3 papers
cs.DC2026
Optimizing Allreduce Operations for Modern Heterogeneous Architectures with Multiple Processes per GPU
Michael Adams, Amanda Bienz
Large inter-GPU all-reduce operations, prevalent throughout deep learning, are bottlenecked by communication costs. Emerging heterogeneous architectures are comprised of complex no…
cs.DC2026
Scaling All-to-all Operations Across Emerging Many-Core Supercomputers
Shannon Kinkead, Jackson Wesley, Whit Schonbein +3
Performant all-to-all collective operations in MPI are critical to fast Fourier transforms, transposition, and machine learning applications. There are many existing implementation…
cs.DC2025
Persistent and Partitioned MPI for Stencil Communication
Gerald Collom, Jason Burmark, Olga Pearce +1
Many parallel applications rely on iterative stencil operations, whose performance are dominated by communication costs at large scales. Several MPI optimizations, such as persiste…