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I have an Azure function triggered via service bus which basically just does a long await (1 - 4.5 minutes (managed with cancellation token to prevent function timeout and host restarting)).
I want to process as many of these long await messages as I can. (Ideally about 1200 at the same time..)
First I ran my function on an App Service Plan (with Concurrent Calls = 1200), but I think each trigger creates a thread, and 1200 threads causes some issues.
So I decided to run it on Consumption, with Batch Size 32, with the idea what I can avoid creating tons of threads and scale out the consumption function instead when it sees the queue build up.
Unfortunately exactly the opposite happens, the Consumption function will process 32 messages, but never scales out even though the queue has 1000+ items in it. Even worse, some times the function just goes to sleep although there are still many items in the queue.
I feel my best option would be to group work in a message, so instead of 1 message = 1 long await, 1 message could be 100 awaits for example, but my architecture doesn't really allow to me group messages easily (because if some tasks fail but some succeed this is easily managed with dead letters, but with grouping I need to maintain state to track this). Is there an existing way to efficiently have many (independent) long running awaits on an azure function, either consumption or service plan.
GPU is really fast when it comes to paralleled computation and out performs CPU with being 15-30 ( some have reported even 50 ) times faster however,
GPU memory is very limited compared to CPU memory and communication between GPU memory and CPU is not as fast.
Lets say we have some data what won't fit into GPU ram but we still want to use
it's wonders to compute. What we can do is split that data into pieces and feed it into GPU one by one.
Sending large data to GPU can take time and one might think, what if we would split a data piece into two and feed the first half, run the kernel and then feed the other half while kernel is running.
By that logic we should save some time because data transfer should be going on while computation is, hopefully not interrupting it's job and when finished, it can just, well, continue it's job without needs for waiting a new data path.
I must say that I'm new to gpgpu, new to cuda but I have been experimenting around with simple cuda codes and have noticed that the function cudaMemcpy used to transfer data between CPU and GPU will block if kerner is running. It will wait until kernel is finished and then will do its job.
My question, is it possible to accomplish something like that described above and if so, could one show an example or provide some information source of how it could be done?
Thank you!
is it possible to accomplish something like that described above
Yes, it's possible. What you're describing is a pipelined algorithm, and CUDA has various asynchronous capabilities to enable it.
The asynchronous concurrent execution section of the programming guide covers the necessary elements in CUDA to make it work. To use your example, there exists a non-blocking version of cudaMemcpy, called cudaMemcpyAsync. You'll need to understand CUDA streams and how to use them.
I would also suggest this presentation which covers most of what is needed.
Finally, here is a worked example. That particular example happens to use CUDA stream callbacks, but those are not necessary for basic pipelining. They enable additional host-oriented processing to be asynchronously triggered at various points in the pipeline, but the basic chunking of data, and delivery of data while processing is occurring does not depend on stream callbacks. Note also the linked CUDA sample codes in that answer, which may be useful for study/learning.
I'm developing a web app that needs to handle bursts of very high loads,
once per minute I get a burst of requests in very few seconds (~1M-3M/sec) and then for the rest of the minute I get nothing,
What's my best strategy to handle as many req /sec as possible at each front server, just sending a reply and storing the request in memory somehow to be processed in the background by the DB writer worker later ?
The aim is to do as less as possible during the burst, and write the requests to the DB ASAP after the burst.
Edit : the order of transactions in not important,
we can lose some transactions but 99% need to be recorded
latency of getting all requests to the DB can be a few seconds after then last request has been received. Lets say not more than 15 seconds
This question is kind of vague. But I'll take a stab at it.
1) You need limits. A simple implementation will open millions of connections to the DB, which will obviously perform badly. At the very least, each connection eats MB of RAM on the DB. Even with connection pooling, each 'thread' could take a lot of RAM to record it's (incoming) state.
If your app server had a limited number of processing threads, you can use HAProxy to "pick up the phone" and buffer the request in a queue for a few seconds until there is a free thread on your app server to handle the request.
