google/highway

Features

• Hardware Dispatchers - Features a dynamic dispatcher that detects CPU capabilities at startup to select the most efficient instruction set.
• Hardware Abstraction Layers - Acts as a hardware abstraction layer that maps processor-specific intrinsics into a unified C++ API.
• SIMD-Based Data Parallelism - Provides a portable interface to write data-parallel code that maps to hardware-accelerated SIMD instructions.
• Cross-Architecture Performance Strategies - Enables high-performance software execution across multiple processor types without requiring architecture-specific assembly.
• Instruction Set Targeting - Selects the best available CPU instruction set during program execution to maximize performance on heterogeneous hardware.
• Runtime Instruction Dispatchers - Ships a dynamic instruction dispatcher that selects the most efficient CPU instruction set based on detected hardware.
• SIMD Abstraction Layers - Provides a portable C++ template interface that maps to architecture-specific SIMD intrinsics at compile time.
• SIMD Libraries - Provides a portable C++ library for writing SIMD code that maps to various CPU instruction sets.
• Loop Vectorization - Provides a portable interface to implement loop vectorization using strip-mining strategies across various CPU architectures.
• Vector Masking and Reduction - Provides hardware-specific sum and max instructions to aggregate vector elements into scalar values.
• Conditional Execution Masks - Uses binary masks to control vector lane operations and avoid branching in data-parallel code.
• Hardware Instruction Targeting - Executes specialized code paths by targeting specific hardware instructions based on the detected CPU target.
• Vector Instruction Targets - Optimizes binaries by resolving specific SIMD instruction set targets like SSE or AVX during compilation.
• Vector Instruction Mapping - Provides a portable interface that maps data-parallel logic to architecture-specific vector instruction intrinsics.
• Custom SIMD Operations - Allows adding new hardware-accelerated functions by writing target-specific implementations for all supported architectures.
• SIMD Memory Alignment - Ensures memory allocations start on specific byte boundaries to maximize CPU cache efficiency and prevent faults.
• SIMD Memory Managers - Includes a memory manager for aligned allocation and masked load-store operations to optimize vector processing.
• High-Performance Data Analysis - Accelerates mathematical operations, sorting, and hashing using optimized low-level vector algorithmic toolkits.
• Vectorized Memory Access - Moves data between memory and registers using aligned, unaligned, masked, or interleaved access patterns.
• Masked Memory Operations - Implements masked load and store operations to prevent out-of-bounds memory access during partial vector processing.
• Vector Lane Shuffling - Implements vector lane shuffling including reverses, slides, and broadcasts to rearrange elements within registers.
• Cryptographic Accelerators - Implements hardware-accelerated cryptographic primitives and carryless multiplication using SIMD vector instructions.
• Hardware Target Extensions - Integrates new processor architectures by defining target identifiers and connecting them to dispatch mechanisms.
• Vector Element Comparison - Evaluates equality and relational conditions between vectors to create binary masks for conditional processing.
• Vector Mask Manipulation - Provides tools to create and combine binary masks for conditional execution on specific vector lanes.
• Vectorized Math Functions - Offers a vectorized math library for fast trigonometric, hyperbolic, and complex arithmetic using CPU instructions.
• Parallel Math Libraries - Provides a collection of hardware-accelerated functions for performing element-wise and complex math on vectors.
• Vector Mathematics - Implements hardware-accelerated dot products and other mathematical functions across various CPU architectures.
• Vector Arithmetic Operations - Provides hardware-accelerated element-wise arithmetic and fused multiply-add operations across diverse CPU architectures.
• Vector Type Converters - Provides functions for changing element widths and casting between integer and floating-point representations in vector registers.
• Conditional Vector Operations - Implements masked conditional execution to control which vector lanes perform operations without branching.
• Hardware-Accelerated Primitive Substitutions - Implements secure hashing and encryption rounds using processor-specific hardware-accelerated primitives.
• Compile-Time Metaprogramming - Implements compile-time logic to resolve optimal data types and vector widths across different hardware targets.
• Parallel Programming Frameworks - Performance-portable SIMD intrinsics for various architectures.

Historial de estrellas