taskflow/taskflow

Features

• C++ Task Engines - Provides a high-performance framework for composing asynchronous tasks into parallel workflows in C++.
• Directed Acyclic Graph Engines - Organizes computational work into directed acyclic graphs to manage task dependencies and execution order across multi-core CPUs and GPUs.
• Task-Based Concurrency Frameworks - Organizes computational work into directed acyclic graphs to manage concurrency, dependencies, and resource scheduling.
• Heterogeneous Workflow Orchestrators - Integrates hardware-accelerated kernels and memory operations into unified, heterogeneous parallel execution pipelines.
• GPU Acceleration - Offloads intensive data processing to graphics hardware by orchestrating kernels, memory operations, and streams within a unified framework.
• High-Performance and Parallel Computing - Builds high-performance applications that distribute computational workloads across multiple CPU cores using task-based dependency graphs.
• Data Processing Pipelines - Builds multi-stage processing workflows where data flows through sequential or parallel stages to maximize throughput.
• Dynamic Task Spawning - Supports spawning new tasks during execution to handle recursive or unpredictable workloads.
• Heterogeneous Orchestrators - Integrates device-specific compute kernels into host-side task graphs to enable unified execution across CPUs and graphics accelerators.
• Declarative Pipeline Construction - Builds multi-stage data processing pipelines where stages execute either serially or in parallel to transform data.
• Parallel Work Partitioning - Configures how computational workloads are divided among threads to optimize parallel execution.
• Task Dependencies - Establishes execution order between hardware-accelerated tasks by creating precedence links that ensure specific tasks complete before others begin.
• Task Graph Definition - Enables the construction of complex execution sequences for graphics hardware by chaining memory operations, computational kernels, and specific algorithmic tasks.
• Parallel Algorithms - Provides a comprehensive suite of parallel algorithms for data processing, such as sorting, reduction, and prefix sums.
• Recursive Subflow Generators - Enables recursive parallelism by spawning sub-graphs dynamically during execution.
• Modular Program Composition - Enables grouping independent task graphs into modular units for complex workflow composition.
• Subflow Orchestrators - Supports recursive and unpredictable workload generation by allowing tasks to spawn additional parallel work during active execution.
• Work-Stealing Queues - Allocates work-stealing queues to store and distribute parallel tasks across multiple threads for efficient load balancing.
• Host-Device Synchronization - Supports running CPU-side functions within graphics hardware task graphs to handle non-device operations.
• Kernel Dispatchers - Manages the dispatch of computational tasks to graphics hardware by coordinating graph-based execution flows.
• Concurrency and Parallelism - General-purpose parallel and heterogeneous task programming system.
• Concurrency Libraries - Modern parallel task programming framework.
• Parallel Programming Frameworks - Modern C++ library for parallel task programming.
• Data Processing Pipelines - Constructs multi-stage processing workflows where data flows through concurrent stages to maximize throughput in data-intensive applications.
• Stream and Pipeline Orchestration - Orchestrates multi-stage processing by flowing data tokens through sequential or parallel stages to maximize throughput.
• Parallel Execution Profilers - Monitors and analyzes the performance of task-based programs to identify bottlenecks and optimize work distribution.
• Pipeline Execution Engines - Manages the lifecycle and state of data processing pipelines, including resetting or running tasks until specific conditions are met.
• Task Composition Engines - Enables the construction of complex workflows by nesting modular sub-graphs and pipelines within larger, reusable task structures.
• Work-Stealing Schedulers - Balances computational load dynamically by allowing idle worker threads to claim pending tasks from the queues of busy threads.
• Concurrent Task Limiters - Limits task execution by requiring the acquisition of a specified range of semaphores to manage concurrency levels.
• Execution Flow Control - Integrates conditional logic into task graphs to enable branching and cycles during execution.
• Taskflow Cancellation Handlers - Allows stopping pending tasks while permitting currently running tasks to finish.
• Stream Event Synchronizers - Records events on a stream or forces a stream to wait for the completion of specific events to manage task dependencies.
• Parallel Data Reducers - Aggregates elements from a collection into a single result using parallel reduction patterns within a task graph.
• Parallel Data Merging - Combines two sorted sequences into a single sorted sequence using parallel processing to improve throughput.
• Pipeline Execution Monitors - Queries runtime statistics of scheduling tokens during pipeline processing to track progress and identify stage, line, and token numbers.
• Task Dependency Managers - Queries task dependencies to analyze the structure of parallel execution graphs.
• Operation Capture Engines - Records sequences of stream operations into a graph structure for later execution, supporting multiple thread-safety modes.
• Cross-Memory Transfer Utilities - Copies data blocks between CPU and hardware accelerator memory locations using typed or byte-level operations.
• Parallel Prefix Sum Calculators - Computes the cumulative sum of a range of elements across multiple threads to aggregate data efficiently.
• Parallel - Organizes collections of data using multi-threaded sorting algorithms that leverage task-parallel infrastructure.
• Subflow Lifecycle Monitoring - Provides methods to verify subflow execution status to manage task lifecycles.
• Execution Progress Trackers - Observes the state and performance of task execution through custom observers to track progress and identify bottlenecks.
• Stream Lifecycle Managers - Encapsulates stream resources with unique ownership to ensure proper creation and destruction of stream handles and associated events.
• Memory Object Pools - Recycles memory resources through a dedicated pool to minimize allocation overhead during high-frequency task cycles.
• Host Memory Accessors - Provides mechanisms to retrieve memory addresses associated with graphics hardware structures to coordinate data movement.
• Placeholder Task Nodes - Reserves nodes in a task graph without assigning work to allow for dynamic task assignment.
• Task Dependency Visualizers - Exports the structure of a task graph into a standard format to allow for graphical inspection and debugging.

Star-Verlauf