In fact, you could just use a web server like nginx to take the request and say "200 OK". Then later, a simple app reads the web log and inserts into DB. This will scale pretty well, although you probably want one thread reading the log and several threads inserting.
2) If your language has coroutines, it may be better to handle the buffering yourself. You should measure the overhead of relying on our language runtime for scheduling.
For example, if each HTTP request is 1K of headers + data, want to parse it and throw away everything but the one or two pieces of data that you actually need (i.e. the DB ID). If you rely on your language coroutines as an 'implicit' queue, it will have 1K buffers for each coroutine while they are being parsed. In some cases, it's more efficient/faster to have a finite number of workers, and manage the queue explicitly. When you have a million things to do, small overheads add up quickly, and the language runtime won't always be optimized for your app.
Also, Go will give you far better control over your memory than Node.js. (Structs are much smaller than objects. The 'overhead' for the Keys to your struct is a compile-time thing for Go, but a run-time thing for Node.js)
3) How do you know it's working? You want to be able to know exactly how you are doing. When you rely on the language co-routines, it's not easy to ask "how many threads of execution do I have and what's the oldest one?" If you make an explicit queue, those questions are much easier to ask. (Imagine a handful of workers putting stuff in the queue, and a handful of workers pulling stuff out. There is a little uncertainty around the edges, but the queue in the middle very explicitly captures your backlog. You can easily calculate things like "drain rate" and "max memory usage" which are very important to knowing how overloaded you are.)
My advice: Go with Go. Long term, Go will be a much better choice. The Go runtime is a bit immature right now, but every release is getting better. Node.js is probably slightly ahead in a few areas (maturity, size of community, libraries, etc.)
How about a channel with a buffer size equal to what the DB writer can handle in 15 seconds? When the request comes in, it is sent on the channel. If the channel is full, give some sort of "System Overloaded" error response.
Then the DB writer reads from the channel and writes to the database.
I have a program that does a limited form of multithreading. It is written in Delphi, and uses libmysql.dll (the C API) to access a MySQL server. The program must process a long list of records, taking ~0.1s per record. Think of it as one big loop. All database access is done by worker threads which either prefetch the next records or write results, so the main thread doesn't have to wait.
At the top of this loop, we first wait for the prefetch thread, get the results, then have the prefetch thread execute the query for the next record. The idea being that the prefetch thread will send the query immediately, and wait for results while the main thread completes the loop.
It often does work that way. But note there's nothing to ensure that the prefetch thread runs right away. I found that often the query was not sent until the main thread looped around and started waiting for the prefetch.
I sort-of fixed that by calling sleep(0) right after launching the prefetch thread. This way the main thread surrenders the remainder of it's time slice, hoping that the prefetch thread will now run, sending the query. Then that thread will sleep while waiting, which allows the main thread to run again.
Of course, there's plenty more threads running in the OS, but this did actually work to some extent.
What I really want to happen is for the main thread to send the query, and then have the worker thread wait for the results. Using libmysql.dll I call
result := mysql_query(p.SqlCon,pChar(p.query));
in the worker thread. Instead, I'd like to have the main thread call something like
mysql_threadedquery(p.SqlCon,pChar(p.query),thread);
which would hand off the task as soon as the data went out.
Anybody know of anything like that?
This is really a scheduling problem, so I could try is lauching the prefetch thread at a higher priority, then have it reduce its priority after the query is sent. But again, I don't have any mysql call that separates sending the query from receiving the results.
Maybe it's in there and I just don't know about it. Enlighten me, please.
Added Question:
Does anyone think this problem would be solved by running the prefetch thread at a higher priority than the main thread? The idea is that the prefetch would immediately preempt the main thread and send the query. Then it would sleep waiting for the server reply. Meanwhile the main thread would run.
Added: Details of current implementation
This program performs calculations on data contained in a MySQL DB. There are 33M items with more added every second. The program runs continuously, processing new items, and sometimes re-analyzing old items. It gets a list of items to analyze from a table, so at the beginning of a pass (current item) it knows the next item ID it will need.
