Reorganizing a large parameter in GAMS

To export some fairly substantial data in a certain format I wanted to form:

DownLoadData(downloadfiles,SP_Varname,SP_SubVarName,SP_SubSubVarName,SP_Indicator,                                 SP_Description,SP_Source,SP_Unit,cty,Fwdy) =       sum((mapdownloadfiles(downloadfiles,SP_control),SP_Map,SP_Graph,SP_Options,SP_Type,mapy(FwdY,SP_year)),           cube(SP_Control,                SP_VarName,                SP_SubVarName,                SP_SubSubVarName,                SP_Year,                SP_Indicator,                SP_Source,                SP_Description,                SP_Unit,                SP_Map,                SP_Graph,                SP_Options,                SP_Type,                cty                ) );

The original Cube parameter has 326,755 elements, and this operation basically reshuffles things into a lower-dimensional parameter with some mappings applied. Upon completion the new parameter Download_Data has the same number of elements: 326,755. Unfortunately this operation was very expensive, so much so that I never saw it finish.

The following code does exactly the same in just 0.5 seconds:

loop((mapdownloadfiles(downloadfiles,SP_control),mapy(Download_Year,SP_year)),    Download_Data(downloadfiles,SP_Varname,SP_SubVarName,SP_SubSubVarName,cty,SP_Indicator,                              SP_Description,SP_Source,SP_Unit,Download_Year) =         sum((SP_Map,SP_Graph,SP_Options,SP_Type),           cube(SP_Control,                SP_VarName,                SP_SubVarName,                SP_SubSubVarName,                SP_Year,                SP_Indicator,                SP_Source,                SP_Description,                SP_Unit,                SP_Map,                SP_Graph,                SP_Options,                SP_Type,                cty                ) ); );

GAMS has some built-in facilities to automatically rearrange assignments like these to achieve better performance, but in this case it was not very effective. So human intervention was needed to optimize this assignment.