Maxim Naumov

According to our database1, Maxim Naumov authored at least 30 papers between 2004 and 2021.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.



In proceedings 
PhD thesis 




Low-Precision Hardware Architectures Meet Recommendation Model Inference at Scale.
CoRR, 2021

High-performance, Distributed Training of Large-scale Deep Learning Recommendation Models.
CoRR, 2021

Time-based Sequence Model for Personalization and Recommendation Systems.
CoRR, 2020

Deep Learning Training in Facebook Data Centers: Design of Scale-up and Scale-out Systems.
CoRR, 2020

Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation Systems.
Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020

Building Recommender Systems with PyTorch.
Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020

RecNMP: Accelerating Personalized Recommendation with Near-Memory Processing.
Proceedings of the 47th ACM/IEEE Annual International Symposium on Computer Architecture, 2020

The Architectural Implications of Facebook's DNN-Based Personalized Recommendation.
Proceedings of the IEEE International Symposium on High Performance Computer Architecture, 2020

Mixed Dimension Embeddings with Application to Memory-Efficient Recommendation Systems.
CoRR, 2019

The Architectural Implications of Facebook's DNN-based Personalized Recommendation.
CoRR, 2019

Deep Learning Recommendation Model for Personalization and Recommendation Systems.
CoRR, 2019

Spatial-Winograd Pruning Enabling Sparse Winograd Convolution.
CoRR, 2019

On the Dimensionality of Embeddings for Sparse Features and Data.
CoRR, 2019

Bandana: Using Non-Volatile Memory for Storing Deep Learning Models.
Proceedings of Machine Learning and Systems 2019, 2019

Deep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations and Hardware Implications.
CoRR, 2018

On Periodic Functions as Regularizers for Quantization of Neural Networks.
CoRR, 2018

A Block-Oriented, Parallel and Collective Approach to Sparse Indefinite Preconditioning on GPUs.
Proceedings of the 8th IEEE/ACM Workshop on Irregular Applications: Architectures and Algorithms, 2018

Parallel Complexity of Forward and Backward Propagation.
CoRR, 2017

AdaBatch: Adaptive Batch Sizes for Training Deep Neural Networks.
CoRR, 2017

Feedforward and Recurrent Neural Networks Backward Propagation and Hessian in Matrix Form.
CoRR, 2017

Parallel Depth-First Search for Directed Acyclic Graphs.
Proceedings of the Seventh Workshop on Irregular Applications: Architectures and Algorithms, 2017

Parallel jaccard and related graph clustering techniques.
Proceedings of the 8th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems, 2017

Parallel Modularity Clustering.
Proceedings of the International Conference on Computational Science, 2017

AmgX: A Library for GPU Accelerated Algebraic Multigrid and Preconditioned Iterative Methods.
SIAM J. Sci. Comput., 2015

On the modification of an eigenvalue problem that preserves an eigenspace.
J. Comput. Appl. Math., 2011

A tearing-based hybrid parallel sparse linear system solver.
J. Comput. Appl. Math., 2010


Stability of Semi-implicit Atmospheric Models with Respect to the Reference Temperature Profile.
Proceedings of the Numerical Methods and Applications, 6th International Conference, 2006

Semi-Lagrangian Scale Selective Two-Time-Level Scheme for Hydrostatic Atmospheric Model.
Proceedings of the Computational Science, 2006

On correctness of the vertical discretization in numerical weather prediction models.
Appl. Math. Comput., 2004