908 citations · 2.4k across the 149 of their papers we have counts for
14 papers · 1 filter
PathCAS: An Efficient Middle Ground for Concurrent Search Data Structures
Trevor Brown, William Sigouin, Dan Alistarh
To maximize the performance of concurrent data structures, researchers have often turned to highly complex fine-grained techniques, resulting in efficient and elegant algorithms, w…
Fast and Scalable Channels in Kotlin Coroutines
Nikita Koval, Dan Alistarh, Roman Elizarov
Asynchronous programming has gained significant popularity over the last decade: support for this programming pattern is available in many popular languages via libraries and nativ…
L-GreCo: Layerwise-Adaptive Gradient Compression for Efficient and Accurate Deep Learning
Mohammadreza Alimohammadi, Ilia Markov, Elias Frantar +1
Data-parallel distributed training of deep neural networks (DNN) has gained very widespread adoption, but can still experience communication bottlenecks. To address this issue, ent…
GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers
Elias Frantar, Saleh Ashkboos, Torsten Hoefler +1
Generative Pre-trained Transformer models, known as GPT or OPT, set themselves apart through breakthrough performance across complex language modelling tasks, but also by their ext…
CAP: Correlation-Aware Pruning for Highly-Accurate Sparse Vision Models
Denis Kuznedelev, Eldar Kurtic, Elias Frantar +1
Driven by significant improvements in architectural design and training pipelines, computer vision has recently experienced dramatic progress in terms of accuracy on classic benchm…
Hybrid Decentralized Optimization: Leveraging Both First- and Zeroth-Order Optimizers for Faster Convergence
Matin Ansaripour, Shayan Talaei, Giorgi Nadiradze +1
Distributed optimization is the standard way of speeding up machine learning training, and most of the research in the area focuses on distributed first-order, gradient-based metho…