31 citations · 32 across the 6 of their papers we have counts for
7 papers
TOPIQ: Statistical Error Propagation for Quantity-of-Interest Prediction under Lossy Compression
Youyuan Liu, Bo Jiang, Taolue Yang +3
Lossy compression is essential for managing massive scientific data, but per-element error bounds do not translate into bounds on downstream quantities of interest (QoIs) such as r…
PTStore (Prefix Tensor Store): Distributed Prefix Caching and Replication for High Throughput Inference Serving
Meghana Maghyastha, Robert Underwood, Randal Burns +1
Inspired by the design of client caching in Content Delivery Networks (CDNs), PTStore distributes and replicates popular tensors that form reusable KV cache prefixes, which are the…
Rate-Distortion Bounds for Heterogeneous Random Fields on Finite Lattices
Sujata Sinha, Vishwas Rao, Robert Underwood +4
Since Shannon's foundational work, rate-distortion theory has defined the fundamental limits of lossy compression. Classical results, derived for memoryless and stationary ergodic…
SPARe: Stacked Parallelism with Adaptive Reordering for Fault-Tolerant LLM Pretraining Systems with 100k+ GPUs
Jin Lee, Zhonghao Chen, Xuhang He +6
In large-scale LLM pre-training systems with 100k+ GPUs, failures become the norm rather than the exception, and restart costs can dominate wall-clock training time. However, exist…
EAIRA: Establishing a Methodology for Evaluating AI Models as Scientific Research Assistants
Franck Cappello, Sandeep Madireddy, Robert Underwood +23
Recent advancements have positioned AI, and particularly Large Language Models (LLMs), as transformative tools for scientific research, capable of addressing complex tasks that req…
DataStates-LLM: Lazy Asynchronous Checkpointing for Large Language Models
Avinash Maurya, Robert Underwood, M. Mustafa Rafique +2
LLMs have seen rapid adoption in all domains. They need to be trained on high-end high-performance computing (HPC) infrastructures and ingest massive amounts of input data. Unsurpr…
Understanding Patterns of Deep Learning ModelEvolution in Network Architecture Search
Robert Underwood, Meghana Madhastha, Randal Burns +1
Network Architecture Search and specifically Regularized Evolution is a common way to refine the structure of a deep learning model.However, little is known about how models empiri…