thrust/thrust की मुख्य विशेषताएं क्या हैं?

thrust/thrust की मुख्य विशेषताएं हैं: Parallel Algorithms, Device Backends, C++ Parallel Programming, C++ Parallelism Libraries, Generic Programming, Iterator-Based Abstractions, Template Libraries, Heterogeneous Computing Libraries।

thrust/thrust के कुछ ओपन-सोर्स विकल्प क्या हैं?

thrust/thrust के ओपन-सोर्स विकल्पों में शामिल हैं: nvidia/thrust — Thrust is a C++ parallel algorithms library that provides a suite of standard-library-inspired interfaces for… nvidia/tensorrt — TensorRT is a deep learning inference engine and software development kit designed to optimize and deploy neural… dask/dask — Dask is a parallel computing framework and distributed task scheduler designed to scale Python data science workflows… oneapi-src/onetbb — oneTBB is a C++ parallelism library and framework designed to add multi-core parallelism to applications. It provides… microsoft/stl — This project is a C++ Standard Library implementation that provides the foundational classes and functions required by… nvidia/isaac-gr00t.

Thrust

Thrust एक हेटेरोजेनियस कंप्यूटिंग लाइब्रेरी और C++ टेम्पलेट लाइब्रेरी है जो डेटा-पैरेलल ऑपरेशन्स को निष्पादित करने के लिए हाई-लेवल टेम्पलेट्स का संग्रह प्रदान करती है। यह एक पैरेलल एल्गोरिदम लाइब्रेरी के रूप में कार्य करती है जिसे मल्टीकोर CPUs और NVIDIA GPU हार्डवेयर सहित विभिन्न हार्डवेयर बैकएंड पर काम करने के लिए डिज़ाइन किया गया है।

यह फ्रेमवर्क हेडर-ओनली इम्प्लीमेंटेशन और CPU तथा GPU मेमोरी और निष्पादन मॉडल के बीच अंतर को एब्स्ट्रैक्ट करने के लिए एक जेनेरिक-प्रोग्रामिंग पॉलिसी इंटरफेस का उपयोग करता है। यह होस्ट RAM और डिवाइस VRAM में तत्वों तक पहुँचने के लिए एक समान इंटरफेस प्रदान करने के लिए इटररेटर-आधारित डेटा एब्स्ट्रैक्शन का उपयोग करता है।

यह लाइब्रेरी पैरेलल डेटा सॉर्टिंग और बड़े डेटासेट में मानों की गणना के लिए एग्रीगेट रिडक्शन प्रोसेसिंग सहित पैरेलल प्रोसेसिंग क्षमताओं को कवर करती है। ये ऑपरेशन्स GPU हार्डवेयर पर उच्च-प्रदर्शन कंप्यूटिंग के लिए CUDA पैरेलल प्रोग्रामिंग लाइब्रेरी के माध्यम से प्रबंधित किए जाते हैं।

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.
पैरेलल प्रोसेसिंग - हेटेरोजेनियस सिस्टम के लिए C++ पैरेलल प्रोग्रामिंग लाइब्रेरी।
GPU Acceleration - Parallel programming library for GPU acceleration.
Parallel and High-Performance Computing - C++ parallel programming library for heterogeneous systems.

स्टार हिस्ट्री

thrustthrustArchived

Name: thrust/thrust
Author: thrust

View on GitHub

5,003 स्टार्स760 फोर्क्सC++2 व्यूज़

Thrust

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.
पैरेलल प्रोसेसिंग - हेटेरोजेनियस सिस्टम के लिए C++ पैरेलल प्रोग्रामिंग लाइब्रेरी।
GPU Acceleration - Parallel programming library for GPU acceleration.
Parallel and High-Performance Computing - C++ parallel programming library for heterogeneous systems.

Thrust के ओपन-सोर्स विकल्प

समान ओपन-सोर्स प्रोजेक्ट्स, जो Thrust के साथ साझा की गई सुविधाओं के आधार पर रैंक किए गए हैं।

nvidia/thrust
NVIDIA/thrust
5,003GitHub पर देखें
Thrust is a C++ parallel algorithms library that provides a suite of standard-library-inspired interfaces for execution on multi-core and accelerator hardware. It serves as a CUDA-accelerated data library and a generic parallel programming interface designed to enable high-performance data processing across GPUs and CPUs. The project implements a portable abstraction layer that allows for heterogeneous computing workflows, enabling the same core algorithm logic to run on different hardware accelerators. This is achieved through a generic programming policy design and a backend-agnostic execut
C++algorithmscppcpp11
GitHub पर देखें5,003
nvidia/tensorrt
NVIDIA/TensorRT
13,076GitHub पर देखें
TensorRT is a deep learning inference engine and software development kit designed to optimize and deploy neural networks for high-performance execution on NVIDIA GPUs. It functions as a GPU acceleration framework that reduces latency and increases throughput for trained models during production deployment. The toolkit imports models from the Open Neural Network Exchange format and transforms them into optimized engines. It utilizes graph-based model optimization, layer-fusion kernel generation, and precision-based quantization to convert floating point weights into lower precision formats.
C++deep-learninggpu-accelerationinference
GitHub पर देखें13,076
dask/dask
dask/dask
13,746GitHub पर देखें
Dask is a parallel computing framework and distributed task scheduler designed to scale Python data science workflows from single machines to large clusters. It functions as a cluster resource manager that orchestrates computational logic by representing tasks and their dependencies as directed acyclic graphs. This architecture allows the system to automate the distribution of workloads across available hardware while managing complex execution requirements. The project distinguishes itself through a lazy evaluation engine that defers data operations until they are explicitly requested, enabl
Pythondasknumpypandas
GitHub पर देखें13,746
oneapi-src/onetbb
oneapi-src/oneTBB
6,683GitHub पर देखें
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
C++
GitHub पर देखें6,683

Thrust के सभी 30 विकल्प देखें

अक्सर पूछे जाने वाले प्रश्न

thrust/thrust क्या करता है?

Thrust

Features

स्टार हिस्ट्री

Thrust

Features

Thrust के ओपन-सोर्स विकल्प

NVIDIA/thrust

NVIDIA/TensorRT

dask/dask

oneapi-src/oneTBB

अक्सर पूछे जाने वाले प्रश्न

स्टार हिस्ट्री

Thrust के ओपन-सोर्स विकल्प

NVIDIA/thrust

NVIDIA/TensorRT

dask/dask

oneapi-src/oneTBB

अक्सर पूछे जाने वाले प्रश्न