Multi-core Processing
Multi-core Processing - Related Videos
22:10
Multi-target Parallel Processing Approach for Gene-to-structure Determination of the Influenza Polymerase PB2 Subunit
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Cited by 3 •
2013
Structure-based drug design plays an important role in drug development. Pursuing multiple targets in parallel greatly increases the chance of success for lead discovery. The following article highlights how the Seattle Structural Genomics Center for Infectious Disease utilizes a multi-target approach for gene-to-structure determination of the PB2 influenza A subunit.
02:10
The Nucleosome Core Particle
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2020
Nucleosomes are the DNA-histone complex, where the DNA strand is wound around the histone core. The histone core is an octamer containing two copies of H2A, H2B, H3, and H4 histone proteins. The paradox Nucleosomes, paradoxically, perform two opposite functions simultaneously. On the one hand, their main responsibility is to protect the delicate DNA strands from physical damage and help achieve a higher compaction ratio. While on the other hand, they must allow polymerase enzymes to access DNA...
13:40
Examining Local Network Processing using Multi-contact Laminar Electrode Recording
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Cited by 14 •
2011
A fundamental issue in our understanding of cortical circuitry is how networks in different cortical layers encode sensory information. Here we describe electrophysiological techniques utilizing multi-contact laminar electrodes to record single-units and local field potentials and present analyses to identify cortical layers.
02:23
Visualizing the Effect of pH on Solubilization of the Influenza A Viral Core
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2026
Source: Stauffer, S. et al., In Vitro Disassembly of Influenza A Virus Capsids by Gradient Centrifugation. J. Vis. Exp. (2016)This video demonstrates the effect of pH on the solubilization of influenza A viral cores. Gel electrophoresis and protein staining reveal the progressive disassembly of the core structure under acidic conditions.
06:53
SpOT the Correct Tissue Every Time in Multi-tissue Blocks
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Cited by 5 •
2015
The purpose of the Specimen Orientation Tag (SpOT) is to function as an orientation tool to aid in individual tissue identification in multi-tissue paraffin blocks. These protocols demonstrate how it is constructed easily from common, low-cost histology materials and serves as a reliable visual marker in paraffin blocks and sections.
FAQs
Performance gains depend on how effectively software separates a biological workload into tasks that can run at the same time. The system must also track dependencies, so one task does not use data before another task produces it. This balance lets analyses take advantage of concurrent execution while preserving correct results, which is important for complex computational biology workflows.
Shared memory allows concurrently running tasks to access information needed across the workload, but coordination is essential when those tasks depend on one another. Operating systems and programming frameworks schedule tasks across available cores and manage synchronization. In biological analyses, this coordination helps maintain consistent computational results while processing large datasets more responsively.
Adding cores does not automatically make every analysis equally faster. The greatest benefit occurs when a workload contains tasks that can be executed concurrently and when software can schedule them without disrupting data dependencies. Work that is difficult to divide or requires frequent coordination may gain less. Thus, software design and workload structure influence practical performance.
Compared with handling instructions sequentially on one core, concurrent execution can reduce the time required for suitable workloads and improve responsiveness. The tradeoff is that parallel tasks require explicit coordination of shared memory, dependencies, and synchronization. This distinction helps explain why the same computational task may benefit differently depending on how readily its steps can operate in parallel.
A practical workflow begins by identifying which biological computation can be divided into parallel tasks. Software then assigns those tasks through an operating system or programming framework, while shared memory, data dependencies, and synchronization are coordinated. After execution, the resulting computations can support analysis of larger datasets with reduced processing time, when the workload is suitable.
Multi-core Processing is especially relevant to computationally intensive biological activities, including genome assembly, sequence alignment, molecular simulations, image analysis, and biological modeling. These tasks can involve substantial computational workloads, so distributing suitable portions across cores can reduce processing time. The resulting capacity supports more efficient investigation of complex biological systems and large experimental datasets.
High-throughput experiments can generate datasets whose size makes timely computation important. By shortening processing time and supporting larger datasets, the approach strengthens the practical use of these experiments in computational biology. It also enables researchers to apply computational models and analyses to complex biological systems more efficiently, linking experimental output with scalable data processing.