Different data, different analytical tools, different users. How can scientific computing cope with diversity in the life sciences? Tom Wilkie discovers that data pipelining and workflow technology offer new approaches
The automotive sector is undergoing a period of rapid and disruptive transformation from both a business and technical standpoint.
Estimates suggest that the global laboratory informatics market, valued at $2.6 billion in 2019, will witness CAGR of 7.5 per cent over the next five years, reaching $3.8 billion in 2024[1].
Modern drug discovery and development workflows incorporate a growing range of automated, high-throughput technologies that can generate data on a truly impressive scale.
‘This will be the life sciences century … because of personalised medicine, information technology, and knowledge on biological systems,’ said Fred Hassan, a partner of Warburg Pincus and former CEO of Schering-Plough.
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
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.
Architectural specialisation is one option to continue to improve performance beyond the limits imposed by the slow down in Moore’s Law.
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
