A customer involved in research and development of renewable energy requested us to propose a PC configuration.
The client is considering purchasing a PC to use a mixed integer linear programming solver (MILP) in MATLAB, and is looking for a configuration that can solve MILP problems as quickly as possible within a budget of 100 million yen.
We have also received questions regarding whether the proposed PC configuration is suitable for use with the solver in Gurobi Optimizer.
Taking these discussions into consideration, we proposed the following structure:
| CPU | Intel Core i9-14900K (3.20GHz 8 cores + 2.40GHz 16 cores) |
| memory | 128GB (32GB x 4) |
| storage | 1TB SSD S-ATA |
| Video | on board |
| network | on board (2.5GBase-T x1) Wi-Fi x1 |
| Housing + power supply | Middle tower type housing + 850W |
| OS | Microsoft Windows 11 Professional 64bit |
Parts configuration according to the software used
I assume that the MILP you mentioned was run using "intlinprog".
Since "intlinprog" does not support calculations using GPUs or parallel calculations using multiple cores, we recommend a configuration that uses the "Core i1-9K 14900 cores", which has the fastest CPU speed per core within your budget.
The maximum memory capacity for this configuration is 128GB and cannot be expanded.
Performing multiple calculations simultaneously requires a large amount of memory, so depending on the calculation content, memory capacity may be insufficient.
If you require more than 128GB of memory capacity with the same number of CPU cores, you can increase the upper limit of memory capacity by changing to two 12-core Xeon series CPUs.
*In this case, the proposal was not adopted because it would have significantly exceeded the budget.
Using solvers in Gurobi Optimizer
The Gurobi Solver's calculation processing has an advantage over CPUs with high clock speeds, but the number of CPU cores and memory capacity required will vary depending on the model you plan to optimize.
This is because the Gurobi Solver performs calculations by combining multiple algorithms, and the effectiveness of parallelization varies for each algorithm.
Hardware selection guidelines are posted on manufacturer websites, so please consider the size of the machine you need based on the actual processing content and usage scenario.
The configuration of this case study is based on the conditions given by the customer.
Please feel free to contact us even if you are considering different conditions from what is posted.
■ Keywords・What is MATLAB? ・What is Gurobi Optimizer? A linear/integer programming solver that is widely used around the world, is applicable to a wide range of problems, and incorporates high-performance mathematical optimization techniques.
|
|
■ Click here for details and inquiries about this PC case
* Please enter the name of the case or your desired conditions. |
