2 citations · 4 across the 6 of their papers we have counts for
7 papers
DLCB: Ahead-of-Time Compilation for Dynamic Deep Learning
Alexander Collins, Bin Fan, Evghenii Gaburov +4
Deep learning workloads are increasingly deployed in settings where tensor shapes are not fully known at compile time. Batch sizes vary across requests, sequence lengths differ bet…
FlashFormer: Whole-Model Kernels for Efficient Low-Batch Inference
Aniruddha Nrusimha, William Brandon, Mayank Mishra +4
The size and compute characteristics of modern large language models have led to an increased interest in developing specialized kernels tailored for particular training and infere…
Ladder-residual: parallelism-aware architecture for accelerating large model inference with communication overlapping
Muru Zhang, Mayank Mishra, Zhongzhu Zhou +7
Large language model inference is both memory-intensive and time-consuming, often requiring distributed algorithms to efficiently scale. Various model parallelism strategies are us…
Fast Matrix Multiplications for Lookup Table-Quantized LLMs
Han Guo, William Brandon, Radostin Cholakov +3
The deployment of large language models (LLMs) is often constrained by memory bandwidth, where the primary bottleneck is the cost of transferring model parameters from the GPU's gl…
Reducing Transformer Key-Value Cache Size with Cross-Layer Attention
William Brandon, Mayank Mishra, Aniruddha Nrusimha +2
Key-value (KV) caching plays an essential role in accelerating decoding for transformer-based autoregressive large language models (LLMs). However, the amount of memory required to…
Hydra: Sequentially-Dependent Draft Heads for Medusa Decoding
Zachary Ankner, Rishab Parthasarathy, Aniruddha Nrusimha +3
To combat the memory bandwidth-bound nature of autoregressive LLM inference, previous research has proposed the speculative decoding frame-work. To perform speculative decoding, a…
Striped Attention: Faster Ring Attention for Causal Transformers
William Brandon, Aniruddha Nrusimha, Kevin Qian +4
To help address the growing demand for ever-longer sequence lengths in transformer models, Liu et al. recently proposed Ring Attention, an exact attention algorithm capable of over…