NVidia has introduced the new Quadro Plex Visual Computing System Model S4, a graphics server featuring more graphics processing units (GPUs) in a standard 1U server form factor.
NVidia has released a plug-in example that allows Matlab programs to use standard graphics processing unit (GPU) libraries to speed up their applications.
Following strong demand from the high-performance-computing community for Mathematica 6, Wolfram Research has launched Parallel Computing Toolkit 2.1 - a parallel solution for anyone with access to Mathematica and more than one processor.
Interactive Supercomputing has unveiled a new version of its Star-P software designed to accelerate time-to-discovery and boost productivity for users in the life sciences industries.
Interactive Supercomputing has released an updated version of Star-P software, designed for users of Python. The new version offers huge productivity gains for Python users who need to solve computationally intensive problems.
Elsevier and ExactCure recently announced a collaboration to develop and offer to hospitals – without charge – drug-specific exposure models for 20 already approved medicines that are being tested as potential treatments for Covid-19.
With the world’s focus on the COVID-19 pandemic, LIMS providers are working to develop solutions tailored to assist in the testing and management of virus samples.
Researchers at Harvard Medical School (HMS) have released an open source drug discovery platform, VirtualFlow, that harnesses supercomputing power to screen potentially billions of small organic compound structures in parallel, in the hunt for promising new drug molecules.
When designing a component or system, where do you start? In days gone by, designers would literally go back to the drawing board, using their experience and expertise to develop a new concept.
Effective utilisation of computing tools is critical to both AI and HPC research, with recent projects in both fields highlighting the scientific benefit of carefully managing resources.
Advances in computer vision combined with AI computing are helping pathologists to more accurately identify subtypes of cancer - leading to better treatments for patients, explains Dan Ruderman.
