1 research outputs found
Massively Parallel Graph Drawing and Representation Learning
To fully exploit the performance potential of modern multi-core processors,
machine learning and data mining algorithms for big data must be parallelized
in multiple ways. Today's CPUs consist of multiple cores, each following an
independent thread of control, and each equipped with multiple arithmetic units
which can perform the same operation on a vector of multiple data objects.
Graph embedding, i.e. converting the vertices of a graph into numerical vectors
is a data mining task of high importance and is useful for graph drawing
(low-dimensional vectors) and graph representation learning (high-dimensional
vectors). In this paper, we propose MulticoreGEMPE (Graph Embedding by
Minimizing the Predictive Entropy), an information-theoretic method which can
generate low and high-dimensional vectors. MulticoreGEMPE applies MIMD
(Multiple Instructions Multiple Data, using OpenMP) and SIMD (Single
Instructions Multiple Data, using AVX-512) parallelism. We propose general
ideas applicable in other graph-based algorithms like \emph{vectorized hashing}
and \emph{vectorized reduction}. Our experimental evaluation demonstrates the
superiority of our approach