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High-Performance Computing

Computation & Analysis

Nobel Prize–Inspired de novo Protein Design with Wolfram Language

When I read a recent New York Times article on AI, I didn’t think I would be following the footsteps of a Nobel laureate, but I soon discovered that I could do just that with Wolfram Language. The Nobel Prize in Chemistry for 2024 was awarded for computational protein design and protein structure prediction, which have been active areas of research for several decades. The early work was built upon a foundation of physics and chemistry, attempting to model the folding of the chain of amino acid residues comprising a protein into a three-dimensional structure using conformational analysis and energetics. More recently, AI methods have been brought to bear on the problem, making use of deep neural networks (DNNs) and large language models (LLMs) such as trRosetta, AlphaFold and ESMFold. The work of David Baker, one of the laureates, was recently showcased in a New York Times article.
Robert Nachbar, Senior Project Director, Wolfram Solutions
Current Events & History

The Solution of the Zodiac Killer’s 340-Character Cipher

In 2020, Melbourne, Australia, had a 112-day lockdown of the entire city to help stop the spread of COVID-19. The wearing of masks was mandatory and we were limited to one hour a day of outside activity. Otherwise, we were stuck in our homes. This gave me lots of time to look into interesting problems I’d been putting off for years.

I was inspired by a YouTube video by David Oranchak, which looked at the Zodiac Killer’s 340-character cipher (Z340), which is pictured below. This cipher is considered one of the holy grails of cryptography, as at the time the cipher had resisted attacks for 50 years, so any attempts to find a solution were truly a moonshot.

Sam Blake, Guest Author
Announcements & Events

Free-Form Bioprinting with Mathematica and the Wolfram Language

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.
Greg Hurst, Wolfram|Alpha Math Content Matt Gelber, Postdoctoral Researcher, University of Illinois at Urbana-Champaign
Computation & Analysis

Thrust Supersonic Car Engineering Insights: Applying Multiparadigm Data Science

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.

Jon McLoone, Director, Technical Communication & Strategy
Best of Blog

Mersenne Primes and the Lucas–Lehmer Test

Introduction

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.
John McGee, Applications Developer, Wolfram Technology Group
Education & Academic

New Wolfram Language Books

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.
Zach Littrell, Technical Content Writer, Technical Communications and Strategy Group
Best of Blog

Using Mathematica to Simulate and Visualize Fluid Flow in a Box

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.
Paritosh Mokhasi, Kernel Developer, Algorithms R&D
Computation & Analysis

Optimizing Instrumentation Design with Mathematica: Neutron Polarizer

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.
Wolfram Blog Team
Computation & Analysis

Tapping Into the Power of GPU in Mathematica

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.
Ulises Cervantes-Pimentel, Senior Kernel Developer Abdul Dakkak, Senior Compiler Developer, Compiler Development
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