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installation:what_is_the_recommended_machine_processing_speed_for_running_gams [2013/01/03 15:12]
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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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 === 32bit vs 64bit === === 32bit vs 64bit ===
  
-GAMS is available in a 32 bit and a 64 bit version for Windows, Linux, and Solaris. The 64 bit Windows version requires ​ a 64 bit version of Windows to operate properly - just a 64 bit processor is not sufficient. The 32 bit Windows version of GAMS will work on both versions of Windows, but the operating system limits the GAMS and solver process to  approximately ​ 1.7 GB (Windows 32 Bit) or approximately ​ 4 GB (Windows 64 Bit). The 64 bit version of GAMS can address much more memory -  only the (physically) available RAM will limit the size of your model. Please note that some solvers are not available as a 64 bit version and some NLP solvers still have a [[solver:​fatal_error_insufficient_memory_for_setup_of_optimization_vectors| limit of 8 GB]].+GAMS is available in a 32 bit and a 64 bit version for Windows. All other platforms are only available in 64 bit. The 64 bit Windows version requires ​ a 64 bit version of Windows to operate properly - just a 64 bit processor is not sufficient. The 32 bit Windows version of GAMS will work on both versions of Windows, but the operating system limits the GAMS and solver process to  approximately ​ 1.7 GB (Windows 32 Bit) or approximately ​ 4 GB (Windows 64 Bit). The 64 bit version of GAMS can address much more memory -  only the (physically) available RAM will limit the size of your model. Please note that some solvers are not available as a 64 bit version and some NLP solvers still have a [[solver:​fatal_error_insufficient_memory_for_setup_of_optimization_vectors| limit of 8 GB]].
  
 In general, you should not expect a major performance improvement from the 64 bit version. The benefit is being able to solve larger models. ​ Please note that the 64bit version of GAMS may require up to 35% more RAM than the 32 bit version. Sometimes it may be advantageously to run the 32 bit version of GAMS on a 64 bit version of the operating system. ​ In general, you should not expect a major performance improvement from the 64 bit version. The benefit is being able to solve larger models. ​ Please note that the 64bit version of GAMS may require up to 35% more RAM than the 32 bit version. Sometimes it may be advantageously to run the 32 bit version of GAMS on a 64 bit version of the operating system. ​
 +
 +The free mem utility will tell you, how much memory is available to a process. It allocates memory in one MB chunks and stops when the OS refuses to provide more memory. ​
 +  * {{:​installation:​mem32.zip|32 bit version for Windows}}
 +  * {{:​installation:​mem64.zip|64 bit version for Windows}}
 +  * {{:​installation:​mem_source.zip| source (C)}}
  
 === What computer should I buy for my model? === === What computer should I buy for my model? ===
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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
 +
 +~~SHORTURL~~