#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
https://github.com/google/highway
GitHub
GitHub - google/highway: Performance-portable, length-agnostic SIMD with runtime dispatch
Performance-portable, length-agnostic SIMD with runtime dispatch - google/highway
#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
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
GitHub
GitHub - simdjson/simdjson: Parsing gigabytes of JSON per second : used by Facebook/Meta Velox, the Node.js runtime, ClickHouse…
Parsing gigabytes of JSON per second : used by Facebook/Meta Velox, the Node.js runtime, ClickHouse, WatermelonDB, Apache Doris, Milvus, StarRocks - simdjson/simdjson
#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
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
GitHub
GitHub - google/highway: Performance-portable, length-agnostic SIMD with runtime dispatch
Performance-portable, length-agnostic SIMD with runtime dispatch - google/highway
#c_lang #avx2 #c99 #cpu_inference #deep_learning #from_scratch #inference_engine #kimi_k3 #linear_attention #llm #llm_inference #machine_learning #memory_efficient #mixture_of_experts #moe #mxfp4 #quantization #simd #systems_programming #transformer #zero_dependencies
kimi-k3-in-c runs the Kimi K3 language model on a CPU without a GPU or machine learning framework. It supports text generation and multi-turn text chat on Linux, macOS, and Windows. By loading model data from disk as needed, it can run the full model with limited memory, making it useful on machines that cannot hold the model in RAM. It requires roughly 1.7 TB of free storage, and generation can be slow, especially with less memory or slower disks.
https://github.com/FareedKhan-dev/kimi-k3-in-c
kimi-k3-in-c runs the Kimi K3 language model on a CPU without a GPU or machine learning framework. It supports text generation and multi-turn text chat on Linux, macOS, and Windows. By loading model data from disk as needed, it can run the full model with limited memory, making it useful on machines that cannot hold the model in RAM. It requires roughly 1.7 TB of free storage, and generation can be slow, especially with less memory or slower disks.
https://github.com/FareedKhan-dev/kimi-k3-in-c
GitHub
GitHub - FareedKhan-dev/kimi-k3-in-c: A 2.78-trillion-parameter Kimi K3 running inference on a single CPU in 8.24 GB of RAM. Portable…
A 2.78-trillion-parameter Kimi K3 running inference on a single CPU in 8.24 GB of RAM. Portable C99: no BLAS, no framework, no GPU. - FareedKhan-dev/kimi-k3-in-c
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