The International Conference for High Performance Computing, Networking, Storage, and Analysis (usually referred to as “Supercomputing”) is one of the largest and most influential technology conferences in the world. I’ve been attending since 2004 (I have the swag to prove it), and the changes since then are staggering. Infrastructure continues to evolve at a breakneck pace to meet the needs of a data driven economy, and the storage component is no exception.
Recent posts by Adam Marko
3 min read
Last Friday, we talked about DataDiscover, and went over a few positive outcomes that our customers have achieved as a result of being able to make more fact-based decisions about their data. For our customers in the Life Sciences space, the presence or absence of data visibility is especially impactful to their success. Their concerns include everything mentioned in that article, but since the life sciences generates such massive volumes of data per employee (ie, per researcher), these customers have some additional concerns.
4 min read
Life science organizations face many challenges when it comes to the informatics component of their research. Scientific instrumentation is generating unstructured data at an unprecedented rate, and existing first tier storage systems can quickly reach capacity.
Next Generation Sequencing (NGS) is currently the largest consumer of storage capacity in the life sciences, but adding to expensive high performance storage as demands increase is not a sustainable or cost effective solution. Let’s look at the unstructured data management challenges of NGS workflows and possible solutions.
Topics: Life Sciences
2 min read
Artificial Intelligence (AI) has various applications today, from self-driving vehicles to optimizing workflows in manufacturing operations to detecting malware on the internet. Deep learning is a form of AI where multi-layer neural networks are utilized to transform input data into progressively more defined and useful outputs. Deep learning differs from machine learning (ML) in that ML focuses on the development of task-specific algorithms that can be applied to specific problems, while deep learning focuses on extracting information at multiple levels.