38 citations · 45 across the 9 of their papers we have counts for
9 papers
Streaming Tensor Programs: A Streaming Abstraction for Dynamic Parallelism
Gina Sohn, Genghan Zhang, Konstantin Hossfeld +5
Dynamic behaviors are becoming prevalent in tensor applications, like machine learning, where many widely used models contain data-dependent tensor shapes and control flow. However…
FuseFlow: A Fusion-Centric Compilation Framework for Sparse Deep Learning on Streaming Dataflow
Rubens Lacouture, Nathan Zhang, Ritvik Sharma +4
As deep learning models scale, sparse computation and specialized dataflow hardware have emerged as powerful solutions to address efficiency. We propose FuseFlow, a compiler that c…
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…
Adaptive Self-improvement LLM Agentic System for ML Library Development
Genghan Zhang, Weixin Liang, Olivia Hsu +1
ML libraries, often written in architecture-specific programming languages (ASPLs) that target domain-specific architectures, are key to efficient ML systems. However, writing thes…
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…
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…
BaCO: A Fast and Portable Bayesian Compiler Optimization Framework
Erik Hellsten, Artur Souza, Johannes Lenfers +7
We introduce the Bayesian Compiler Optimization framework (BaCO), a general purpose autotuner for modern compilers targeting CPUs, GPUs, and FPGAs. BaCO provides the flexibility ne…
Stardust: Compiling Sparse Tensor Algebra to a Reconfigurable Dataflow Architecture
Olivia Hsu, Alexander Rucker, Tian Zhao +2
We introduce Stardust, a compiler that compiles sparse tensor algebra to reconfigurable dataflow architectures (RDAs). Stardust introduces new user-provided data representation and…
The Sparse Abstract Machine
Olivia Hsu, Maxwell Strange, Ritvik Sharma +5
We propose the Sparse Abstract Machine (SAM), an abstract machine model for targeting sparse tensor algebra to reconfigurable and fixed-function spatial dataflow accelerators. SAM…