Valeriu Codreanu

Reduced Precision Strategies for Deep Learning: A High Energy Physics Generative Adversarial Network Use Case

Deep learning is finding its way into high energy physics by replacing traditional Monte Carlo simulations. However, deep learning still requires an excessive amount of computational resources. A...

A Holistic Analysis of Datacenter Operations: Resource Usage, Energy, and Workload Characterization -- Extended Technical Report

Improving datacenter operations is vital for the digital society. We posit that doing so requires our community to shift, from operational aspects taken in isolation to holistic analysis of...

CLTune: A Generic Auto-Tuner for OpenCL Kernels

This work presents CLTune, an auto-tuner for OpenCL kernels. It evaluates and tunes kernel performance of a generic, user-defined search space of possible parameter-value combinations. Example...

Scale out for large minibatch SGD: Residual network training on ImageNet-1K with improved accuracy and reduced time to train

For the past 5 years, the ILSVRC competition and the ImageNet dataset have attracted a lot of interest from the Computer Vision community, allowing for state-of-the-art accuracy to grow...

Deep-learning enhancement of large scale numerical simulations

Traditional simulations on High-Performance Computing (HPC) systems typically involve modeling very large domains and/or very complex equations. HPC systems allow running large models, but limits...

Efficient implementation of a video change detection algorithm

Learning to Evaluate Chess Positions with Deep Neural Networks and Limited Lookahead

Comparative study between deep learning and bag of visual words for wild-animal recognition

GPU-ASIFT: A fast fully affine-invariant feature extraction algorithm

DeepGalaxy: Deducing the Properties of Galaxy Mergers from Images Using Deep Neural Networks

DDLBench: Towards a Scalable Benchmarking Infrastructure for Distributed Deep Learning

Scaling Out Transformer Models for Retrosynthesis on Supercomputers

Distributed Training of Generative Adversarial Networks for Fast Detector Simulation

Densifying Assumed-Sparse Tensors - Improving Memory Efficiency and MPI Collective Performance During Tensor Accumulation for Parallelized Training of Neural Machine Translation Models

Evaluation of Autoparallelization Toolkits for Commodity GPUs

Densifying Assumed-sparse Tensors: Improving Memory Efficiency and MPI Collective Performance during Tensor Accumulation for Parallelized Training of Neural Machine Translation Models

Neural machine translation - using neural networks to translate human language - is an area of active research exploring new neuron types and network topologies with the goal of dramatically...