fpng-java VS xsimd

Compare fpng-java vs xsimd and see what are their differences.

fpng-java

Java Wrapper for the fast, native FPNG Encoder (by manticore-projects)

xsimd

C++ wrappers for SIMD intrinsics and parallelized, optimized mathematical functions (SSE, AVX, AVX512, NEON, SVE)) (by xtensor-stack)
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fpng-java xsimd
1 5
2 2,348
- 1.5%
7.8 8.6
7 months ago 6 days ago
C++ C++
GNU Affero General Public License v3.0 BSD 3-clause "New" or "Revised" License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

fpng-java

Posts with mentions or reviews of fpng-java. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-12-09.

xsimd

Posts with mentions or reviews of xsimd. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2025-01-05.

What are some alternatives?

When comparing fpng-java and xsimd you can also consider the following projects:

MIPP - Portable wrapper for SIMD and vector instructions written in C++11. Compatible with NEON, SSE, AVX, AVX-512 and SVE (length specific).

highway - Performance-portable, length-agnostic SIMD with runtime dispatch

Simd - C++ image processing and machine learning library with using of SIMD: SSE, AVX, AVX-512, AMX for x86/x64, NEON for ARM.

libsimdpp - Portable header-only C++ low level SIMD library

DirectXMath - DirectXMath is an all inline SIMD C++ linear algebra library for use in games and graphics apps

Vc - SIMD Vector Classes for C++

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InfluxDB high-performance time series database
Collect, organize, and act on massive volumes of high-resolution data to power real-time intelligent systems.
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