Time to market, Backup software, Backup software -- Product development, and Product development
Rapid prototyping is synonymous with additive manufacturing. The digital process, also known as 3D printing, is especially valuable because it inverts the economics of product development by generating rapid design [...]
Koch, Mathilde, Faulon, Jean-Loup, and Borkowski, Olivier
Frontiers in Bioengineering and Biotechnology. Nov 29, 2018
Synthetic biology -- Research, Biological research -- Methods, and Biology, Experimental -- Methods
Author(s): Mathilde Koch, Jean-Loup Faulon, Olivier Borkowski Cell-free TX-TL is an increasingly mature and useful platform for prototyping, testing and engineering biological parts and systems. However, to fully accomplish the [...]
Sidler, Hans JoRg, Duvenage, Jacob, Anderson, Eric J., Ng, Joanna, Hageman, Daniel J., and Tate, Melissa L. Knothe
Frontiers in Medicine. Dec 13, 2018
Microscope and microscopy, 3D printing, and Transdermal medication
Author(s): Hans Jorg Sidler, Jacob Duvenage, Eric J. Anderson, Joanna Ng, Daniel J. Hageman, Melissa L. Knothe Tate Natural materials exhibit smart properties including gradients in biophysical properties that engender [...]
Engineering software, Time to market, Return on investment, Product development -- Analysis, Rate of return -- Analysis, Engineering -- Computer programs, Engineering -- Usage, Engineering -- Methods, and Engineering -- Analysis
Design software is rapidly moving beyond the utilitarian process of rendering concepts and qualifying properties through material specification and analyses of mold design and manufacturability. A new generation of programs [...]
International Journal of Biomedical Imaging. Annual, 2018, Vol. 2018
Objective. We have created an open-source application and framework for rapid GPU-accelerated prototyping, targeting image analysis, including volumetric images such as CT or MRI data. Methods. A visual graph editor enables the design of processing pipelines without programming. Run-time compiled compute shaders enable prototyping of complex operations in a matter of minutes. Results. GPU-acceleration increases processing the speed by at least an order of magnitude when compared to traditional multithreaded CPU-based implementations, while offering the flexibility of scripted implementations. Conclusion. Our framework enables real-time, intuition-guided accelerated algorithm and method development, supported by built-in scriptable visualization. Significance. This is, to our knowledge, the first tool for medical data analysis that provides both high performance and rapid prototyping. As such, it has the potential to act as a force multiplier for further research, enabling handling of high-resolution datasets while providing quasi-instant feedback and visualization of results.
As ocean general circulation models (OGCMs) move into the petascale age, where the output of single simulations exceeds petabytes of storage space, tools to analyse the output of these models will need to scale up too. Lagrangian ocean analysis, where virtual particles are tracked through hydrodynamic fields, is an increasingly popular way to analyse OGCM output, by mapping pathways and connectivity of biotic and abiotic particulates. However, the current software stack of Lagrangian ocean analysis codes is not dynamic enough to cope with the increasing complexity, scale and need for customization of use-cases. Furthermore, most community codes are developed for stand-alone use, making it a nontrivial task to integrate virtual particles at runtime of the OGCM. Here, we introduce the new Parcels code, which was designed from the ground up to be sufficiently scalable to cope with petascale computing. We highlight its API design that combines flexibility and customization with the ability to optimize for HPC workflows, following the paradigm of domain-specific languages. Parcels is primarily written in Python, utilizing the wide range of tools available in the scientific Python ecosystem, while generating low-level C code and using just-in-time compilation for performance-critical computation. We show a worked-out example of its API, and validate the accuracy of the code against seven idealized test cases. This version#xC2;#xA0;0.9 of Parcels is focused on laying out the API, with future work concentrating on support for curvilinear grids, optimization, efficiency and at-runtime coupling with OGCMs.