"This paper presents the design and analysis of parallel approximation algorithms for facility-location problems, including $\NC$ and $\RNC$ algorithms for (metric) facility location, $k$-center, $k$-median, and $k$-means. These problems have received considerable attention during the past decades from the approximation algorithms community, concentrating primarily on improving the approximation guarantees. In this paper, we ask, is it possible to parallelize some of the beautiful results from the sequential setting?"

"It boils down to this: GitHub is a warzone. We are constantly overloaded and rely very, very heavily on our queue. If it's backed up, we need to know why. We need to know if we can fix it. We need workers to not get stuck and we need to know when they are stuck.

We need to see what the queue is doing. We need to see what jobs have failed. We need stats: how long are workers living, how many jobs are they processing, how many jobs have been processed total, how many errors have there been, are errors being repeated, did a deploy introduce a new one?

We need a background job system as serious as our web framework. I highly recommend DelayedJob to anyone whose site is not 50% background work.

But GitHub is 50% background work."

"PyGPU is an embedded language in Python, that allow most of Python features (list-comprehensions, higher-order functions, iterators) to be used for constructing GPU algorithms. It uses a image abstraction to abstract away implementation details of the GPU, while still allowing translation to very efficient GPU native-code."

Important.