As each item is independent, this is a perfect target for multiprocessing. The easiest way to do this is to run multiple instances of the program on multiple machines. The program is highly optimized via profiling, rewrites, and algorithm redesign. Still, a single instance utilizes 100% of a CPU core when not data-starved. I run 4-8 copies on two quad-core workstations. But at this rate they must spend time waiting on the MySQL server. (Optimization of the Server/DB schema is another topic.)
I implemented multi-threading in the process solely to avoid blocking on the SQL calls. That's why I called this "limited multi-threading". A worker thread has one task: send a command and wait for results. (OK, two tasks.)
It turns out there are 6 blocking tasks associated with 6 tables. Two of these read data and the other 4 write results. These are similar enough to be defined by a common Task structure. A pointer to this Task is passed to a threadpool manager which assigns a thread to do the work. The main thread can check the task status through the Task structure.
This makes the main thread code very simple. When it needs to perform Task1, it waits for Task1 to be not busy, puts the SQL command in Task1 and hands it off. When Task1 is no longer busy, it contains the results (if any).
The 4 tasks that write results are trivial. The main thread has a Task write records while it goes on to the next item. When done with that item it makes sure the previous write finished before starting another.
The 2 reading threads are less trivial. Nothing would be gained by passing the read to a thread and then waiting for the results. Instead, these tasks prefetch data for the next item. So the main thread, coming to this blocking tasks, checks if the prefetch is done; Waits if necessary for the prefetch to finish, then takes the data from the Task. Finally, it reissues the Task with the NEXT Item ID.
The idea is for the prefetch task to immediately issue the query and wait for the MySQL server. Then the main thread can process the current Item and by the time it starts on the next Item the data it needs is in the prefetch Task.
So the threading, a thread pool, the synchronization, data structures, etc. are all done. And that all works. What I'm left with is a Scheduling Problem.
The Scheduling Problem is this: All the speed gain is in processing the current Item while the server is fetching the next Item. We issue the prefetch task before processing the current item, but how do we guarantee that it starts? The OS scheduler does not know that it's important for the prefetch task to issue the query right away, and then it will do nothing but wait.
The OS scheduler is trying to be "fair" and allow each task to run for an assigned time slice. My worst case is this: The main thread receives its slice and issues a prefetch, then finishes the current item and must wait for the next item. Waiting releases the rest of its time slice, so the scheduler starts the prefetch thread, which issues the query and then waits. Now both threads are waiting. When the server signals the query is done the prefetch thread restarts, and requests the Results (dataset) then sleeps. When the server provides the results the prefetch thread awakes, marks the Task Done and terminates. Finally, the main thread restarts and takes the data from the finished Task.
To avoid this worst-case scheduling I need some way to ensure that the prefetch query is issued before the main thread goes on with the current item. So far I've thought of three ways to do that:
Right after issuing the prefetch task, the main thread calls Sleep(0). This should relinquish the rest of its time slice. I then hope that the scheduler runs the prefetch thread, which will issue the query and then wait. Then the scheduler should restart the main thread (I hope.) As bad as it sounds, this actually works better than nothing.
I could possibly issue the prefetch thread at a higher priority than the main thread. That should cause the scheduler to run it right away, even if it must preempt the main thread. It may also have undesirable effects. It seems unnatural for a background worker thread to get a higher priority.
I could possibly issue the query asynchronously. That is, separate sending the query from receiving the results. That way I could have the main thread send the prefetch using mysql_send_query (non blocking) and go on with the current item. Then when it needed the next item it would call mysql_read_query, which would block until the data is available.
Note that solution 3 does not even use a worker thread. This looks like the best answer, but requires a rewrite of some low-level code. I'm currently looking for examples of such asynchronous client-server access.
I'd also like any experienced opinions on these approaches. Have I missed anything, or am I doing anything wrong? Please note that this is all working code. I'm not asking how to do it, but how to do it better/faster.
Still, a single instance utilizes 100% of a CPU core when not data-starved. I run 4-8 copies on two quad-core workstations.
