Linear optimization on modern GPUs

Spampinato, Daniele G.; Elster, Anne C.

doi:10.1109/ipdps.2009.5161106

Cited by 33 publications

(21 citation statements)

References 9 publications

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“…Spampinato et al [31] have proposed a parallel implementation of the revised simplex method based on NVIDIA CUBLAS and LAPACK libraries, with a maximum speedup of 2.5, using a GTX 280 GPU vs. the sequential implementation on CPU with Intel Core2 Quad 2.83 GHz for randomly generated LP problems of size 2000x2000. In [32] another implementation of the revised simplex method on GPU was proposed, which permits one to speed up solution with a maximum factor of 18 in single precision on a GeForce 9600 GT GPU card as compared with GLPK solver run on Intel Core 2 Duo 3GHz CPU.…”

Section: Related Workmentioning

confidence: 99%

Simplex Parallelization in a Fully Hybrid Hardware Platform

Mamalis¹,

Perlitis²

2017

ijacsa

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Abstract-The simplex method has been successfully used in solving linear programming (LP) problems for many years. Parallel approaches have also extensively been studied due to the intensive computations required, especially for the solution of large LP problems. Furthermore, the rapid proliferation of multicore CPU architectures as well as the computational power provided by the massive parallelism of modern GPUs have turned CPU / GPU collaboration models increasingly into focus over the last years for better performance. In this paper, a highly scalable implementation framework of the standard full tableau simplex method is first presented, over a hybrid parallel platform which consists of multiple multicore nodes interconnected via a high-speed communication network. The proposed approach is based on the combined use of MPI and OpenMP, adopting a suitable column-based distribution scheme for the simplex tableau. The parallelization framework is then extended in such a way that it can exploit concurrently the full power of the provided resources on a multicore single-node environment with a CUDA-enabled GPU (i.e. using the CPU cores and the GPU concurrently), based on a suitable hybrid multithreading/GPU offloading scheme with OpenMP and CUDA. The corresponding experimental results show that the hybrid MPI+OpenMP based parallelization scheme leads to particularly high speed-up and efficiency values, considerably better than in other competitive approaches, and scaling well even for very large / huge linear problems. Furthermore, the performance of the hybrid multithreading/GPU offloading scheme is clearly superior to both the OpenMP-only and the GPU-only based implementations in almost all cases, which validates the worth of using both resources concurrently. The most important, when it is used in combination with MPI in a multi-node (fully hybrid) environment, it leads to substantial improvements in the speedup achieved for large and very large LP problems.

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Section: Related Workmentioning

confidence: 99%

Simplex Parallelization in a Fully Hybrid Hardware Platform

Mamalis¹,

Perlitis²

2017

ijacsa

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“…Thus, we can perform parallel optimization tasks on co-processors concurrently to query processing without slowing down the DBMS. Other com- munities already use co-processors as accelerators for their optimization problems successfully [12,25,32,34,37]. This is a strong indicator that DBMSs can also benefit from parallel optimization on co-processors, as it was already shown for the selectivity estimation problem [2][3][4]21].…”

Section: Introductionmentioning

confidence: 92%

“…Currently, GPUs are only used for query processing and selectivity estimation in DBMSs. Other communities use GPUs in a variety of different optimization problems, such as ant colony optimization [12], fast circuit optimization [25], knapsack optimization [32], linear optimization [34], genetic algorithms [35] or particle swarm optimization [37].…”

Section: Gpu-accelerated Optimizationmentioning

confidence: 99%

Toward GPU-accelerated Database Optimization

2015

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For over three decades, research investigates optimization options in DBMSs. Nowadays, the hardware used in DBMSs become more and more heterogeneous, because processors are bound by a fixed energy budget leading to increased parallelism. Existing optimization approaches in DBMSs do not exploit parallelism for a single optimization task and, hence, can only benefit from the parallelism offered by current hardware by batch-processing multiple optimization tasks.Since a large optimization space often allows us to process sub-spaces in parallel, we expect large gains in result quality for optimization approaches in DBMSs and, hence, performance for query processing on modern (co-)processors. However, parallel optimization on CPUs is likely to slow down query processing, because DBMSs can fully exploit the CPUs computing resources due to high data parallelism. In contrast, the communication overhead of co-processors such as GPUs typically lead to plenty of compute resources unused.In this paper, we motivate the use of parallel co-processors for optimization in DBMSs, identify optimization problems benefiting from parallelization, and show how we can design parallel optimization approaches on the example of the operator placement problem.A. Meister ( ) · G. Saake

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“…Our framework can be applied at the price of a higher complexity (up to several hours). One may note that the analysis of the BnB subdivided spaces, the LP solver, and the MBM can be all parallelized [32], [33], [34]. Therefore, if necessary, an appropriate GPU implementation could greatly accelerate the processing speed.…”

Section: Real Datamentioning

confidence: 99%

A Branch-and-Bound Approach to Correspondence and Grouping Problems

Bazin

Kweon

et al. 2013

IEEE Trans. Pattern Anal. Mach. Intell.

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Abstract-Data correspondence/grouping under an unknown parametric model is a fundamental topic in computer vision. Finding feature correspondences between two images is probably the most popular application of this research field, and is the main motivation of our work. It is a key ingredient for a wide range of vision tasks, including three-dimensional reconstruction and object recognition. Existing feature correspondence methods are based on either local appearance similarity or global geometric consistency or a combination of both in some heuristic manner. None of these methods is fully satisfactory, especially in the presence of repetitive image textures or mismatches. In this paper, we present a new algorithm that combines the benefits of both appearance-based and geometry-based methods and mathematically guarantees a global optimization. Our algorithm accepts the two sets of features extracted from two images as input, and outputs the feature correspondences with the largest number of inliers, which verify both the appearance similarity and geometric constraints. Specifically, we formulate the problem as a mixed integer program and solve it efficiently by a series of linear programs via a branch-and-bound procedure. We subsequently generalize our framework in the context of data correspondence/grouping under an unknown parametric model and show it can be applied to certain classes of computer vision problems. Our algorithm has been validated successfully on synthesized data and challenging real images.

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Linear optimization on modern GPUs

Cited by 33 publications

References 9 publications

Simplex Parallelization in a Fully Hybrid Hardware Platform

Simplex Parallelization in a Fully Hybrid Hardware Platform

Toward GPU-accelerated Database Optimization

A Branch-and-Bound Approach to Correspondence and Grouping Problems

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