Developer Skills at Work: Serious (and Not-So-Serious) Stuff

Feeling knowledgeable? I have two different opportunities to put your software development knowledge to work. They could not be more different!

Feeling knowledgeable? I have two different opportunities to put your software development knowledge to work. They could not be more different!

Not-so-serious first. In preparing for this blog, I wasted enough time to become the leader on this interactive game (my initials: “JRR”). Some of the questions are based on a trivia contest we held at the Supercomputing conference a few years ago. The booth trivia contest was a hit with our audience. Now we can all participate and show off our knowledge in a simple video game. Can you displace me? 

 

Five Students, nine weeks, and our votes help determine the winner

On the more serious note, my friends at CERN openlab have five brilliant students working on applications as part of their Summer Student Programme.  In conjunction with Intel, they’ll be giving us an opportunity to review their progress, comment on it, question it, and vote on our favorites. I’ll share what I know now – and you can visit this link for updates.

The five challenges are:

1.     Smash-simulation–fifth generation GEANT This project aims to develop faster algorithms for simulating particle-collision events as part of creating a fifth generation of the popular GEANT (GEometry ANd Tracking) software. Physicists widely use a software toolkit called GEANT4. An international team, led by researchers at CERN, is now working to develop a new version of this simulation toolkit, called GeantV. This work is supported by a CERN openlab project with Intel on code modernization. GeantV aims to improve physics accuracy and boost performance.

2.     Connecting the dots – can machine learning help?Particle detectors, such as those used at CERN, are designed to recognize different types of charged particles produced by the collisions at the heart of the detector. As the charged particles fly outwards through the various layers of the detector, they leave traces (or ‘hits’). This project aims to combine the use of machine-learning techniques to help recognize these hit shapes more efficiently. 

3.     Cells in the cloud This project aims to adapt the BioDynaMo for running high-performance computing resources over the cloud. It focuses on adding network support for the single-node simulator and prototyping the computation management across many nodes.

4.     Disaster reliefHelping computers “see” objects in UN satellite maps: This project is exploring new approaches to image analysis and automated feature recognition to ease the task of identifying different classes of objects from satellite maps. It will evaluate available machine-learning-based feature-extraction algorithms.

5.     IoT at the LHC small computing for the largest science project in the world?: The Large Hadron Collider (LHC) accelerates particles to over 99.9999% of the speed of light. It is the most complex machine ever built, relying on a wide range of industrial control systems for proper functioning.  This project will focus on integrating modern “systems-on-a-chip” devices into the LHC control systems so they will be able to communicate via an overlaying cloud-computing service. A goal is to reduce the network load within the entire control infrastructure and ensure that applications are not disrupted by limited or intermittent network connectivity.

I will post updates as the summer continues – and we’ll all be able to review and vote on our favorites. One of the five students will be selected to showcase the winning project. The winner will be announced at the upcoming Intel HPC Developers Conference on November 11, and will be demonstrated in the days following that at the Supercomputing 2017 conference in Denver.

Additional reading to learn more:

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