In past blog posts, we’ve talked about the Wolfram Language’s built-in, high-level functionality for 3D printing. Today we’re excited to share an example of how some more general functionality in the language is being used to push the boundaries of this technology. Specifically, we’ll look at how computation enables 3D printing of very intricate sugar structures, which can be used to artificially create physiological channel networks like blood vessels.
September 11, 2018 — Jon McLoone, Director, Technical Communication & Strategy
Having a really broad toolset and an open mind on how to approach data can lead to interesting insights that are missed when data is looked at only through the lens of statistics or machine learning. It’s something we at Wolfram Research call multiparadigm data science, which I use here for a small excursion through calculus, graph theory, signal processing, optimization and statistics to gain some interesting insights into the engineering of supersonic cars.
September 30, 2016 — John McGee, Applications Developer, Wolfram Technology Group
A Mersenne prime is a prime number of the form Mp = 2p – 1, where the exponent p must also be prime. These primes take their name from the French mathematician and religious scholar Marin Mersenne, who produced a list of primes of this form in the first half of the seventeenth century. It has been known since antiquity that the first four of these, M2 = 3, M3 = 7,
M5 = 31 and M7 = 127, are prime.
August 26, 2016 — Zach Littrell, Technical Content Writer, Technical Communications and Strategy Group
We are constantly surprised by what fascinating applications and topics Wolfram Language experts are writing about, and we’re happy to again share with you some of these amazing authors’ works. With topics ranging from learning to use the Wolfram Language on a Raspberry Pi to a groundbreaking book with a novel approach to calculations, you are bound to find a publication perfect for your interests.
July 9, 2013 — Paritosh Mokhasi, Mathematica Algorithm R&D
The motion of fluid flow has captured the interest of philosophers and scientists for a long time. Leonardo da Vinci made several sketches of the motion of fluid and made a number of observations about how water and air behave. He often observed that water had a swirling motion, sometimes big and sometimes small, as shown in the sketch below.
We would now call such swirling motions vortices, and we have a systematic way of understanding the behavior of fluids through the Navier–Stokes equations. Let’s first start with understanding these equations.
May 30, 2013 — Wolfram Blog Team
Using Mathematica, Wolfgang Schmidt, a scientist at the Jülich Centre for Neutron Science, designed new neutron optical components to improve the efficiency of one of the most powerful spectrometers available for neutron scattering research.
Mathematica‘s flexible programming language allowed Schmidt to quickly write new programs and verify lengthy calculations for simulations he needed to investigate for spectrometer upgrades, which included a neutron polarizer. With Mathematica, he could test and visualize various parameters that helped him design the polarizer and optimize its performance.
Last week we posted an item about Wolfram Research‘s partnership with NVIDIA to integrate GPU programming into Mathematica. With NVIDIA’s GPU Technology Conference 2010 starting today, we thought we would share a little more for those who won’t be at the show to see us (booth #31, for those who are attending).
Mathematica‘s GPU programming integration is not just about performance. Yes, of course, with GPU power you get some of your answers several times faster than before—but that is only half the story.
The heart of the integration is the full automation of the GPU function developing process. With proper hardware, you can write, compile, test, and run your code in a single transparent step. There is no need to worry about details, such as memory allocation or library binding. Mathematica handles it elegantly and gracefully for you. As a developer, you will be able to focus on developing and optimizing your algorithms, and nothing else.
Here are a couple of examples to give you a taste of the upcoming feature.
September 14, 2010 — Angela Sims, Partnership Support Specialist
CUDA is NVIDIA’s performance computing architecture that harnesses modern GPU’s potential. The new partnership means that if you have GPU-equipped hardware, you can transform Mathematica‘s computing, modeling, simulation, or visualization performance, boosting speed by factors easily exceeding 100. Now that’s fast!
Afraid of the programming involved? Don’t be. Mathematica‘s new CUDA programming capabilities dramatically reduce the complexity of coding required to take advantage of GPU’s parallel power. So you can focus on innovating your algorithms rather than spending time on repetitive tasks, such as GUI design.
March 31, 2009 — Joel Klein, Kernel Technology
For some people, parallel computing and the need for a cluster is a way of life. For others, the need sneaks up on them. Most clusters and grids are planned and organized from the first, and that can take time and effort, to say nothing of configuration. Other times there’s no budget for new hardware, but there are computer labs or desktop computers unused for much of the day—a cluster waiting to be harnessed, if only you can get the Macs to talk to the Windows boxes, and keep straight all the hostnames in use. For situations like these I helped develop Wolfram Lightweight Grid System, which is designed from the ground up to let you assemble existing hardware into a self-organized network, accessible from Mathematica with almost no configuration.
March 18, 2009 — Roman Maeder, Director of Parallel Computing Technology
In the eighties I attended a scientific presentation about a rather cumbersome way to parallelize one of the symbolic computation systems in existence at that time and quickly realized how much more elegantly I could bring parallelism to Mathematica, thanks to its symbolic communication protocol, MathLink. This protocol allowed me to exchange not only data but also programs between concurrently running Mathematica kernels.
The result was a package, written entirely in Mathematica, called Parallel Computing Toolkit. At a time when parallel computing meant big expensive machines, FORTRAN, and batch jobs, it was quite satisfying to experiment with different parallel paradigms from an interactive Mathematica notebook, with a couple of machines on a local network doing the computations, and be able to do parallel functional programming and work with symbolic expressions and arbitrary-precision arithmetic in parallel. I got a lot of surprised reactions from people who thought that parallelization is this big complicated thing, requiring supercomputers and large funds, and rather large problems, to be worthwhile. The truth is, most problems people solve are easy to parallelize.