arrayfire/arrayfire

Features

• Compute Backend Abstractions - Provides a hardware-agnostic abstraction layer that maps a single API to CUDA, OpenCL, and CPU backends at runtime.
• GPU-Accelerated Computation - Provides a comprehensive framework for offloading general numerical and mathematical operations to GPU hardware for high-performance processing.
• Array Type Casting - Converts numerical arrays between different data types to optimize processing performance.
• Hardware Abstraction Layers - Includes a hardware abstraction layer that routes tensor operations to available CPU or GPU backends automatically.
• Hardware Acceleration Kernels - Executes optimized computational kernels across different hardware backends to accelerate intensive mathematical operations.
• Hardware-Agnostic Accelerators - Implements a hardware-agnostic framework that compiles compute graphs to run efficiently across diverse GPU and CPU backends.
• Numerical Computing Libraries - Serves as a high-performance numerical computing library for vectorized tensor operations and linear algebra.
• Tensor Indexing - Selects specific elements, rows, or columns of tensors using scalars, sequences, and slicing.
• Operation Fusion Optimizations - Merges multiple mathematical operations into single kernels to reduce memory overhead and improve throughput.
• Advanced Array Indexing - Retrieves tensor elements using coordinate arrays via Cartesian product expansion or direct mapping.
• Host-to-Device Data Transfers - Copies tensor data between device memory and the host for CPU-side access.
• Tensor Slicing - Extracts specific rows, columns, or subarrays using sequences, spans, and strides.
• Hardware-Threaded Loop Distribution - Distributes high-level loop iterations across physical hardware threads to maximize GPU and CPU throughput.
• Boolean Masking - Selects array elements that satisfy specific boolean conditions using masking techniques.
• GPU Interoperability Primitives - Provides primitives to share data and execution contexts between CUDA and OpenCL applications.
• Device-Specific Memory Allocators - Allocates multi-dimensional arrays directly on hardware accelerators using various numerical types.
• Device Buffer Managers - Coordinates the allocation and synchronization of multi-dimensional array buffers between host and accelerator memory.
• General JIT Compilers - Uses a just-in-time compiler to generate optimized machine code for complex numerical kernels at runtime.
• Tensor Expression Compilers - Uses a JIT engine to compile mathematical expressions and fuse operations to minimize memory overhead.
• Reshaping - Rearranges the shape and dimensions of arrays without modifying the underlying data.
• Kernel Fusion Operations - Combines multiple mathematical operations into single compute kernels to minimize global memory access.
• Array Data Copying - Implements deep copying of arrays and writing data from pointers into existing tensors.
• Array Population - Populates tensors with constants, random distributions, or data copied from memory pointers.
• Array Transpositions - Provides operations to generate transposed or conjugate-transpose versions of multi-dimensional arrays.
• Digital Signal Processing Libraries - Provides a toolkit for parallel digital signal processing, including fast Fourier transforms and convolutions on multi-dimensional data.
• Signal Convolutions - Implements multi-dimensional convolutions for processing signals and images by applying computational kernels.
• Fourier Transforms - Provides high-performance forward and inverse fast Fourier transforms to convert signals between time and frequency domains.
• Element-wise Array Operations - Performs high-performance element-wise array operations across multi-dimensional tensors.
• Generalized Matrix Multiplications - Provides high-performance generalized matrix multiplications for neural network layers and linear transformations.
• Vectorized Array Operations - Executes arithmetic and logical functions across entire arrays in parallel to optimize performance.
• Just-In-Time Numerical Compilation - Translates high-level mathematical expressions into optimized machine code during execution to maximize performance.
• Linear Algebra Libraries - Implements high-performance parallel linear algebra libraries for matrix operations and solving complex systems of equations.
• Multi-Dimensional Arrays - Creates containers of specified dimensions and data types using host or device pointers.
• Numerical Array Operations - Performs high-performance mathematical calculations, indexing, and reshaping on multi-dimensional arrays using vectorized operations.
• Signal Filtering Pipelines - Processes data using finite and infinite impulse response filters to remove noise from signals.
• Deferred Execution Graphs - Implements a deferred execution graph to optimize computation chains before triggering final array operations.
• Framework Interoperability Layers - Ships interoperability primitives to share data and execution contexts between the engine and external compute libraries like CUDA and OpenCL.
• GPU Accelerated Computer Vision - Analyzes and transforms digital images through GPU-accelerated feature detection and color space conversion.
• Machine Learning Implementations - Provides the computational primitives necessary to implement and train core machine learning algorithms and neural networks.
• Hardware-Accelerated - Combines with other hardware-accelerated codebases to extend functionality within larger applications.
• Batch Processing - Implements utilities for performing bulk data operations efficiently by parallelizing across a batch dimension.
• Vectorized Data Processing - Executes operations across N-dimensional arrays by tiling data and parallelizing loop iterations on hardware.
• Mobile and Embedded Accelerator Execution - Runs parallel computations on specialized embedded devices and mobile accelerators.
• Execution Optimizations - Implements runtime optimizations to increase memory throughput and minimize temporary allocations during execution.
• Expression Evaluators - Provides an expression evaluator that processes mathematical and logical expressions via JIT compilation.
• Array Reductions - Computes single scalar values from arrays through reduction operations like sum and maximum.
• Data Interpolation - Estimates unknown values between known data points using one-dimensional and two-dimensional mathematical interpolation.
• Linear System Solvers - Implements direct linear solvers and routines for solving systems of linear equations and least squares problems.
• Matrix Decompositions - Performs mathematical matrix decompositions such as LU, QR, and Cholesky to simplify linear algebra problems.
• Sparse Matrix Storage - Provides memory-efficient sparse matrix storage and management for handling large datasets with many zero elements.
• Prefix Scan Operations - Implements prefix scan operations to produce arrays of cumulative results.
• पैरेलल प्रोसेसिंग - जनरल-पर्पस GPU कंप्यूटिंग लाइब्रेरी।
• Concurrency and Parallelism - General purpose GPU computing library.
• Parallel and High-Performance Computing - General-purpose GPU computing library.

स्टार हिस्ट्री