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Quantum Integer Programming
Quantum Integer Programming

FastDOG: Fast Discrete Optimization on GPU
FastDOG: Fast Discrete Optimization on GPU

HetPipe: Enabling Large DNN Training on (Whimpy) Heterogeneous GPU Clusters  through Integration of Pipelined Model Parallelism and Data Parallelism |  DeepAI
HetPipe: Enabling Large DNN Training on (Whimpy) Heterogeneous GPU Clusters through Integration of Pipelined Model Parallelism and Data Parallelism | DeepAI

Mining the CPLEX Node Log for Faster MIP Performance
Mining the CPLEX Node Log for Faster MIP Performance

Mining the CPLEX Node Log for Faster MIP Performance
Mining the CPLEX Node Log for Faster MIP Performance

Mapping and configuration in a CPU-GPU system | Download Scientific Diagram
Mapping and configuration in a CPU-GPU system | Download Scientific Diagram

Introduction to OpenACC - UL HPC Tutorials
Introduction to OpenACC - UL HPC Tutorials

Jonas Velasco (@jonasovich) / Twitter
Jonas Velasco (@jonasovich) / Twitter

IBM ILOG CPLEX Optimization Studio vs. NVIDIA RAPIDS Comparison
IBM ILOG CPLEX Optimization Studio vs. NVIDIA RAPIDS Comparison

Integer programming based heterogeneous CPU–GPU cluster schedulers for  SLURM resource manager - ScienceDirect
Integer programming based heterogeneous CPU–GPU cluster schedulers for SLURM resource manager - ScienceDirect

FastDOG: Fast Discrete Optimization on GPU
FastDOG: Fast Discrete Optimization on GPU

arXiv:1802.08557v1 [cs.DC] 21 Feb 2018
arXiv:1802.08557v1 [cs.DC] 21 Feb 2018

Applying an annealing algorithm to the collision avoidance problem in a  congested straits
Applying an annealing algorithm to the collision avoidance problem in a congested straits

optimization - Gurobi and CPLEX cannot exploit more than 32 cores of  machine - Operations Research Stack Exchange
optimization - Gurobi and CPLEX cannot exploit more than 32 cores of machine - Operations Research Stack Exchange

PDF) Solving Very Large Optimization Problems (Up to One Billion Variables)  with a Parallel Evolutionary Algorithm in CPU and GPU | S. Nesmachnow and  S. Iturriaga - Academia.edu
PDF) Solving Very Large Optimization Problems (Up to One Billion Variables) with a Parallel Evolutionary Algorithm in CPU and GPU | S. Nesmachnow and S. Iturriaga - Academia.edu

IBM ILOG CPLEX Optimization Studio vs. NVIDIA RAPIDS Comparison
IBM ILOG CPLEX Optimization Studio vs. NVIDIA RAPIDS Comparison

Speedup of dwsolver versus CPLEX over a variety of parameters. | Download  Scientific Diagram
Speedup of dwsolver versus CPLEX over a variety of parameters. | Download Scientific Diagram

Cplex - Gurobi - UL HPC Tutorials
Cplex - Gurobi - UL HPC Tutorials

On the Efficiency of Supernodal Factorization in Interior-Point Method  Using CPU-GPU Collaboration
On the Efficiency of Supernodal Factorization in Interior-Point Method Using CPU-GPU Collaboration

GitHub - mikeroyal/CUDA-Guide: CUDA Guide
GitHub - mikeroyal/CUDA-Guide: CUDA Guide

Mining the CPLEX Node Log for Faster MIP Performance
Mining the CPLEX Node Log for Faster MIP Performance

Systems And Methods For Safe And Reliable Autonomous Vehicles DITTY;  Michael Alan ; et al. [NVIDIA Corporation]
Systems And Methods For Safe And Reliable Autonomous Vehicles DITTY; Michael Alan ; et al. [NVIDIA Corporation]

アニーリングマシンを用いた 船舶衝突回避アプリケーションの開発
アニーリングマシンを用いた 船舶衝突回避アプリケーションの開発

アニーリングマシンを用いた 船舶衝突回避アプリケーションの開発
アニーリングマシンを用いた 船舶衝突回避アプリケーションの開発

Features (detail) view - Unity マニュアル
Features (detail) view - Unity マニュアル

Applying an annealing algorithm to the collision avoidance problem in a  congested straits
Applying an annealing algorithm to the collision avoidance problem in a congested straits