RNS can be used for efficient multiplication and addition which opens various ways for RNS applicability in AI and HPC (particularly through matrix multiplication). However, there are challenges to apply RNS for the floating point types: effective RNS scaling and comparison as well as conversion are needed to be implemented. The main target is trying to enable RNS for floating point matrix multiplication.
This topic is devoted to the problems of analysis of large graphs. An overview of current approaches to graph analysis and open problems in this field (linear-algebraic method, problems of calculations on a distributed cluster, problem of segmentation (partitioning) graph for sparse graphs of social networks type, analysis of dynamic graphs and others) will be presented .
(coffee-break + lunch included)
(coffee-break + lunch included)
Free time.
(dinner included)