Scientific Computing

The Protein Data Bank (PDB) bioinformatics database is the world's largest repository of experimentally-determined structures of proteins, nucleic acids, and complex assemblies. All data is gathered using experimental methods such as X-ray, spectroscopy, crystallography, NMR, etc. This article explains how to extract, filter, and clean data from the PDB to make it suitable for further analysis.

Genome data is one of the most widely analyzed datasets in the realm of Bioinformatics. The SciPy stack offers a suite of popular Python packages designed for numerical computing, data transformation, analysis and visualization, which is ideal for many bioinformatic analysis needs. In this tutorial, Toptal Software Engineer Zhuyi Xue walks us through some of the capabilities of the SciPy stack. He also answers some interesting questions about the human genome, including: How much of the genome is incomplete? How long is a typical gene?

HSA is a set of standards and specifications designed to allow further integration of CPUs and GPUs on the same bus. This is not an entirely new concept, but HSA takes it to the next level. HSA would effectively take the developer out of the equation, at least when it comes to assigning different loads to different processing cores. In this post, Toptal Technical Editor and resident chip geek Nermin Hajdarbegovic takes a closer look at HSA and the future of heterogeneous computing in general. Get ready for some good news, but don't forget to brace for bad news first.

In Part I of this three-part series, we saw how the free motion of rigid bodies can be simulated. In Part II, we saw how to make bodies aware of each other through collision and proximity tests. Up to this point, however, we still have not seen how to make objects truly interact with each other. The final step to simulating realistic, solid objects, is to apply constraints, defining restrictions on the motion of rigid bodies. In this article, we'll discuss equality constraints and inequality constraints. We'll describe them first in terms of a force-based approach, where corrective forces are computed, and then in terms of an impulse-based approach, where corrective velocities are computed instead. Finally, we'll go over some clever tricks to eliminate unnecessary work and speed up computation.

In Part I of this three-part series on game physics, we explored rigid bodies and their motions. In that discussion, however, objects did not interact with each other. Without some additional work, the simulated rigid bodies can go right through each other. In Part II, we will cover the collision detection step, which consists of finding pairs of bodies that are colliding among a possibly large number of bodies scattered around a 2D or 3D world.

Simulating physics in video games is very common, since most games are inspired by things we have in the real world. Rigid body dynamics -- the movement and interaction of solid, inflexible objects -- is by far the most popular kind of effect simulated in games. In this series, rigid body simulation will be explored, starting with simple rigid body motion in this article, and then covering interactions among bodies through collisions and constraints in the following installments.

How do we understand and interpret the huge amounts of data coming out of simulations? How do we visualize potential gigabytes of datapoints in a large dataset? In this article I will give a quick introduction to VTK and its pipeline architecture, and go on to discuss a real-life visualization example.

Scientific computing is hard. But thanks to an ever-growing landscape of open source tools, really tough problems are becoming easier to solve. Toptal engineer Charles Cook provides an in-depth example, leveraging open source tools to solve a problem in computational fluid dynamics.