Cyan4973/xxHash
XxHash
xxHash is a high-performance, non-cryptographic hash library designed for rapid checksum generation and data integrity verification. It functions as an incremental hashing engine, allowing for the processing of large or streaming data inputs by maintaining a persistent internal state across sequential chunks.
The library is engineered as a computational framework that maximizes throughput by utilizing wide CPU registers and branchless instruction pipelining. It achieves high-speed performance by aligning data access with CPU cache lines and employing multi-stage mixing functions that ensure consistent results across different hardware architectures.
These capabilities support a range of data-intensive tasks, including efficient hash table indexing, distributed system data verification, and high-speed data integrity checks. The implementation is provided as a C library, offering a standardized interface for integrating rapid hashing into existing systems.
Features
- Non-Cryptographic Hash Libraries - Provides a high-performance library for calculating rapid checksums and hash values at the speed of system memory.
- High-Speed Checksum Generators - Verifying that large files or data streams remain unchanged during storage or transmission by calculating rapid checksums at memory speed.
- Incremental Hashing Engines - Computes hash values for large or streaming data inputs by maintaining a running state across sequential chunks.
- Data Integrity Verification - Provides a utility for generating fast hash values to index information and verify data consistency across large datasets.
- SIMD-Accelerated Data Processors - Utilizes wide CPU registers and branchless instruction pipelining to maximize throughput during large block hashing operations.
- Non-Cryptographic Hashers - Calculates non-cryptographic hash values for specific memory blocks in a single operation to accelerate data processing tasks.
- Hash Tables - Calculates high-speed checksums or hash values for arbitrary data blocks to simplify indexing and integrity verification.
- Incremental Hashing Utilities - Processes large or streaming datasets in small chunks to generate consistent hash values without needing to load everything into memory.
- Hashing Implementations - Processes data in small chunks to generate a hash for large or unknown inputs by maintaining a running state.
- Incremental Hashing Contexts - Maintains a persistent internal context that allows the algorithm to process arbitrary data streams in sequential chunks.
- Hash Tables - Improves the performance of data structures by using fast, non-cryptographic hashing algorithms to map keys to values in memory.
- Cryptographic Libraries - Extremely fast non-cryptographic hash algorithm.
- General Utilities - Extremely fast non-cryptographic hash algorithm.
- Hashing Libraries - Fast non-cryptographic hash algorithm.
- Cryptography - Fast hash function.
- SIMD-Based Data Parallelism - Processes data in parallel using wide CPU registers to maximize throughput during large memory block hashing operations.
- Mixing Functions - Combines multiple bitwise operations and rotations to achieve high avalanche effects with minimal computational overhead per byte.
- Cache-Aware Memory Access - Optimizes memory throughput by structuring data access patterns to fit within the CPU cache lines for faster retrieval.
- Instruction-Level Pipelining - Minimizes CPU pipeline stalls by utilizing linear instruction sequences that avoid conditional jumps during the hashing process.
- Data Endianness - Normalizes data ingestion across different hardware architectures to ensure consistent hash results regardless of the host processor byte order.
Star history
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