# GPU computing in discrete optimization. Part II: Survey focused on routing problems

@article{Schulz2013GPUCI, title={GPU computing in discrete optimization. Part II: Survey focused on routing problems}, author={Christian Schulz and Geir Hasle and Andr{\'e} Rigland Brodtkorb and Trond Runar Hagen}, journal={EURO Journal on Transportation and Logistics}, year={2013}, volume={2}, pages={159-186} }

In many cases there is still a large gap between the performance of current optimization technology and the requirements of real-world applications. As in the past, performance will improve through a combination of more powerful solution methods and a general performance increase of computers. These factors are not independent. Due to physical limits, hardware development no longer results in higher speed for sequential algorithms, but rather in increased parallelism. Modern commodity PCs…

## 43 Citations

GPU computing in discrete optimization. Part I: Introduction to the GPU

- Computer ScienceEURO J. Transp. Logist.
- 2013

This paper is the first part of a series of two, where the goal of this first part is to give a tutorial style introduction to modern PC architectures and GPU programming, and a broad survey of the existing literature on parallel computing targeted at modern PCs in discrete optimization.

Parallel local search on GPU and CPU with OpenCL

- Computer Science
- 2015

This work proposes a parallelization in an iterative level of a local search through two popular neighborhood structures and applies it to some combinatorial problems and adapted for the GPU platform.

CHAPTER 10 GPU computing appliedto linear and mixed-integerprogramming

- Computer Science
- 2018

Looking at the GPU computing accelerators previously released (some of which are presented in Table 1, which also summarizes the characteristics of GPUs considered in this chapter), the progress accomplished during one decade is measured.

Adaptive Large Neighborhood Search on the Graphics Processing Unit

- Computer ScienceEur. J. Oper. Res.
- 2019

FANG: Fast and Efficient Successor-State Generation for Heuristic Optimization on GPUs

- Computer ScienceICA3PP
- 2019

A GPU-based method called FANG is presented that implements a generic and reusable N(x) for arbitrary domains in the field of combinatorial optimization that can be customized to satisfy domain-specific requirements and leverages the underlying hardware in a fast and efficient way by construction.

Using GPU Computing for Solving the Two-Dimensional Guillotine Cutting Problem

- Computer ScienceINFORMS J. Comput.
- 2016

This paper investigates the application of GPU computing to the two-dimensional guillotine cutting problem, solved by dynamic programming, and shows the effectiveness of the dynamic programming approach based on GPU computing for this problem.

Toward efficient parallel routing optimization for large-scale SDN networks using GPGPU

- Computer ScienceJ. Netw. Comput. Appl.
- 2018

Testing Fine-Grained Parallelism for the ADMM on a Factor-Graph

- Computer Science2016 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)
- 2016

This work proposes a problem-independent scheme of accelerating the Alternating Direction Method of Multipliers that can automatically exploit fine-grained parallelism both in GPUs and shared-memory multi-core computers and achieves significant speedup in such diverse application domains as combinatorial optimization, machine learning, and optimal control.

## References

SHOWING 1-10 OF 108 REFERENCES

GPU computing in discrete optimization. Part I: Introduction to the GPU

- Computer ScienceEURO J. Transp. Logist.
- 2013

This paper is the first part of a series of two, where the goal of this first part is to give a tutorial style introduction to modern PC architectures and GPU programming, and a broad survey of the existing literature on parallel computing targeted at modern PCs in discrete optimization.

General Parallel Computation on Commodity Graphics Hardware: Case Study with the All-Pairs Shortest Paths Problem

- Computer SciencePDPTA
- 2004

The implementation of the Warshall-Floyd algorithm on a class of GPUs, which utilizes interpolators, vertex and fragment pipelines, as well as vector operations to maximize performance, is described.

Linear optimization on modern GPUs

- Computer Science2009 IEEE International Symposium on Parallel & Distributed Processing
- 2009

This paper shows how a revised simplex algorithm for solving linear programming problems originally described by Dantzig is used for both the CPU and GPU implementations, and shows that it can scale to problem sizes up to at least 2000 variables.

Tabu Search on GPU

- Computer ScienceJ. Univers. Comput. Sci.
- 2008

This paper presents the imple- mentation of two optimization algorithms based on the tabu search technique, namely for the traveling salsesman problem and the flow shop scheduling problem, implemented in two versions and utilizing multi-core CPU, and GPU.

Accelerating Large Graph Algorithms on the GPU Using CUDA

- Computer ScienceHiPC
- 2007

This work presents a few fundamental algorithms - including breadth first search, single source shortest path, and all-pairs shortest path - using CUDA on large graphs using the G80 line of Nvidia GPUs.

Parallel Local Search on GPU

- Computer Science
- 2009

This paper presents a new methodology to design and implement local search algorithms on GPU that can be extended with a hybrid multi-core and multi-GPU approach for multiple local search methods such as multistart.

Tabu Search with two approaches to parallel flowshop evaluation on CUDA platform

- Business, Computer ScienceJ. Parallel Distributed Comput.
- 2011

Designing Efficient Many-Core Parallel Algorithms for All-Pairs Shortest-Paths Using CUDA

- Computer Science2010 Seventh International Conference on Information Technology: New Generations
- 2010

This paper presents the designs of two efficient parallel algorithms for many-core GPUs using CUDA that expose substantial fine-grained parallelism while maintaining minimal global communication.

GPU Computing for Parallel Local Search Metaheuristic Algorithms

- Computer ScienceIEEE Transactions on Computers
- 2013

A new guideline for the design and implementation of effective LSMs on GPU is introduced and very efficient approaches are proposed for CPU-GPU data transfer optimization, thread control, mapping of neighboring solutions to GPU threads, and memory management.