31 citations · 42 across the 10 of their papers we have counts for
7 papers · 1 filter
Towards Transparent Checkpointing with AI-driven Code Generation
Hai Duc Nguyen, Tekin Bicer, Kyle Chard +2
Adding reliable checkpoint/restart support to an MPI scientific application is a time-consuming expert effort that requires deep knowledge of both the application and resilience. W…
Recency/Frequency Adaptive KV Caching for Large Language Model Serving
Yang Shen, Meghana Madhyastha, Robert Underwood +2
Key-value (KV) caching is a powerful technique for accelerating large language model inference and generation. Inference workloads are large and diverse, which makes them difficult…
Understanding Inference Scaling for LLMs: Bottlenecks, Trade-offs, and Performance Principles
Moiz Arif, Avinash Maurya, Sudharshan Vazhkudai +1
The transition from standard generative AI to \emph{reasoning-centric architectures}, exemplified by models capable of extensive Chain-of-Thought~(CoT) processing, marks a fundamen…
DataStates-LLM: Scalable Checkpointing for Transformer Models Using Composable State Providers
Avinash Maurya, M. Mustafa Rafique, Franck Cappello +1
The rapid growth of Large Transformer-based models, specifically Large Language Models (LLMs), now scaling to trillions of parameters, has necessitated training across thousands of…
MLP-Offload: Multi-Level, Multi-Path Offloading for LLM Pre-training to Break the GPU Memory Wall
Avinash Maurya, M. Mustafa Rafique, Franck Cappello +1
Training LLMs larger than the aggregated memory of multiple GPUs is increasingly necessary due to the faster growth of LLM sizes compared to GPU memory. To this end, multi-tier hos…
Breaking the Memory Wall: A Study of I/O Patterns and GPU Memory Utilization for Hybrid CPU-GPU Offloaded Optimizers
Avinash Maurya, Jie Ye, M. Mustafa Rafique +2
Transformers and LLMs have seen rapid adoption in all domains. Their sizes have exploded to hundreds of billions of parameters and keep increasing. Under these circumstances, the t…