Table of Contents
Understanding Task Parallelism
Task parallelism is a style of parallel computing where the focus is on doing different work at the same time, rather than doing the same work on different data. Instead of taking one large array and splitting it across many workers, you think in terms of distinct activities or tasks that can run concurrently.
In practice, a task is some unit of work that can be executed independently of other units, at least for part of its lifetime. Tasks might differ in the operations they perform, the data they access, or the resources they require. The main goal in task parallelism is to identify such independent or partially independent tasks in your application and schedule them to run in parallel.
Task parallelism appears in many HPC applications, sometimes hidden inside higher level libraries and frameworks. Understanding it helps you reason about parallel algorithms, choose appropriate tools, and recognize when task based approaches are preferable to pure data parallel designs.
Key idea: Task parallelism focuses on executing different units of work at the same time, as long as their dependencies allow it.
Tasks, Dependencies, and Concurrency
To use task parallelism safely and effectively, you must understand how tasks depend on each other. Not every piece of work can start immediately. Some tasks must wait for data or results produced by earlier work.
You can think of a task based program as a directed acyclic graph (DAG). Each node in the graph is a task, and each edge represents a dependency, for example “Task B can start only after Task A finishes.” If a task has no unsatisfied dependencies, it is ready and can be executed. Several ready tasks can run concurrently.
There are different kinds of dependencies between tasks. Common patterns include a task that must completely finish before another one starts, tasks that share data and must synchronize at certain points, or tasks that can start as soon as part of the input is available. In HPC, tools and runtimes often manage this dependency tracking and scheduling for you, but your algorithm design must still expose tasks and their dependencies clearly.
Rule: Only tasks whose dependencies are satisfied may run in parallel. Executing dependent tasks concurrently can lead to incorrect results or race conditions.
Task Parallelism versus Data Parallelism
Data parallelism, treated in a separate chapter, is most useful when you have a large collection of similar elements and you apply the same operation to each element. In contrast, task parallelism is more natural when different parts of the computation do not all look the same.
Consider a workflow where a simulation generates output, an analysis stage processes that output, and a visualization stage creates images. Each of these stages is a different kind of work. Within each stage you might still use data parallelism, but at the higher level you can overlap these stages as tasks, so that while one time step is being analyzed, the next time step is already being simulated.
As a guideline, data parallelism often gives the simplest and most regular performance model. Task parallelism offers more flexibility in irregular, multi stage, or event driven computations, but it is also more challenging to reason about and tune.
Examples of Task Parallelism in HPC
Task parallel patterns show up in many scientific and engineering workflows.
In multi stage pipelines, the computation is divided into stages, for example preprocessing, simulation, and postprocessing. Each stage is a task that consumes data from the previous one and produces data for the next. After an initial startup period, different stages can operate in parallel on different items. This is often called pipeline parallelism.
In adaptive algorithms such as adaptive mesh refinement, the amount of work in different regions changes over time. Each region or block becomes a task. When new regions are refined, new tasks appear. When regions coarsen, tasks disappear. The irregularity of the work makes a pure data parallel approach awkward, but a task based approach handles it more naturally.
In ensemble and parameter studies, many independent simulations must be run with different parameter sets or initial conditions. Each simulation is a task. Since there are few or no dependencies between these tasks, they can often run completely independently, spread across a cluster or even across multiple clusters.
In graph based computations, such as some network analyses or sparse matrix algorithms, tasks may correspond to regions of a graph. Tasks execute when the necessary neighbors have been processed. This again leads naturally to a DAG view of computation.
Granularity of Tasks
A central design question in task parallelism is task granularity, which is the size or amount of work performed by a single task. Task granularity has a strong influence on performance.
If tasks are too fine grained, for example doing only a few arithmetic operations each, the overhead of managing tasks, creating them, scheduling them, and synchronizing them, can dominate the useful work. In that case, adding more parallelism by splitting tasks further actually reduces performance.
If tasks are too coarse grained, you may not expose enough parallelism to keep all processing units busy, or you may get poor load balance when different tasks have very different runtimes. You also lose opportunities to overlap computation and communication, which is important in distributed memory systems.
