Practical Many-Core Programming

Vajda, András

doi:10.1007/978-1-4419-9739-5_9

Cited by 2 publications

(1 citation statement)

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“…The multi-core architecture has the advantage of providing higher clock rates because data do not have to travel across chip sets. 33 Computational tasks are typically scaled through manual or programmatic forking of tasks to multiple cores, or by writing multi-threaded applications using the Open Multi-Processing (OpenMP 34,35 ) API.…”

Section: Scalable Computing Systemsmentioning

confidence: 99%

Next Generation Distributed Computing for Cancer Research

Agarwal

Owzar

2014

Cancer Inform

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Advances in next generation sequencing (NGS) and mass spectrometry (MS) technologies have provided many new opportunities and angles for extending the scope of translational cancer research while creating tremendous challenges in data management and analysis. The resulting informatics challenge is invariably not amenable to the use of traditional computing models. Recent advances in scalable computing and associated infrastructure, particularly distributed computing for Big Data, can provide solutions for addressing these challenges. In this review, the next generation of distributed computing technologies that can address these informatics problems is described from the perspective of three key components of a computational platform, namely computing, data storage and management, and networking. A broad overview of scalable computing is provided to set the context for a detailed description of Hadoop, a technology that is being rapidly adopted for large-scale distributed computing. A proof-of-concept Hadoop cluster, set up for performance benchmarking of NGS read alignment, is described as an example of how to work with Hadoop. Finally, Hadoop is compared with a number of other current technologies for distributed computing.

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Section: Scalable Computing Systemsmentioning

confidence: 99%