Power and energy efficiency are now critical concerns in extreme-scale high-performance
scientific computing. Many extreme-scale computing systems today (for example: Top500) …

Previous studies of application usage show that the performance of collective
communications are critical for high-performance computing. Despite active research in the …

Applying machine learning techniques to the quickly growing data in science and industry
requires highly-scalable algorithms. Large datasets are most commonly processed" data …

A 5-year-profiling in production mode at the University of Stuttgart has shown that more than
40% of the execution time of Message Passing Interface (MPI) routines is spent in the …

Matrix factorization, when the matrix has missing values, has become one of the leading
techniques for recommender systems. To handle web-scale datasets with millions of users …

BlueGene/L is currently the world's fastest supercomputer. It consists of a large number of
low power dual-processor compute nodes interconnected by high speed torus and collective …

The spectral clustering algorithm is extensively employed in different aspects, especially in
the field of pattern recognition. However, the efficient construction of the neighborhood …

Communication scheduling has been shown to be effective in accelerating distributed
training, which enables all-reduce communications to be overlapped with backpropagation …

As the number of cores per node keeps increasing, it becomes increasingly important for
MPI to leverage shared memory for intranode communication. This paper investigates the …

With local core counts on the rise, taking advantage of shared-memory to optimize collective
operations can improve performance. We study several on-host shared memory optimized …