Work stealing
Work stealing is a dynamic scheduling strategy that redistributes pending tasks to idle workers to balance workloads and improve utilization.
Clojure: The Essential Reference
Work-stealing is a parallel scheduling technique in which an idle worker takes unprocessed tasks from another worker's queue.
- How it is used
- Keeps workers occupied during parallel folds when chunks take unpredictable amounts of time.
- How deep it goes
- Explains chunk splitting and idle-worker stealing, but does not detail the underlying scheduling algorithm.
- Examples
- Uses a million-element fold sum and a benchmark where folding 1,000 elements is slower than reducing them.
- What you can do after
- Evaluate/understand when work stealing can help a fold and why a small fold may still be slower.
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Learn Concurrent Programming with Go
Work stealing is a scheduling technique in which an execution worker takes tasks from another worker's queue to improve utilization.
- How it is used
- Balances goroutine execution across kernel-level threads when blocking or uneven queue loads would otherwise leave processors idle.
- How deep it goes
- Explains queue migration and replacement threads, but stops short of runtime implementation details or stealing policies.
- Examples
- Uses local run queues, I/O blocking, replacement thread creation, and an empty queue stealing from a busy queue.
- What you can do after
- Evaluate how Go maintains goroutine progress and processor utilization during blocking operations and uneven workloads.
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Learn Haskell by Example
Work stealing is a scheduling technique in which an idle execution worker takes available work from a shared pool.
- How it is used
- Schedules sparks across Haskell execution contexts during parallel evaluation.
- How deep it goes
- Explains spark pools, execution contexts, and failure states, while deferring scheduling internals to the runtime.
- Examples
- Uses parallel list mapping, pixel-vector processing, chunked execution, and runtime spark statistics.
- What you can do after
- Apply it via Control.Parallel.Strategies to parallelize list or vector computations and inspect spark behavior.
C++ Concurrency in Action, Second Edition
Work stealing is a scheduling technique in which an idle worker takes tasks from another worker’s queue.
- How it is used
- Balances uneven task distribution in a thread pool while reducing the synchronization overhead of shared work queues.
- How deep it goes
- Implements stealing with a mutex-protected deque and opposite-end access, while leaving lock-free queue design beyond scope.
- Examples
- Uses Quicksort to illustrate how recursive tasks can accumulate unevenly on one worker’s local queue.
- What you can do after
- Apply work stealing by giving workers local queues and allowing idle workers to take tasks from another queue.
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100 Go Mistakes and How to Avoid Them
Work stealing is a scheduling technique in which an underutilized processor obtains runnable tasks from another processor's queue.
- How it is used
- Keeps processors productive by redistributing runnable goroutines when local and global queues do not provide work.
- How deep it goes
- Explains queue-search pseudocode and scheduling context, but stops short of runtime implementation details.
- Examples
- Uses a four-core scheduling state and runtime.schedule pseudocode to illustrate queue checks and stealing.
- What you can do after
- Evaluate/understand how Go redistributes runnable goroutines and distinguishes stealing from preemptive context switching.
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Related concepts
- green threadused by · A green thread is a lightweight user-space execution unit whose scheduling and context switching are managed by a runtime and multiplexed onto operating-system threads.
- reduction operationused by · A reduction operation combines multiple input values or partial results according to a combining rule to produce fewer results, often a single aggregate.
- parallel streamused by · A parallel stream is a Java stream configured to allow its operations to process elements concurrently across multiple execution threads.
- parallelismimplements · Parallelism is the concurrent execution of multiple computational operations, often across multiple processing resources.
- work queue patternuses · The work queue pattern stores pending units of work for workers or processors to retrieve and process, often asynchronously.
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