Choosing a good task size is therefore a balancing act. You want tasks large enough that the overhead is small compared to the useful work, but small enough that you can generate enough of them to occupy the available resources and adapt to irregular workloads.
Guideline: Make tasks large enough that scheduling overhead is negligible, but small enough to expose sufficient parallelism and enable good load balance.
Task Scheduling and Load Balance
Once you have defined tasks and their dependencies, some system must assign tasks to processing elements, such as threads or processes. This is the job of a task scheduler. Schedulers are usually part of the runtime system of parallel libraries or languages.
A simple scheduler might use a fixed assignment, for example assigning the first group of tasks to thread 0, the next group to thread 1, and so on. This approach works reasonably well when all tasks take roughly the same amount of time. However, many real applications have tasks with very different runtimes. In that case, a static assignment leads to idle resources and poor performance.
More advanced schedulers use dynamic strategies and load balancing. A common idea is work stealing, where each worker maintains its own queue of tasks. When a worker finishes its tasks and becomes idle, it steals tasks from the queue of another busy worker. This tends to balance the load automatically without centralized control.
In distributed memory settings, task scheduling and load balancing become more complex because tasks might need data that resides on remote nodes. Techniques like over decomposition, where you create more tasks than processing elements, and dynamic mapping of tasks to processes are common strategies. Dedicated chapters on load balancing will discuss this topic in more detail. Here it is enough to note that task parallelism often relies heavily on dynamic scheduling for good performance.
Task Parallel Patterns
Several recurring patterns help structure task parallel programs.
Pipeline parallelism appears when data flows through a sequence of stages. Each stage is a task type, and multiple items can be in different stages at once. After an initial fill phase, all stages can work concurrently on different items, which can significantly improve throughput.
Fork join parallelism is a pattern where the program occasionally splits into multiple tasks that run concurrently, then later joins back to a single thread of control. A common structure is: a serial region, followed by a fork where tasks are spawned, followed by a join where all tasks complete, followed by another serial region. Many parallel libraries support this pattern explicitly.
Event driven and task graph parallelism occur when the order of tasks is not a simple sequence or pipeline, but depends on events or conditions during execution. Tasks are created dynamically as certain conditions are met, and dependencies are expressed as a general DAG. The runtime then executes tasks whenever their dependencies are satisfied. This is common in irregular simulations and in some modern task based runtimes.
Recognizing which of these patterns best matches your application can help you design a cleaner and more efficient parallel structure.
Task Parallelism across Programming Models
Different programming models express task parallelism in different ways. In shared memory environments, threads often execute tasks from shared queues. Many models allow tasks to be created dynamically and let the runtime decide which thread runs which task.
In distributed memory environments, task parallelism is often expressed by mapping tasks to processes, or by using libraries that manage distributed task graphs. Processes may communicate to exchange data between tasks, or to signal that dependencies are satisfied.
Hybrid programs can combine both, using threads for fine grained tasking within nodes and processes for coarse grained distribution across nodes. The same conceptual ideas of tasks and dependencies apply, but the cost model changes when communication crosses node boundaries.
You will encounter specific mechanisms for creating and scheduling tasks in later chapters on particular programming models. At this stage, the important point is that these mechanisms are all different interfaces to the same underlying concept of task parallel execution.
When to Use Task Parallelism
Task parallelism is particularly useful when your computation consists of several distinct steps that can overlap in time, or when the workload is irregular and difficult to partition evenly ahead of time.
You might favor task parallelism if your application has a natural pipeline structure, if you run many independent or loosely coupled jobs, or if workloads change dynamically in ways that are hard to predict. Task based runtimes can then adapt at run time by assigning more or fewer tasks to each worker as needed.
In contrast, if your problem naturally fits a regular array of data with uniform operations, data parallelism will often be simpler and more efficient. In many realistic HPC applications, the best solutions are hybrids that combine data parallelism within tasks and task parallelism across components of the computation.
Practical rule: Use task parallelism when work units differ, arrive dynamically, or form a pipeline. Use data parallelism when you repeatedly apply the same operations to large, regular data sets.
By learning to recognize and structure task parallelism in your applications, you gain an important tool for building scalable, flexible, and efficient HPC codes, especially in the presence of irregular or evolving workloads.