I have a conceptual problem here. In your situation I would either create a multi-process solution, with each process doing everything in its single thread, or I would create a multi-threaded solution that is limited to a single instance on any particular machine. Once you decide to work with multiple threads and accept the added complexity and probability of hard-to-fix bugs, then you should make maximum use of them. Using a single process with multiple threads allows you to employ varying numbers of threads for reading from and writing to the database and to process your data. The number of threads may even change during the runtime of your program, and the ratio of database and processing threads may too. This kind of dynamic partitioning of the work is only possible if you can control all threads from a single point in the program, which isn't possible with multiple processes.
I implemented multi-threading in the process solely to avoid blocking on the SQL calls.
With multiple processes there wouldn't be a real need to do so. If your processes are I/O-bound some of the time they don't consume CPU resources, so you probably simply need to run more of them than your machine has cores. But then you have the problem to know how many processes to spawn, and that may again change over time if the machine does other work too. A threaded solution in a single process can be made adaptable to a changing environment in a relatively simple way.
So the threading, a thread pool, the synchronization, data structures, etc. are all done. And that all works. What I'm left with is a Scheduling Problem.
Which you should leave to the OS. Simply have a single process with the necessary pooled threads. Something like the following:
A number of threads reads records from the database and adds them to a producer-consumer queue with an upper bound, which is somewhere between N and 2*N where N is the number of processor cores in the system. These threads will block on the full queue, and they can have increased priority, so that they will be scheduled to run as soon as the queue has more room and they become unblocked. Since they will be blocked on I/O most of the time their higher priority shouldn't be a problem.
I don't know what that number of threads is, you would need to measure.
A number of processing threads, probably one per processor core in the system. They will take work items from the queue mentioned in the previous point, on block on that queue if it's empty. Processed work items should go to another queue.
A number of threads that take processed work items from the second queue and write data back to the database. There should probably an upper bound for the second queue as well, to make it so that a failure to write processed data back to the database will not cause processed data to pile up and fill all your process memory space.
The number of threads needs to be determined, but all scheduling will be performed by the OS scheduler. The key is to have enough threads to utilise all CPU cores, and the necessary number of auxiliary threads to keep them busy and deal with their outputs. If these threads come from pools you are free to adjust their numbers at runtime too.
The Omni Thread Library has a solution for tasks, task pools, producer consumer queues and everything else you would need to implement this. Otherwise you can write your own queues using mutexes.
The Scheduling Problem is this: All the speed gain is in processing the current Item while the server is fetching the next Item. We issue the prefetch task before processing the current item, but how do we guarantee that it starts?
By giving it a higher priority.
The OS scheduler does not know that it's important for the prefetch task to issue the query right away
It will know if the thread has a higher priority.
The OS scheduler is trying to be "fair" and allow each task to run for an assigned time slice.
Only for threads of the same priority. No lower priority thread will get any slice of CPU while a higher priority thread in the same process is runnable.
[Edit: That's not completely true, more information at the end. However, it is close enough to the truth to ensure that your higher priority network threads send and receive data as soon as possible.]
Right after issuing the prefetch task, the main thread calls Sleep(0).
Calling Sleep() is a bad way to force threads to execute in a certain order. Set the thread priority according to the priority of the work they perform, and use OS primitives to block higher priority threads if they should not run.
I could possibly issue the prefetch thread at a higher priority than the main thread. That should cause the scheduler to run it right away, even if it must preempt the main thread. It may also have undesirable effects. It seems unnatural for a background worker thread to get a higher priority.
There is nothing unnatural about this. It is the intended way to use threads. You only must make sure that higher priority threads block sooner or later, and any thread that goes to the OS for I/O (file or network) does block. In the scheme I sketched above the high priority threads will also block on the queues.
I could possibly issue the query asynchronously.
I wouldn't go there. This technique may be necessary when you write a server for many simultaneous connections and a thread per connection is prohibitively expensive, but otherwise blocking network access in a threaded solution should work fine.
