Accelerating scientific discovery by formulating grand scientific challenges

Hephaestus: Data Reuse for Accelerating Scientific Discovery

Data-intensive science, wherein domain experts use big data analytics in the course of their research, is becoming increasingly common in the physical and social sciences. Moreover, data reuse is becoming the new normal, owing to the open data movement [15] and arrival of big science experiments such as the Large Hadron Collider. Here, a small group of researchers with exotic equipment produce ...

Accelerating Scientific Discovery Through Computation and Visualization

The rate of scientific discovery can be accelerated through computation and visualization. This acceleration results from the synergy of expertise, computing tools, and hardware for enabling high-performance computation, information science, and visualization that is provided by a team of computation and visualization scientists collaborating in a peer-to-peer effort with the research scientist...

Dynamic Software Updates for Accelerating Scientific Discovery

Distributed parallel applications often run for hours or even days before arriving to a result. In the case of such long-running programs, the initial requirements could change after the program has started executing. To shorten the time it takes to arrive to a result when running a distributed computationally-intensive application, this paper proposes leveraging the power and flexibility of dy...

Scientific Grand Challenges: Toward Exascale Supercomputing and Beyond

Accelerating Scientific Discovery Through Computation and Visualization II

This is the second in a series of articles describing a wide variety of projects at NIST that synergistically combine physical science and information science. It describes, through examples, how the Scientific Applications and Visualization Group (SAVG) at NIST has utilized high performance parallel computing, visualization, and machine learning to accelerate research. The examples include sci...