105 citations · 179 across the 7 of their papers we have counts for
8 papers
Fearless Concurrency on the GPU
Melih Elibol, Jared Roesch, Isaac Gelado +2
Rust has made safe systems programming practical on the CPU, but writing custom GPU kernels in Rust still forces programmers outside the language's ownership guarantees. We present…
Relax: Composable Abstractions for End-to-End Dynamic Machine Learning
Ruihang Lai, Junru Shao, Siyuan Feng +16
Dynamic shape computations have become critical in modern machine learning workloads, especially in emerging large language models. The success of these models has driven the deman…
Bring Your Own Codegen to Deep Learning Compiler
Zhi Chen, Cody Hao Yu, Trevor Morris +6
Deep neural networks (DNNs) have been ubiquitously applied in many applications, and accelerators are emerged as an enabler to support the fast and efficient inference tasks of the…
Dynamic Tensor Rematerialization
Marisa Kirisame, Steven Lyubomirsky, Altan Haan +5
Checkpointing enables the training of deep learning models under restricted memory budgets by freeing intermediate activations from memory and recomputing them on demand. Current c…
Nimble: Efficiently Compiling Dynamic Neural Networks for Model Inference
Haichen Shen, Jared Roesch, Zhi Chen +6
Modern deep neural networks increasingly make use of features such as dynamic control flow, data structures and dynamic tensor shapes. Existing deep learning systems focus on optim…
Tea: A High-level Language and Runtime System for Automating Statistical Analysis
Eunice Jun, Maureen Daum, Jared Roesch +4
Though statistical analyses are centered on research questions and hypotheses, current statistical analysis tools are not. Users must first translate their hypotheses into specific…