2.3k citations · 2.7k across the 17 of their papers we have counts for
18 papers
Packrat: Automatic Reconfiguration for Latency Minimization in CPU-based DNN Serving
Ankit Bhardwaj, Amar Phanishayee, Deepak Narayanan +2
In this paper, we investigate how to push the performance limits of serving Deep Neural Network (DNN) models on CPU-based servers. Specifically, we observe that while intra-operato…
MGit: A Model Versioning and Management System
Wei Hao, Daniel Mendoza, Rafael da Silva +2
Models derived from other models are extremely common in machine learning (ML) today. For example, transfer learning is used to create task-specific models from "pre-trained" model…
Cheaply Evaluating Inference Efficiency Metrics for Autoregressive Transformer APIs
Deepak Narayanan, Keshav Santhanam, Peter Henderson +3
Large language models (LLMs) power many state-of-the-art systems in natural language processing. However, these models are extremely computationally expensive, even at inference ti…
MegaBlocks: Efficient Sparse Training with Mixture-of-Experts
Trevor Gale, Deepak Narayanan, Cliff Young +1
We present MegaBlocks, a system for efficient Mixture-of-Experts (MoE) training on GPUs. Our system is motivated by the limitations of current frameworks, which restrict the dynami…
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
BigScience Workshop, :, Teven Le Scao +391
Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to wi…
Solving Large-Scale Granular Resource Allocation Problems Efficiently with POP
Deepak Narayanan, Fiodar Kazhamiaka, Firas Abuzaid +5
Resource allocation problems in many computer systems can be formulated as mathematical optimization problems. However, finding exact solutions to these problems using off-the-shel…