#c_lang #convolutional_neural_network #convolutional_neural_networks #cpu #inference #inference_optimization #matrix_multiplication #mobile_inference #multithreading #neural_network #neural_networks #simd

https://github.com/google/XNNPACK

#rust #search_engine #machine_learning #algorithm #neural_network #high_performance #artificial_intelligence #simd #data_structures #image_search #recommender_system #approximate_nearest_neighbor_search #similarity_search #k_nearest_neighbors #hnsw #vector_search

https://github.com/hora-search/hora

#java #database #grafana #hacktoberfest #influxdb #iot #low_latency #metrics #monitoring #postgres #postgresql #questdb #simd #sql #time_series #timeseries_database #tsdb

https://github.com/questdb/questdb

#cplusplus #avx #avx_512 #avx_instructions #avx2 #avx512 #intrinsics #neon #simd #simd_instructions #simd_intrinsics #simd_library #simd_parallelism #simd_programming #sse42 #wasm

https://github.com/google/highway

#cplusplus #baseband #ccsds #digital_signal_processing #satellite #sdr #simd #volk

https://github.com/SatDump/SatDump

#go #high_performance #jit #json #simd

https://github.com/bytedance/sonic

#scala #clickhouse #simd #spark_sql #vectorization #velox

https://github.com/oap-project/gluten

#cplusplus #aarch64 #arm #arm64 #avx2 #avx512 #c_plus_plus #clang #clang_cl #cpp11 #gcc_compiler #json #json_parser #json_pointer #loongarch #neon #simd #sse42 #vs2019 #x64

The simdjson library is very fast and efficient for parsing JSON files. It uses special computer instructions called SIMD to parse JSON up to 4 times faster than other popular parsers. Here are the key benefits Parses JSON much quicker than other libraries.
- **Easy to Use** Ensures full JSON and UTF-8 validation without losing any data.
- **Automatic Optimization** Designed to avoid unexpected errors and surprises.

Using simdjson can significantly speed up your application's performance when dealing with large amounts of JSON data.

https://github.com/simdjson/simdjson

#c_lang #convolutional_neural_network #convolutional_neural_networks #cpu #inference #inference_optimization #matrix_multiplication #mobile_inference #multithreading #neural_network #neural_networks #simd

XNNPACK is a powerful tool that helps make neural networks run faster on various devices like smartphones, computers, and Raspberry Pi boards. It supports many different types of processors and operating systems, making it very versatile. XNNPACK doesn't work directly with users but instead helps other machine learning frameworks like TensorFlow Lite, PyTorch, and ONNX Runtime to perform better. This means your apps and programs that use these frameworks can run neural networks more quickly and efficiently, which is beneficial because it saves time and improves performance.

https://github.com/google/XNNPACK

#cplusplus #avx #avx_512 #avx_instructions #avx2 #avx512 #intrinsics #neon #simd #simd_instructions #simd_intrinsics #simd_library #simd_parallelism #simd_programming #sse42 #wasm

Highway is a C++ library that helps make software run faster and use less energy. It does this by using SIMD (Single Instruction, Multiple Data) instructions, which let the CPU perform the same operation on many pieces of data at once. This can make programs up to 10 times faster and reduce energy use by up to five times. Highway works on many different types of computers and is easy to use, making it a good choice for developers who want to improve their software's performance.

https://github.com/google/highway