Questions tagged [parallel-computing]
In parallel computing a computational task is typically broken down into many sub-tasks that can be processed independently and whose results are combined upon completion. It is also called multiprocessing. In serial computing a single core multitasks all or part of a series of instructions for a short period of time before switching to another task, giving the appearance of all the tasks running simultaneously even though they run one at a time.
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Pygmo2: What is the point of evolving an archipelago in a loop if number of generations already set in algo
I want to solve a multi-objective problem with nsga2 or moead taking advantage of the parallelism available in pygmo library. I have seen a very nice example on github posted below. However I am not ...
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Fast way to repeatedly solve many similar LPs/QPs in parallel
I am running a simulation where I need to repeatedly solve a set of LPs or QPs with slightly different input parameters for a Model Predictive Control application. The problem is I need it to be fast, ...
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RAM requirement for optimization problems
I understand that RAM required for optimization problem is problem specific and some problems require much more memory. I am thinking how much RAM I need for my system and need to decide between ...
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When solving many MILPs how to assign CPU cores to solver instances?
Let's say I have an 16 core machine and many roughly equally hard MILP problems and enough RAM. The latency of solving doesn't matter. With how many cores should I start solvers on to maximize ...
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Linear Optimization Library for C++ with GPU Support
Does anyone know any linear optimization libraries for C++ supporting GPUs for parallelization? If multiple, which do you recommend?
The GPU support is important to me since I am dealing with large ...
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Literature for building solver portfolios
A portfolio of solvers is a collection of solvers with different configurations that share CPU time, CPU cores, and memory resources and try to get an answer as soon as possible. Solvers might also ...
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Gurobi and CPLEX cannot exploit more than 32 cores of machine
I have some attempts to solve a scheduling problem using the Gurobi and doCPLEX API in python and .NET on Ubuntu-server installed on a hyper-computing cluster with 64 physical cores. Unfortunately, ...
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How to use solvers with virtual machines?
Is there any way to install and use commercial solvers (for which I have a license) on some virtual machines?
For example, if it's possible to install the Octeract engine student version on a google ...
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How to parallelize metaheuristics algorithms (Island Model)?
I have different metaheuristics (Tabu search, Simulated annealing, Iterated local search) solving an optimization problem (a variant of the resource-constrained project scheduling problem). Each ...
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CPLEX, number of threads and solving time
Using CPLEX via its Python API, I encountered a "weird" behavior.
For some instances, with a limited number of threads (10 in my tests), the instances cannot be solved after 10 days (afterwards, the ...
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Parallel nonlinear solvers
I've noticed that parallel (CPU or GPU) nonlinear programming solvers are few and far between. It seems that if any parallelization is involved at all, it generally applies to solving the underlying ...
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Which GPUs to get for Mathematical Optimization (if any)?
The Machine Learning community has largely benefited from modern GPUs and several large companies are investing in new dedicated hardware.
Unfortunately, academic and commercial mathematical ...
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Parallelization of an existing Adaptive Large Neighbourhood Search Heuristic
I am solving (or say, trying to find good solutions for) an arbitrary combinatorial optimization problem, think of it as a Vehicle Routing Problem with a bunch of side constraints that are not ...
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