19
votes
Accepted
Linear Optimization Library for C++ with GPU Support
For context: most (if not all) major LP solvers are built on 2 algorithms: the simplex method, and the interior-point method.
The simplex method is intrinsically sequential: you're doing a lot of (...
- 4,068
15
votes
Which GPUs to get for Mathematical Optimization (if any)?
I've not seen any efficient use from GPU's for metaheuristics - only experiments that proved their inefficiency for these algorithms. So not the right tool for the job, apparently. Maybe there's a ...
- 4,685
14
votes
Which GPUs to get for Mathematical Optimization (if any)?
The first algorithm coming to mind that can benefit from GPUs is the Interior-Point Method (IPM), at its heart is the resolution of a linear system. See references:
GPU Acceleration of the Matrix-...
- 242
14
votes
Which GPUs to get for Mathematical Optimization (if any)?
If you problem is continuous I would say that it might be beneficial. For problems that involve discrete variables I've not seen anything that does benefit from the usage of a GPU.
GPUs aid problem ...
- 2,727
13
votes
CPLEX, number of threads and solving time
What you encounter is called performance variability, it was first (?) observed by Emilie Danna. Yes, B&B is an exact method, but during the run, a lot of heuristic decisions are taken, which ...
- 5,889
10
votes
Which GPUs to get for Mathematical Optimization (if any)?
A lot depends on what kinds of computations you are doing. The subject of this group is "Operations Research", but that surely includes a range of computational work including discrete event ...
- 781
10
votes
Accepted
RAM requirement for optimization problems
Easy answer: 64 GB
With 24 threads (is this already including hyperthreads? Maybe not, so we are actually talking about 48 threads...) you'll have about 2 and a half GB for every thread - that's not ...
- 1,538
8
votes
Fast way to repeatedly solve many similar LPs/QPs in parallel
The OPTMODEL modeling language in SAS (disclaimer: I work at SAS) supports two features for solving independent optimization (LP or otherwise) problems concurrently:
The COFOR loop, which ...
- 29.8k
8
votes
CPLEX, number of threads and solving time
I wouldn't call this "normal", but then I rarely use the term "normal" for anything involving MIPs. If the optimal solution from the quick run is close to the best bound from the long run, then yes, ...
- 37.5k
8
votes
Accepted
Parallel nonlinear solvers
As a developer of parallel non-linear software, I want to share my experience working in this space and the challenges we face. If I were to break down why we don't have more parallel non-linear ...
- 11.9k
8
votes
Which GPUs to get for Mathematical Optimization (if any)?
Which GPU, if any, should I get for mathematical optimization?
In the case of commercially available software, where no source code is available, you are stuck using the GPU that is better ...
- 2,098
7
votes
Gurobi and CPLEX cannot exploit more than 32 cores of machine
Modern CPUs are very complex and have at least two features that limit their scaling capability. The first one is a turbo feature that increases the clock speed when not all cores are utilized. The ...
- 171
7
votes
Gurobi and CPLEX cannot exploit more than 32 cores of machine
You need to distinguish between threads and (physical) cores. Is it possible that the cores you see in your machine are actually just hyperthreads, i.e. 2 cores resemble one physical core?
Furthermore,...
- 1,538
6
votes
Fast way to repeatedly solve many similar LPs/QPs in parallel
A very easy way to do this is to use multiprocessing alongside cvxpy. It won't be fastest possible, but since you want to stick ...
- 613
6
votes
Accepted
Fast way to repeatedly solve many similar LPs/QPs in parallel
After a good bit of experimentation based on the ideas posted, here was my solution:
Do as many matrix multiplications up front using pytorch on the GPU to simplify the problem. This means two things....
- 131
6
votes
Parallel nonlinear solvers
I think that there exist multiple solvers based on ADMM. If the variables can be partitioned in two sets in a way that the problem decomposes for one set fixed, then every other iteration can be ...
- 2,306
5
votes
Parallelization of an existing Adaptive Large Neighbourhood Search Heuristic
Another paradigm to parallelize search heuristics is the Backbone strategy. See for example this paper.
The main idea is to run multiple instances of an arbitrary heuristic in parallel, and then ...
- 2,856
5
votes
How to use solvers with virtual machines?
It depends on the solver and on the license type, but generally it is possible and you should reach out to the software provider directly to get more information.
Most solvers (I have seen this with ...
5
votes
Accepted
How to use solvers with virtual machines?
(Full disclosure: I founded Octeract)
So, a few things here:
In practice
Technologically speaking, of course you can (that's the point of a VM), unless a solver is using anti-virtualisation technology,...
- 11.9k
5
votes
Fast way to repeatedly solve many similar LPs/QPs in parallel
If I understand this correctly, you are solving 900 QPs (one for each combination of $i$ and $j$), tweaking the parameters, then solving all 900 again (and again). One possibility to try would be hot-...
- 37.5k
4
votes
When solving many MILPs how to assign CPU cores to solver instances?
So we know that MILP instances are independent and that the total throughput is to be maximized. In practice, increasing the number of threads used by a solver to solve a MILP instance could ...
- 2,140
3
votes
How to use solvers with virtual machines?
SAS solvers are part of the SAS Viya cloud platform and thus can be run in containers and virtual machines. The same is probably true for most commercial solvers.
But the benefits might be not as ...
- 3,168
3
votes
How to parallelize metaheuristics algorithms (Island Model)?
The easiest way is to use the Python multiprocessing module (or similar). You can create a pool of parallel workers, each of which would run a different heuristic. The multiprocessing toolbox also ...
- 11.9k
3
votes
CPLEX, number of threads and solving time
I had a similar observation while running my developed optimization framework (based on column generation) on different machines. Being new to this phenomenon, I was confused for days to see these ...
- 668
3
votes
Literature for building solver portfolios
For the automatic solver configuration, I know of this reference (there may be a journal version): A learning-based mathematical programming formulation for the automatic configuration of optimization ...
- 4,068
2
votes
Gurobi and CPLEX cannot exploit more than 32 cores of machine
Your screenshot here indicates to me that you have 32 physical cores, 64 threads, and 64 vCPUs. You observed that Gurobi and CPLEX are not making use of more than 32 cores, but you have not shown us ...
- 1,264
2
votes
Parallel nonlinear solvers
I've noticed that parallel (CPU or GPU) nonlinear programming solvers are few and far between.
The General Nonlinear Problem
The $n \times n$ nonlinear problem is:
$$\begin{array}
\mathcal{f}_1(x_1, ...
- 2,098
2
votes
Parallelization of an existing Adaptive Large Neighbourhood Search Heuristic
I've implemented reproducible parallelization on a number of Local Search variants with incremental score calculation (= delta constraint and fitness evaluation). Some of our requirements you might be ...
- 4,685
2
votes
Fast way to repeatedly solve many similar LPs/QPs in parallel
I suggest you consider the Parameterized Fusion API for MOSEK (available in Python). You can use it to construct your model without passing actual data for the parameter values, and then set the ...
- 141
2
votes
Accepted
When solving many MILPs how to assign CPU cores to solver instances?
As far as I know, solution speed for solvers is typically a sublinear function of the number of threads/cores. This makes sense since parallel processing requires additional effort (CPU cycles) to ...
- 37.5k
Only top scored, non community-wiki answers of a minimum length are eligible