Clare Sansom looks at the impact big data is having on genomics research
Machine learning and Big Data analytics offer significant opportunities to improve R&D in the materials sciences, providing scientists with a new set of tools to analyze their data. These Machine learning and Big Data analytics offer significant opportunities to improve R&D in the materials sciences, providing scientists with a new set of tools to analyze their data. These approaches can help scientists do more with less, building a stronger, data-driven foundation for decision making and guiding future research.
David Wang gives his view on how modern laboratories can leverage data to provide maximum value
As not only the HPC industry but the larger computing ecosystem tries to overcome the slowdown and eventual end of transistor scaling described by Moore’s Law, scientists and researchers are implementing programmes that will define the technologies and new materials needed to supplement, or repla
Architectural specialisation is one option to continue to improve performance beyond the limits imposed by the slow down in Moore’s Law.
As the complexity of both HPC hardware and the challenges that scientists are trying to solve using HPC systems increases, ensuring a system is utilised efficiently is necessary to deliver a computing service with value for money and a high research output.
With data rates increasing and more complex challenges arising in life sciences, Panasas’ Jim Donovan and Dale Brantly discuss the benefits of using HPC for these workloads