Edit:
Thanks to Jeroen Pluimers for the poke to look closer into this. As the information in the links he gave in his comment shows my statement
No lower priority thread will get any slice of CPU while a higher priority thread in the same process is runnable.
is not true. Lower priority threads that haven't been running for a long time get a random priority boost and will indeed sooner or later get a share of CPU, even though higher priority threads are runnable. For more information about this see in particular "Priority Inversion and Windows NT Scheduler".
To test this out I created a simple demo with Delphi:
type
TForm1 = class(TForm)
Label1: TLabel;
Label2: TLabel;
Label3: TLabel;
Label4: TLabel;
Label5: TLabel;
Label6: TLabel;
Timer1: TTimer;
procedure FormCreate(Sender: TObject);
procedure FormDestroy(Sender: TObject);
procedure Timer1Timer(Sender: TObject);
private
fLoopCounters: array[0..5] of LongWord;
fThreads: array[0..5] of TThread;
end;
var
Form1: TForm1;
implementation
{$R *.DFM}
// TTestThread
type
TTestThread = class(TThread)
private
fLoopCounterPtr: PLongWord;
protected
procedure Execute; override;
public
constructor Create(ALowerPriority: boolean; ALoopCounterPtr: PLongWord);
end;
constructor TTestThread.Create(ALowerPriority: boolean;
ALoopCounterPtr: PLongWord);
begin
inherited Create(True);
if ALowerPriority then
Priority := tpLower;
fLoopCounterPtr := ALoopCounterPtr;
Resume;
end;
procedure TTestThread.Execute;
begin
while not Terminated do
InterlockedIncrement(PInteger(fLoopCounterPtr)^);
end;
// TForm1
procedure TForm1.FormCreate(Sender: TObject);
var
i: integer;
begin
for i := Low(fThreads) to High(fThreads) do
// fThreads[i] := TTestThread.Create(True, #fLoopCounters[i]);
fThreads[i] := TTestThread.Create(i >= 4, #fLoopCounters[i]);
end;
procedure TForm1.FormDestroy(Sender: TObject);
var
i: integer;
begin
for i := Low(fThreads) to High(fThreads) do begin
if fThreads[i] <> nil then
fThreads[i].Terminate;
end;
for i := Low(fThreads) to High(fThreads) do
fThreads[i].Free;
end;
procedure TForm1.Timer1Timer(Sender: TObject);
begin
Label1.Caption := IntToStr(fLoopCounters[0]);
Label2.Caption := IntToStr(fLoopCounters[1]);
Label3.Caption := IntToStr(fLoopCounters[2]);
Label4.Caption := IntToStr(fLoopCounters[3]);
Label5.Caption := IntToStr(fLoopCounters[4]);
Label6.Caption := IntToStr(fLoopCounters[5]);
end;
This creates 6 threads (on my 4 core machine), either all with lower priority, or 4 with normal and 2 with lower priority. In the first case all 6 threads run, but with wildly different shares of CPU time:
In the second case 4 threads run with roughly equal share of CPU time, but the other two threads get a little share of the CPU as well:
But the share of CPU time is very very small, way below a percent of what the other threads receive.
And to get back to your question: A program using multiple threads with custom priority, coupled via producer-consumer queues, should be a viable solution. In the normal case the database threads will block most of the time, either on the network operations or on the queues. And the Windows scheduler will make sure that even a lower priority thread will not completely starve to death.
I don't know any database access layer that permits this.
The reason is that each thread has its own "thread local storage" (The threadvar keyword in Delphi, other languages have equivalents, it is used in a lot of frameworks).
When you start things on one thread, and continue it on another, then you get these local storages mixed up causing all sorts of havoc.
The best you can do is this:
pass the query and parameters to the thread that will handle this (use the standard Delphi thread synchronization mechanisms for this)
have the actual query thread perform the query
return the results to the main thread (use the standard Delphi thread synchronization mechanisms for this)
The answers to this question explains thread synchronization in more detail.
