thrust/thrustArchived
Thrust
Thrust is a heterogeneous computing library and C++ template library that provides a collection of high-level templates for executing data-parallel operations. It functions as a parallel algorithms library designed to work across different hardware backends, including multicore CPUs and NVIDIA GPU hardware.
The framework utilizes a header-only implementation and a generic-programming policy interface to abstract the differences between CPU and GPU memory and execution models. It employs an iterator-based data abstraction to provide a uniform interface for accessing elements across host RAM and device VRAM.
The library covers parallel processing capabilities, including parallel data sorting and aggregate reduction processing for calculating values across large datasets. These operations are managed through a CUDA parallel programming library for high-performance computing on GPU hardware.
Features
- Parallel Algorithms - Provides a comprehensive collection of high-level parallel algorithms for data-parallel operations.
- Device Backends - Provides a specialized CUDA-based backend to offload data-parallel computations to NVIDIA GPUs.
- C++ Parallel Programming - Enables high-performance C++ programming for data-parallel operations across heterogeneous hardware.
- C++ Parallelism Libraries - Functions as a specialized C++ parallelism library offering high-level templates for data-parallel operations.
- Generic Programming - Utilizes generic programming with template parameters to decouple execution strategies from algorithm implementations.
- Iterator-Based Abstractions - Employs iterator-based abstractions to provide a uniform interface for accessing host and device memory.
- Template Libraries - Implemented as a C++ template library to ensure generic behavior across different hardware backends.
- Heterogeneous Computing Libraries - Acts as a heterogeneous computing library that abstracts the differences between CPU and GPU execution models.
- GPU-Accelerated Processing - Facilitates GPU-accelerated processing by moving large datasets to parallel hardware for fast computation.
- Parallel Sorting - Implements high-performance parallel data sorting by leveraging GPU and multicore CPU hardware.
- Header-Only Libraries - Ships as a header-only template library to enable aggressive compiler inlining and optimization.
- Parallel Reductions - Provides parallel reduction operations to calculate sums, minimums, or maximums across distributed cores.
- Compile-Time Type Dispatch - Uses C++ templates for compile-time type dispatch to eliminate runtime overhead in performance-critical paths.
- CUDA Libraries - Provides a robust set of CUDA-based algorithms and data structures for high-performance GPU computing.
- Aggregate Reductions - Provides parallel aggregate reduction processing for calculating values across large datasets.
- Asynchronous Execution - Implements non-blocking execution models to overlap data transfers with kernel execution.
- Parallel Processing - C++ parallel programming library for heterogeneous systems.
- GPU Acceleration - Parallel programming library for GPU acceleration.
- Parallel and High-Performance Computing - C++ parallel programming library for heterogeneous systems.
