Acceleware and Computer Simulation Technology (CST) have released a Cuda (Compute Unified Device Architecture) based solution for accelerating EM simulations with CST Microwave Studio (CST MWS).
Acceleware has released the C30-16, a GPU-based cluster solution combining Acceleware's clustering technology with its portfolio of designed-for-parallel computational algorithms to harness the power of 64 GPUs.
Acceleware has released Acceleware AxKTM, an application library that enables users of 3D Pre-Stack Time Migration (PSTM) solutions to easily access the multi-fold performance benefits of todays leading GPUs (graphic processor units).
Acceleware has launched its AxRecon image reconstruction solution for the medical imaging, security, and non-destructive testing markets.
Acceleware has launched its seismic data acceleration software, which reduces processing times and delivers faster results when the oil and gas industry are making drilling decisions.
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.
