Neural Ordinary Differential Equations

Neural ordinary differential equations Chen et al., NeurIPS’18 ‘Neural Ordinary Differential Equations’ won a best paper award at NeurIPS last month. It’s not an easy piece (at least not for me!), but in the spirit of ‘deliberate practice’ that doesn’t mean there isn’t something to be gained from trying to understand as much as possible. […]

Continue Reading

Applied machine learning at Facebook: a datacenter infrastructure perspective

Applied machine learning at Facebook: a datacenter infrastructure perspective Hazelwood et al., _HPCA’18 _ This is a wonderful glimpse into what it’s like when machine learning comes to pervade nearly every part of a business, with implications top-to-bottom through the whole stack. It’s amazing to step back and think just how fundamentally software systems have […]

Continue Reading

Darwinian data structure selection

Darwinian data structure selection Basios et al., FSE’18 GraphIt may have caught your attention for the success of its approach, but I suspect for many readers it’s not something you’ll be immediately applying. Darwinian Data Structures (DDSs) on the other hand looks to be of immediate interest to many Java and C++ projects (and generalises […]

Continue Reading

GraphIt: A high-performance graph DSL

GraphIt: a high-performance graph DSL Zhang et al., OOPSLA’18 See also: http://graphit-lang.org/. The problem with finding the optimal algorithm and data structures for a given problem is that so often it depends. This is especially true when it comes to graph algorithms. It is difficult to implement high-performance graph algorithms. The performance bottlenecks of these […]

Continue Reading