Edit: (on presumed slowness of starting something in an other thread)
"Right away" is a relative term: it depends in how you do your thread synchronization and can be very very fast (i.e. less than a millisecond).
Creating a new thread might take some time.
The solution is to have a threadpool of worker threads that is big enough to service a reasonable amount of requests in an efficient manner.
That way, if the system is not yet too busy, you will have a worker thread ready to start servicing your request almost immediately.
I have done this (even cross process) in a big audio application that required low latency response, and it works like a charm.
The audio server process runs at high priority waiting for requests. When it is idle, it doesn't consume CPU, but when it receives a request it responds really fast.
The answers to this question on changes with big improvements and this question on cross thread communication provide some interesting tips on how to get this asynchronous behaviour working.
Look for the words AsyncCalls, OmniThread and thread.
--jeroen
I'm putting in a second answer, for your second part of the question: your Scheduling Problem
This makes it easier to distinguish both answers.
First of all, you should read Consequences of the scheduling algorithm: Sleeping doesn't always help which is part of Raymond Chen's blog "The Old New Thing".
Sleeping versus polling is also good reading.
Basically all these make good reading.
If I understand your Scheduling Problem correctly, you have 3 kinds of threads:
Main Thread: makes sure the Fetch Threads always have work to do
Fetch Threads: (database bound) fetch data for the Processing Threads
Processing Threads: (CPU bound) process fetched data
The only way to keep 3 running is to have 2 fetch as much data as they can.
The only way to keep 2 fetching, is to have 1 provide them enough entries to fetch.
You can use queues to communicate data between 1 and 2 and between 2 and 3.
Your problem now is two-fold:
finding the balance between the number of threads in category 2 and 3
making sure that 2 always have work to do
I think you have solved the former.
The latter comes down to making sure the queue between 1 and 2 is never empty.
A few tricks:
You can use Sleep(1) (see the blog article) as a simple way to "force" 2 to run
Never let the treads exit their execute: creating and destroying threads is expensive
choose your synchronization objects (often called IPC objects) carefully (Kudzu has a nice article on them)
--jeroen
You just have to use the standard Thread synchronization mechanism of the Delphi threading.
Check your IDE help for TEvent class and its associated methods.
Why is it that I can find lots of information on "work stealing" and nothing on "work shrugging" as a dynamic load-balancing strategy?
By "work-shrugging" I mean pushing surplus work away from busy processors onto less loaded neighbours, rather than have idle processors pulling work from busy neighbours ("work-stealing").
I think the general scalability should be the same for both strategies. However I believe that it is much more efficient, in terms of latency & power consumption, to wake an idle processor when there is definitely work for it to do, rather than having all idle processors periodically polling all neighbours for possible work.
Anyway a quick google didn't show up anything under the heading of "Work Shrugging" or similar so any pointers to prior-art and the jargon for this strategy would be welcome.
Clarification
I actually envisage the work submitting processor (which may or may not be the target processor) being responsible for looking around the immediate locality of the preferred target processor (based on data/code locality) to decide if a near neighbour should be given the new work instead because they don't have as much work to do.
I dont think the decision logic would require much more than an atomic read of the immediate (typically 2 to 4) neighbours' estimated q length here. I do not think this is any more coupling than implied by the thieves polling & stealing from their neighbours. (I am assuming "lock-free, wait-free" queues in both strategies).
Resolution
It seems that what I meant (but only partially described!) as "Work Shrugging" strategy is in the domain of "normal" upfront scheduling strategies that happen to be smart about processor, cache & memory loyality, and scaleable.
I find plenty of references searching on these terms and several of them look pretty solid. I will post a reference when I identify one that best matches (or demolishes!) the logic I had in mind with my definition of "Work Shrugging".
Load balancing is not free; it has a cost of a context switch (to the kernel), finding the idle processors, and choosing work to reassign. Especially in a machine where tasks switch all the time, dozens of times per second, this cost adds up.
