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installation:what_is_the_recommended_machine_processing_speed_for_running_gams [2014/07/17 15:04]
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installation:what_is_the_recommended_machine_processing_speed_for_running_gams [2020/10/20 21:18]
Atharv Bhosekar removed
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 When using a 64bit operation system and GAMS version, then also having as much RAM as possible is good. Modern solvers can also swap to harddisk if running out of memory while solving large LPs or QPs. So a fast hard drive  can also be useful. When using a 64bit operation system and GAMS version, then also having as much RAM as possible is good. Modern solvers can also swap to harddisk if running out of memory while solving large LPs or QPs. So a fast hard drive  can also be useful.
  
-The amount of memory required to solve a model is sometimes difficult to predict. For convex models (LPs, convex QPs, convex NLPs) or solvers that find only local optima (local NLP solvers, MCP solvers), the memory requirement usually depends on the size of the model (number of variables, equations, nonzeros) and numerical properties (matrix fill-in). For branch-and-bound based solvers (MIP solvers, global NLP solvers, many of the MINLP solvers), additionally the difficulty of the problem dictates memory requirement. That is, as more branch-and-bound tree nodes need to be enumerated, as more memory is required. ​CPLEX is also able to use the harddisk to store parts of the branch-and-bound tree (nodefileind option) when memory is low.+The amount of memory required to solve a model is sometimes difficult to predict. For convex models (LPs, convex QPs, convex NLPs) or solvers that find only local optima (local NLP solvers, MCP solvers), the memory requirement usually depends on the size of the model (number of variables, equations, nonzeros) and numerical properties (matrix fill-in). For branch-and-bound based solvers (MIP solvers, global NLP solvers, many of the MINLP solvers), additionally the difficulty of the problem dictates memory requirement. That is, as more branch-and-bound tree nodes need to be enumerated, as more memory is required. ​Some solvers are also able to use the harddisk to store parts of the branch-and-bound tree (nodefileind option) when memory is low.
  
 === Summary === === Summary ===
 First priority should be a CPU with fast cores and fast memory access (maybe via large L2/L3 caches). CPUs with 2 or 4 cores are standard by today, having more than 8 cores may not pay off. Since RAM is cheap, something like 16 GB may be a good option, with a possibility to upgrade later. First priority should be a CPU with fast cores and fast memory access (maybe via large L2/L3 caches). CPUs with 2 or 4 cores are standard by today, having more than 8 cores may not pay off. Since RAM is cheap, something like 16 GB may be a good option, with a possibility to upgrade later.
 +
 +=== Links ===
 +
 +  * Gurobi FAQ: http://​www.gurobi.com/​support/​faqs#​CHS
  
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