Power Query doesn't use 100% of the processor

I think I understand your requirements now. I'm pretty sure you can achieve some performnce improvements by leveraging Group, still, but there are a couple extra steps. To test performance better, I switched to randomly generated data to test 10k and 100k rows in the structure you specified above for your testing.

 

The below works pretty well, just takes 1-2 seconds to load. Approach is to merge grouped rows (when grouped on a column, that column becomes primary key, which improve join performance), filter merged grouped rows as needed, sum values for running total, then do a second group to get the min:

 

let
    Source = PerfTest_10k,
    MaterialGroups = Table.Group(
        Source, 
        {"Material"}, 
        {{
            "Current qty", 
            each _, 
            type table [Material=nullable text, Date=nullable date, Stock movement qty=nullable number]
        }}
    ),
    MergeGroups = Table.NestedJoin( 
        Source, 
        "Material", 
        MaterialGroups, 
        "Material", 
        "Groups", 
        JoinKind.Inner 
    ),
    ExpandGroups = Table.ExpandTableColumn(MergeGroups, "Groups", {"Current qty"}, {"Current qty"}),
    GetCurQtyRows = 
    Table.TransformRows(  
        ExpandGroups, 
        (row)=> Record.TransformFields( 
            row, 
            {
                "Current qty", 
                each let 
                    _t = Table.SelectRows( row[Current qty], each [Date] <= row[Date] ) 
                in 
                    List.Sum( Table.Column(_t, "Stock movement qty") ) 
            } 
        ) 
    ),
    GetCurQty = Table.FromRecords( 
        GetCurQtyRows, 
        type table [Material=text, Date=date, Stock movement qty=number, Current qty=number] 
    ),
    GetMinQty = Table.Group(
        GetCurQty, 
        {"Material"}, 
        {
            { "Min qty", each List.Min([Current qty]), type number }
        }
    )
in
    GetMinQty

 

Output:

 

The above doesn't work so great when you up the rows to 100k, though. For that I think you have to turn to DAX. This takes about 2 sec to work over 100k rows (probably there are ways to improve performance further on this). Note that [Running Total] and [Min Running Total] are measures:

 

Running Total = 
VAR _thisDt = MAX( PerfTest_100k[Date] )
VAR _matGroup = CALCULATETABLE( PerfTest_100k, REMOVEFILTERS( PerfTest_100k ), VALUES( PerfTest_100k[Material] ) )
VAR _curPrevRows = FILTER( _matGroup, PerfTest_100k[Date] <= _thisDt )
RETURN
CALCULATE( SUM( PerfTest_100k[Stock movement qty] ), _curPrevRows )


Min Running Total = 
MINX( SUMMARIZE( PerfTest_100k, PerfTest_100k[Material], PerfTest_100k[Date] ), [Running Total] )

 

Output (note it's all randomly generated which is why these numbers don't match output above):

In case interested and to show my work, here is the M for the test data. Below generates 10k rows for

PerfTest_10k. It's same code, but 10000 replaced with 100000 in line 4, for PerfTest_100k:

 

let
    Source = List.Generate( 
        ()=>0,
        each _ < 10000, 
        each _ + 1, 
        each [
            Material = Character.FromNumber( 
                List.Min( { 
                    Int32.From( Number.RandomBetween(65, 91) ), // A-Z
                    90 
                } ) 
            ), 
            Date = Date.AddDays( 
                #date(2022,1,1), 
                List.Min({ 
                    Int32.From( Number.RandomBetween( 0, 365 ) ), // 1/1/2022-12/31/2022
                    364 } ) 
                ),
            Stock movement qty = Int64.From( Number.RandomBetween( -100, 100 ) ) // -100 - +100
        ]
    ),
    Ouput = Table.FromRecords( Source, type table [Material=text,Date=date,Stock movement qty=number] )
in
    Ouput