oneapi-src/oneTBB
OneTBB
oneTBB is a C++ parallelism library and framework designed to add multi-core parallelism to applications. It provides a task-based parallelism model that maps logical computational tasks to available hardware cores to eliminate the need for manual thread management.
The library functions as a multi-core scaling tool, utilizing generic templates to scale data-parallel operations across processors for portable performance. It employs a task-based framework to ensure computational workloads are distributed across hardware resources.
The project covers shared memory parallelism, multi-core task scheduling, and data parallelism scaling. It utilizes a work-stealing task scheduler, recursive range splitting, and dynamic load balancing to manage the distribution of work across cores at runtime.
Features
- C++ Parallel Programming - Provides a comprehensive framework for implementing multi-core execution in C++ applications without manual thread management.
- Data Parallelism Scaling - Enables running operations across large datasets using templates to ensure portable performance across different multi-core processors.
- Dynamic Load Balancing - Implements runtime adjustments to workload distribution across cores to prevent individual processor bottlenecks.
- Work-Stealing Schedulers - Ships a work-stealing scheduler that dynamically balances computational tasks across hardware threads.
- Logical Parallelism Mapping - Automatically assigns computational tasks to available hardware cores to eliminate the need for manual thread management.
- Multi-Core Scheduling - Distributes computational workloads across available processor cores to optimize hardware utilization and execution speed.
- Multi-Core Scaling Libraries - Offers a set of generic templates and tools for scaling data-parallel operations across processors for portable performance.
- C++ Parallelism Libraries - Provides a library for adding multi-core parallelism to applications through task scheduling and data-parallel templates.
- Parallelism Templates - Utilizes generic C++ templates to decouple algorithmic logic from underlying hardware resource management for portable performance.
- Parallel Computing Implementation - Provides strategies for scaling computational throughput across multiple CPU cores in C++ applications.
- Data-Parallel Scaling - Executes data-parallel operations across multi-core processors using generic templates for portable and scalable performance.
- Parallel Task Execution - Implements a model for executing multiple asynchronous computations simultaneously and aggregating their results.
- Task-Based Concurrency Frameworks - Implements a framework that maps logical computational tasks to available hardware cores to avoid manual thread management.
- Recursive Range Splitting - Divides large datasets into smaller sub-ranges recursively until they reach a grain size suitable for parallel execution.
- Shared Memory Parallelism - Facilitates the development of applications that execute multiple tasks simultaneously within a single shared memory space.
- Thread Pool Abstractions - Provides an abstraction layer that maps logical parallel tasks to a pool of worker threads, hiding physical thread management.
- Development Environments - Template library for implementing parallel programming on multi-core processors.
Star history
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