3 papers
cs.AR2025
Ember: A Compiler for Efficient Embedding Operations on Decoupled Access-Execute Architectures
Marco Siracusa, Olivia Hsu, Victor Soria-Pardos +8
Irregular embedding lookups are a critical bottleneck in recommender models, sparse large language models, and graph learning models. In this paper, we first demonstrate that, by o…
cs.AR2024
DFModel: Design Space Optimization of Large-Scale Systems Exploiting Dataflow Mappings
Sho Ko, Nathan Zhang, Olivia Hsu +2
We propose DFModel, a modeling framework for mapping dataflow computation graphs onto large-scale systems. Mapping a workload to a system requires optimizing dataflow mappings at v…
cs.PL2024
Compilation of Modular and General Sparse Workspaces
Genghan Zhang, Olivia Hsu, Fredrik Kjolstad
Recent years have seen considerable work on compiling sparse tensor algebra expressions. This paper addresses a shortcoming in that work, namely how to generate efficient code (in…