The Evolution of a New Model of Computation
DescriptionThe conventional model of parallel programming today involves either copying data (and then having to track its most recent value), or not copying and requiring deep software stacks to do even the simplest operation on data that is "over there" - out of the range of loads and stores from the current core. As applications require larger data sets, with more irregular access to them, both models begin to exhibit severe scaling problems. This presentation reviews some growing evidence of the potential value of a model of computation that skirts between the two: data does not move (i.e. is not copied), and computation instead moves to the data. Several different applications have been coded for a novel platform where thread movement is handled invisibly by the hardware. The evidence to date indicates that parallel scaling for this paradigm may very well be significantly better than any mix of conventional models.
Event Type
Workshop
TimeFriday, 18 November 20229:30am - 9:50am CST
LocationC144-145
W
Accelerator-based Architectures
Algorithms
Architectures
Big Data
Data Analytics
Parallel Programming Languages and Models
Productivity Tools
Recorded