So what's the difference? Work-shrugging means you further burden over-provisioned resources (busy processors) with the overhead of load-balancing. Why interrupt a busy processor with administrivia when there's a processor next door with nothing to do? Work stealing, on the other hand, lets the idle processors run the load balancer while busy processors get on with their work. Work-stealing saves time.
Example
Consider: Processor A has two tasks assigned to it. They take time a1 and a2, respectively. Processor B, nearby (the distance of a cache bounce, perhaps), is idle. The processors are identical in all respects. We assume the code for each task and the kernel is in the i-cache of both processors (no added page fault on load balancing).
A context switch of any kind (including load-balancing) takes time c.
No Load Balancing
The time to complete the tasks will be a1 + a2 + c. Processor A will do all the work, and incur one context switch between the two tasks.
Work-Stealing
Assume B steals a2, incurring the context switch time itself. The work will be done in max(a1, a2 + c) time. Suppose processor A begins working on a1; while it does that, processor B will steal a2 and avoid any interruption in the processing of a1. All the overhead on B is free cycles.
If a2 was the shorter task, here, you have effectively hidden the cost of a context switch in this scenario; the total time is a1.
Work-Shrugging
Assume B completes a2, as above, but A incurs the cost of moving it ("shrugging" the work). The work in this case will be done in max(a1, a2) + c time; the context switch is now always in addition to the total time, instead of being hidden. Processor B's idle cycles have been wasted, here; instead, a busy processor A has burned time shrugging work to B.
I think the problem with this idea is that it makes the threads with actual work to do waste their time constantly looking for idle processors. Of course there are ways to make that faster, like have a queue of idle processors, but then that queue becomes a concurrency bottleneck. So it's just better to have the threads with nothing better to do sit around and look for jobs.
The basic advantage of 'work stealing' algorithms is that the overhead of moving work around drops to 0 when everyone is busy. So there's only overhead when some processor would otherwise have been idle, and that overhead cost is mostly paid by the idle processor with only a very small bus-synchronization related cost to the busy processor.
Work stealing, as I understand it, is designed for highly-parallel systems, to avoid having a single location (single thread, or single memory region) responsible for sharing out the work. In order to avoid this bottleneck, I think it does introduce inefficiencies in simple cases.
If your application is not so parallel that a single point of work distribution causes scalability problems, then I would expect you could get better performance by managing it explicitly as you suggest.
No idea what you might google for though, I'm afraid.
Some issues... if a busy thread is busy, wouldn't you want it spending its time processing real work instead of speculatively looking for idle threads to offload onto?
How does your thread decide when it has so much work that it should stop doing that work to look for a friend that will help?
How do you know that the other threads don't have just as much work and you won't be able to find a suitable thread to offload onto?
Work stealing seems more elegant, because solves the same problem (contention) in a way that guarantees that the threads doing the load balancing are only doing the load balancing while they otherwise would have been idle.
It's my gut feeling that what you've described will not only be much less efficient in the long run, but will require lots of of tweaking per-system to get acceptable results.
Though in your edit you suggest that you want submitting processor to handle this, not the worker threads as you suggested earlier and in some of the comments here. If the submitting processor is searching for the lowest queue length, you're potentially adding latency to the submit, which isn't really a desirable thing.
But more importantly it's a supplementary technique to work-stealing, not a mutually exclusive technique. You've potentially alleviated some of the contention that work-stealing was invented to control, but you still have a number of things to tweak before you'll get good results, these tweaks won't be the same for every system, and you still risk running into situations where work-stealing would help you.
I think your edited suggestion, with the submission thread doing "smart" work distribution is potentially a premature optimization against work-stealing. Are your idle threads slamming the bus so hard that your non-idle threads can't get any work done? Then comes the time to optimize work-stealing.
So, by contrast to "Work Stealing", what is really meant here by "Work Shrugging", is a normal upfront work scheduling strategy that is smart about processor, cache & memory loyalty, and scalable.
Searching on combinations of the terms / jargon above yields many substantial references to follow up. Some address the added complication of machine virtualisation, which wasn't infact a concern of the questioner, but the general strategies are still relevent.