Note to recruiters

Note to recruiters: We are quite aware that recruiters, interviewers, VCs and other professionals generally perform a Google Search before they interview someone, take a pitch from someone, et cetera. Please keep in mind that not everything put on the Internet must align directly to one's future career and/or one's future product portfolio. Sometimes, people do put things on the Internet just because. Just because. It may be out of their personal interests, which may have nothing to do with their professional interests. Or it may be for some other reason. Recruiters seem to have this wrong-headed notion that if somebody is not signalling their interests in a certain area online, then that means that they are not interested in that area at all. It is worth pointing out that economics pretty much underlies the areas of marketing, strategy, operations and finance. And this blog is about economics. With metta, let us. by all means, be reflective about this whole business of business. Also, see our post on "The Multi-faceted Identity Problem".

Tuesday, January 26, 2016

TECHNOLOGY: Scheduling algorithms based on game theory makes better use of computational resources

Rubing Duan and Xiaorong Li at the A*STAR Institute of High Performance Computing in Singapore and co-workers have now developed a scheme to address the scheduling problem in two large-scale applications: the ASTRO program from the field of cosmology, which simulates the movements and interactions of galaxy clusters, and the WIEK2k program from the field of theoretical chemistry, which calculates the electronic structure of solids1. The researchers' new scheme relies on three game-theory-based scheduling algorithms: one to minimize the execution time; one to reduce the economic cost; and one to limit the storage requirements.

The researchers performed calculations wherein they stopped the competition for resources when the iteration reached the upper limit of optimization. They compared their simulation results with those from related algorithms—namely, Minimum Execution Time, Minimum Completion Time, Opportunistic Load Balancing, Max-min, Min-min and Sufferage. The new approach showed improvements in terms of speed, cost, scheduling results and fairness. Furthermore, the researchers found that the execution time improved as the scale of the experiment increased. In one case, their approach delivered results within 0.3 seconds while other algorithms needed several hours.