ParallelEnumerable.ToDictionary does not parallelize

[madelson]

Description

I would expect the following to be a valid pattern, but it does not lead to parallel execution of ComputeExpensiveThing():

var resultsByItem = items.AsParallel().ToDictionary(i => i, i => ComputeExpensiveThing());

Reproduction Steps

    var items = Enumerable.Range(0, 100).AsParallel();
    var stopwatch = Stopwatch.StartNew();
    
    // completes in ~10s because there is no parallelism
    var dictionary = items.ToDictionary(i => i, i => { Thread.Sleep(100); return i; });
    
    // workaround completes in ~1s
    /*var dictionary = items.Select(i =>
        {
            Thread.Sleep(100);
            return new KeyValuePair<int, int>(i, i);
        })
        .ToDictionary(kvp => kvp.Key, kvp => kvp.Value);*/

    Console.WriteLine(stopwatch.Elapsed);

Expected behavior

From a parallelism perspective, I would expect

items.AsParallel().ToDictionary(i => ..., i => ...)

to be equivalent to:

items.AsParallel().Select(i => KeyValuePair.Create(..., ...)).ToDictionary(kvp => kvp.Key, kvp => kvp.Value);

For example, ParallelEnumerable.Min() parallelizes its selector:

Enumerable.Range(1, 10).AsParallel().Min(i => { Thread.Sleep(1000); return i; }); // completes in ~1s

Actual behavior

keySelector and elementSelector execute serially

Regression?

No response

Known Workarounds

As shown above, pushing any non-trivial selectors up one layer into a Select call gets around the issue.

Configuration

.NET 8 Windows 11 x64

Other information

ParallelEnumerable.ToDictionary has a serial loop in which the selectors are executed.

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Issue Details

---

Description

I would expect the following to be a valid pattern, but it does not lead to parallel execution of ComputeExpensiveThing():

var resultsByItem = items.AsParallel().ToDictionary(i=> i, i => ComputeExpensiveThing());

Reproduction Steps

    var items = Enumerable.Range(0, 100).AsParallel();
    var stopwatch = Stopwatch.StartNew();
    
    // completes in ~10s because there is no parallelism
    var dictionary = items.ToDictionary(i => i, i => { Thread.Sleep(100); return i; });
    
    // workaround completes in ~1s
    /*var dictionary = items.Select(i =>
        {
            Thread.Sleep(100);
            return new KeyValuePair<int, int>(i, i);
        })
        .ToDictionary(kvp => kvp.Key, kvp => kvp.Value);*/

    Console.WriteLine(stopwatch.Elapsed);

Expected behavior

From a parallelism perspective, I would expect

items.AsParallel().ToDictionary(i => ..., i => ...)

to be equivalent to:

items.AsParallel().Select(i => KeyValuePair.Create(..., ...)).ToDictionary(kvp => kvp.Key, kvp => kvp.Value);

For example, ParallelEnumerable.Min() parallelizes it's selector:

Enumerable.Range(1, 10).AsParallel().Min(i => { Thread.Sleep(1000); return i; }); // completes in ~1s

Actual behavior

keySelector and elementSelector execute serially

Regression?

No response

Known Workarounds

As shown above, pushing any non-trivial selectors up one layer into a Select call gets around the issue.

Configuration

.NET 8 Windows 11 x64

Other information

ParallelEnumerable.ToDictionary has a serial loop in which the selectors are executed.

Author:	madelson
Assignees:	-
Labels:	area-System.Collections, untriaged
Milestone:	-

Tagging subscribers to this area: @dotnet/area-system-linq-parallel
See info in area-owners.md if you want to be subscribed.

