4 citations · 6 across the 5 of their papers we have counts for
5 papers
Scaling Studies for Efficient Parameter Search and Parallelism for Large Language Model Pre-training
Michael Benington, Leo Phan, Chris Pierre Paul +5
AI accelerator processing capabilities and memory constraints largely dictate the scale in which machine learning workloads (e.g., training and inference) can be executed within a…
Compiler Optimization for Irregular Memory Access Patterns in PGAS Programs
Thomas B. Rolinger, Christopher D. Krieger, Alan Sussman
Irregular memory access patterns pose performance and user productivity challenges on distributed-memory systems. They can lead to fine-grained remote communication and the data ac…
An Empirical Evaluation of Allgatherv on Multi-GPU Systems
Thomas B. Rolinger, Tyler A. Simon, Christopher D. Krieger
Applications for deep learning and big data analytics have compute and memory requirements that exceed the limits of a single GPU. However, effectively scaling out an application t…
Parallel Sparse Tensor Decomposition in Chapel
Thomas B. Rolinger, Tyler A. Simon, Christopher D. Krieger
In big-data analytics, using tensor decomposition to extract patterns from large, sparse multivariate data is a popular technique. Many challenges exist for designing parallel, hig…
Impact of Traditional Sparse Optimizations on a Migratory Thread Architecture
Thomas B. Rolinger, Christopher D. Krieger
Achieving high performance for sparse applications is challenging due to irregular access patterns and weak locality. These properties preclude many static optimizations and degrad…