Inspired by the great success of neural networks, graph convolutional neural networks
(GCNs) are proposed to analyze graph data. GCNs mainly include two phases with distinct …

For more than thirty years, the parallel programming community has used the dependence
graph as the main abstraction for reasoning about and exploiting parallelism in" regular" …

Graph analytics is an emerging application which extracts insights by processing large
volumes of highly connected data, namely graphs. The parallel processing of graphs has …

Until recently, parallel programming has largely focused on the exploitation of data-
parallelism in dense matrix programs. However, many important application domains …

Thread-Level Speculation (TLS) is a promising technique that allows the parallel execution
of sequential code without relying on a prior, compile-time-dependence analysis. In this …

Cloud computing is emerging as a promising alternative to supercomputers for some high-
performance computing (HPC) applications. With cloud as an additional deployment option …

Many recent parallelization tools lower the barrier for parallelizing a program, but overlook
one of the first questions that a programmer needs to answer: which parts of the program …

There is growing interest in using GPUs to accelerate graph algorithms such as breadth-first
search, computing page-ranks, and finding shortest paths. However, these algorithms do not …

Automatic parallelization of sequential programs combined with auto-tuning is an alternative
to manual parallelization. With wider research directions and the increased number of …

Page 1 - ºf Eijkhou Page 2 Introduction to High Performance Scientific Computing Victor Eijkhout
with Edmond Chow, Robert van de Geijn 2nd edition, revision 2015 Page 3 Introduction to …