The study of parallel and distributed applications and platforms, whether in the cluster, grid,
peer-to-peer, volunteer, or cloud computing domain, often mandates empirical evaluation of …

This survey aims to present the state of the art in analytic communication performance
models, providing sufficiently detailed descriptions of particularly noteworthy efforts …

This paper presents an in-depth analysis of the impact of system noise on large-scale
parallel application performance in realistic settings. Our analytical model shows that not …

In this demonstration, we present an Eclipse plug-in that automatically identifies type-
checking bad smells in Java source code, and resolves them by applying the" replace …

Scalability of Krylov subspace methods suffers from costly global synchronization steps that
arise in dot-products and norm calculations on parallel machines. In this work, a modified …

Many parallel applications suffer from latent performance limitations that may prevent them
from scaling to larger machine sizes. Often, such scalability bugs manifest themselves only …

As deep learning models and input data continue to scale at an unprecedented rate, it has
become inevitable to move towards distributed training platforms to fit the models and …

Optimizing communication performance is imperative for large-scale computing because
communication overheads limit the strong scalability of parallel applications. Today's …

In the generalized minimal residual method (GMRES), the global all-to-all communication
required in each iteration for orthogonalization and normalization of the Krylov base vectors …

Modern Deep Learning systems heavily rely on distributed training over high-performance
accelerator (eg, TPU, GPU)-based hardware platforms. Examples today include Google's …