Issue Details

---

Description

I would expect the following to be a valid pattern, but it does not lead to parallel execution of ComputeExpensiveThing():

var resultsByItem = items.AsParallel().ToDictionary(i=> i, i => ComputeExpensiveThing());

Reproduction Steps

    var items = Enumerable.Range(0, 100).AsParallel();
    var stopwatch = Stopwatch.StartNew();
    
    // completes in ~10s because there is no parallelism
    var dictionary = items.ToDictionary(i => i, i => { Thread.Sleep(100); return i; });
    
    // workaround completes in ~1s
    /*var dictionary = items.Select(i =>
        {
            Thread.Sleep(100);
            return new KeyValuePair<int, int>(i, i);
        })
        .ToDictionary(kvp => kvp.Key, kvp => kvp.Value);*/

    Console.WriteLine(stopwatch.Elapsed);

Expected behavior

From a parallelism perspective, I would expect

items.AsParallel().ToDictionary(i => ..., i => ...)

to be equivalent to:

items.AsParallel().Select(i => KeyValuePair.Create(..., ...)).ToDictionary(kvp => kvp.Key, kvp => kvp.Value);

For example, ParallelEnumerable.Min() parallelizes it's selector:

Enumerable.Range(1, 10).AsParallel().Min(i => { Thread.Sleep(1000); return i; }); // completes in ~1s

Actual behavior

keySelector and elementSelector execute serially

Regression?

No response

Known Workarounds

As shown above, pushing any non-trivial selectors up one layer into a Select call gets around the issue.

Configuration

.NET 8 Windows 11 x64

Other information

ParallelEnumerable.ToDictionary has a serial loop in which the selectors are executed.

Author:	madelson
Assignees:	-
Labels:	area-System.Linq.Parallel, untriaged
Milestone:	-

[stephentoub]

If memory serves, the decision not to parallelize the selectors is because they're almost always trivial, e.g. just accessing a property from the element to select, and parallelizing it would make the 99% case slower.

What led to this issue? I realize it's of course possible for a non-trivial selector to be used, but could you highlight real-world examples of this being done with PLINQ? As is highlighted, a developer can always choose to use a Select instead if they in fact have an expensive selector.

[madelson]

@stephentoub A pattern I’ve found myself wanting to use fairly often with PLINQ is that I have N items and for each item I want to compute some expensive value. It’s convenient to build a dictionary that maps from the items to the values for use in downstream computations.

It’s been a while but I believe the case that prompted me to post this was related to processing large vectors. I had a set of N long vectors (represented as float arrays) and for each one I needed to compute a statistic that required comparing that vector to M other vectors in a larger set. It felt natural to PLINQ a dictionary mapping the vectors to the statistics and then process from there.

Since then, another case I ran into was implementing a generic algorithm style optimization where I had N chromosomes and for each one I needed to run a fitness function. And associate that result wi the the chromosome.

For both of these, there are obviously other ways to do it but I believe it would be preferable from a “least surprise” perspective if the various PLINQ operators were consistent in terms of parallelism.

How much overhead would consistent parallelism really add in this case (especially given that we can assume the query involves parallelism already)? I would assume that this would just tack one more selector to the chain of operations that is already running in parallel.

[stephentoub]

How much overhead would consistent parallelism really add in this case (especially given that we can assume the query involves parallelism already)? I would assume that this would just tack one more selector to the chain of operations that is already running in parallel.

We could test the overhead when there's already parallel work happening and this would indeed be appending an additional item. Possibly we could allow the ToDictionary selector to be parallel in that case but not in the case where there's not already parallel work happening. But it would really come down to measurements... there's going to be significant overhead just in the AsParallel().ToDictionary(...) case if the selectors are trivial, as is often the case (I understand it's not true in your cited case).

[madelson]

there's going to be significant overhead just in the AsParallel().ToDictionary(...) case if the selectors are trivial, as is often the case (I understand it's not true in your cited case).

If a user calls .AsParallel().ToDictionary(...) can't we assume that their intent is parallelism? If someone does someCollection.AsParallel().ToDictionary(trivial, trivial) and that adds overhead over not having AsParallel, would that really be so bad?