Tell ParallelMap[] to use just specific kernels

Question

Is there a way to tell ParallelMap to use just some specific kernels (as is possible for ParallelEvaluate)?

Background: I have 6 local and 16 remote kernels. During some analysis I call ParalleMap twice. One ParallelMap is quicker using all the kernels, but one is actually slower (probably bandwidth limited). This is why I would like to run one ParallelMap on all the kernels and the other only on the local kernels. I don't want to close the kernels in between if possible. I also tried setting the parallelization method to "CoarsestGrained" or "FinestGrained", but that didn't help much.

Edit:

Could somebody shed light on why the following does not work?

num = 10;
list = Range[num];
function = Labeled[Framed[#], $KernelID] &;
ParallelMap[function, list]
aKer = Kernels[];
Block[{Parallel`Protected`$kernels = aKer[[{2, 3}]]},
   ParallelMap[function, list]
]

The second map is still run on 4 of the Kernels instead of just two. I traced ParallelMap to

Parallel`Developer`ParallelDispatch[Parallel`Combine`Private`cmdl]

and

Parallel`Developer`ParallelDispatch[Parallel`Parallel`Private`cmds_] :=
    Parallel`Developer`ParallelDispatch[Parallel`Parallel`Private`cmds,Kernels[]]
Kernels[]:=Parallel`Protected`$kernels

so overriding Parallel`Protected`$kernels should in theory work.

Answer 1

Let me give a different approach. One downside of WalkingRandomly's approach is, that he distributes the whole list over all subkernels. The he uses Part in each subcall to select the data to use. I will make this differently:

• I divide the data into chunks and define each chunk as subdata on every subkernel you want to use in the current call
• then I can simply call Map[f,subdata] on each wanted subkernel with ParallelEvaluate

The chunkenize is only to show it here. For real work it has to be adapted to situations where Length[data] is not divisible by the number of used kernels

chunkenize[data_, nkernels_] := 
 Partition[data, Quotient[Length[data], nkernels]]

MyParallelMap[f_, data_, kernels_] := 
 Module[{chunks = chunkenize[data, Length[kernels]]},
  Block[{subdata},
   MapIndexed[
    ParallelEvaluate[subdata = #1, kernels[[First[#2]]]] &, chunks];
   DistributeDefinitions[f];
   ParallelEvaluate[Map[f, subdata], kernels]
   ]
  ]

Trying it gives

data = Range[20];
f[x_] := {$KernelID, x^2}
kernels = LaunchKernels[];

MyParallelMap[f, data, kernels]
(*
{{{1,1},{1,4},{1,9},{1,16},{1,25}},{{2,36},{2,49},{2,64},{2,81},{2,100}},
{{3,121},{3,144},{3,169},{3,196},{3,225}},{{4,256},{4,289},{4,324},{4,361},{4,400}}}
*)

Or if you like

MyParallelMap[f,data,kernels[[{2,3}]]]
(*
{{{2,1},{2,4},{2,9},{2,16},{2,25},{2,36},{2,49},{2,64},{2,81},{2,100}},
{{3,121},{3,144},{3,169},{3,196},{3,225},{3,256},{3,289},{3,324},{3,361},{3,400}}}
*)

Update

Also I would really like to know why overriding Parallel`Protected`$kernels does not work.

When you trace the output of a simple ParallelMap call, you can investigate what happens. What I did is, I created a full trace output and checked then, on what positions the subkernels like KernelObject[1, "local"] appear.

In detail this meant to check the FullForm of a subkernel because then you see that it has the form

Parallel`Kernels`kernel[....]

then I launched some kernels and trace the output. Using Position you can find all positions which match a sub-kernel

kernels = LaunchKernels[];
trace = Trace[ParallelMap[$KernelID &, Range[100]]];
pos = Position[trace, Parallel`Kernels`kernel, Infinity];

If you now inspect a bit the positions where the sub-kernels arise, you first find what you found: Parallel`Protected`$kernels. But soon you see

Part[trace,Sequence@@Drop[pos[[10]], -4]]
(*
{Parallel`Protected`$sortedkernels,
 {KernelObject[1,local],KernelObject[2,local],
  KernelObject[3,local],KernelObject[4,local]}}
*)

This brings us to the following solution:

Block[{
  $KernelCount = 2,
  Parallel`Protected`$sortedkernels = Take[kernels, 2]
  },
 ParallelMap[$KernelID &, Range[100]]
]
(*
{1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2,2,2,2,1,1,1,1,1,1,1,1,1,1,
1,1,1,2,2,2,2,2,2,2,2,2,2,2,2,2,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,
2,2,2,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2,2,2}
*)

Since I didn't find this that quick, I had time to do some more spelunking. You may have noted, that I set $KernelCount in the Block. This is, because the value of it is used for the partitionizer.

Answer 2

I don't think that you can do this with ParallelMap directly (well, I can't find anything in the documentation at least) but you could emulate such behaviour using ParallelEvaluate. Consider the following example and its output on my quad core laptop

num=10;
list = Range[num];
function = Labeled[Framed[#], $KernelID] &;
ParallelMap[function, list]

The goal of this function is just to frame the input with the $KernelID of the kernel that did the framing printed underneath. Imagine that kernels {3,4} were broken and so I only wanted to work with kernels {1,2}

num = 10;
list = Range[num];
function = Labeled[Framed[#], $KernelID] &;
wantedkernels = {1, 2};
chunk = num/Length[wantedkernels];
DistributeDefinitions[chunk, list, function];

ParallelEvaluate[
  Map[function, 
   list[[1 + ($KernelID - 1)*chunk ;; ($KernelID*chunk)]]],
  wantedkernels] // Flatten 

Performance might suffer from the fact that each kernel gets a fixed amount of work (so no load balancing etc) but perhaps this is enough for your needs?