalar product is scalar, while with vector products, the resultant is a vector. These rules of the scalar or vector product between two vectors can be applied to multiple vectors to obtain meaningful combinations. The scalar triple product is the dot product of a vector with the cross product of two vectors.
The scalar triple product is the dot product of a vector with the cross product of two vectors.
Parallel Processing01:20

Parallel Processing

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...
Vector Operations01:20

Vector Operations

Vectors are physical quantities that have both magnitude and direction. The vector operations include addition, subtraction, and scalar multiplication.
A vector multiplied by a scalar value is called scalar multiplication. The result obtained is a new vector with a different magnitude. If the scalar is positive, the direction of the vector remains the same, but if it is negative, the direction of the vector is reversed. For example, the product of the mass and velocity yields the momentum.
Scalar and Vectors01:22

Scalar and Vectors

In mechanics, commonly used terms like force, speed, velocity, and work can be classified as either scalar or vector quantities. A scalar is a physical quantity that can be described by its magnitude alone and does not require any directional components. Examples of scalar quantities are mass, area, and length.
Scalar quantities with the same physical units can be added or subtracted according to the usual algebra rules for numbers. For example, a class ending 10 min earlier than 50 min lasts...
Vectors in Engineering Applications01:30

Vectors in Engineering Applications

A steel beam supported by two identical cables provides a practical example of static equilibrium. The beam has a downward weight of 5000 N, while the two cables support it from opposite sides. Because the arrangement is symmetric, each cable makes the same angle of 60° with the horizontal beam and carries the same tension.In equilibrium, the beam remains completely at rest. This means that the total horizontal and vertical forces must both be zero. Each cable pulls along its own direction, so...
Vector or Cross Product01:17

Vector or Cross Product

Vector multiplication of two vectors yields a vector product, with the magnitude equal to the product of the individual vectors multiplied by the sine of the angle between both the vectors and the direction perpendicular to both the individual vectors. As there are always two directions perpendicular to a given plane, one on each side, the direction of the vector product is governed by the right-hand thumb rule.
Consider the cross product of two vectors. Imagine rotating the first vector about...

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Updated: Jun 14, 2026

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
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Multi-threaded vectorized distance matrix computation on the CELL/BE and x86/SSE2 architectures.

Adrianto Wirawan1, Chee Keong Kwoh, Bertil Schmidt

  • 1School of Computer Engineering, Nanyang Technological University, Singapore. adri0004@ntu.edu.sg

Bioinformatics (Oxford, England)
|March 30, 2010
PubMed
Summary

This study presents a new implementation for accelerating distance matrix computation in bioinformatics. Leveraging multi-core processors and vectorization, it achieves significant speed-ups for multiple sequence alignment tasks.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • High-Performance Computing

Background:

  • Multiple sequence alignment is crucial in bioinformatics.
  • Biological data is growing exponentially, demanding higher throughput.
  • Multi-core technologies offer potential for improved bioinformatics application performance.

Purpose of the Study:

  • To introduce an implementation accelerating distance matrix computation.
  • To optimize performance on homogeneous (x86) and heterogeneous (Cell Broadband Engine) multi-core systems.

Main Methods:

  • Utilized multi-core processors for parallel computation.
  • Implemented Single Instruction Multiple Data (SIMD) vectorization.
  • Developed C source code for distance matrix computation.

Main Results:

  • Achieved speed-ups of two orders of magnitude.
  • Demonstrated significant performance improvement over existing ClustalW implementations.
  • Successfully accelerated distance matrix computation on diverse multi-core architectures.

Conclusions:

  • The developed implementation significantly enhances throughput for multiple sequence alignment.
  • Multi-core processing and SIMD vectorization are effective strategies for accelerating bioinformatics computations.
  • This work addresses the need for high-throughput solutions in the face of increasing biological data volumes.