Open Source Assembly Apps for Apple iPhone

libjpeg-turbo is a JPEG image codec that uses SIMD instructions (MMX, SSE2, NEON, AltiVec) to accelerate baseline JPEG compression and decompression on x86, x86-64, ARM, and PowerPC systems. On such systems, libjpeg-turbo is generally 2-6x as fast as libjpeg, all else being equal. On other types of systems, libjpeg-turbo can still outperform libjpeg by a significant amount, by virtue of its highly-optimized Huffman coding routines. In many cases, the performance of libjpeg-turbo rivals that of proprietary high-speed JPEG codecs. libjpeg-turbo implements both the traditional libjpeg API as well as the less powerful but more straightforward TurboJPEG API. libjpeg-turbo also features colorspace extensions that allow it to compress from/decompress to 32-bit and big-endian pixel buffers (RGBX, XBGR, etc.), as well as a full-featured Java interface.

Sulin is rolling donkey Gitlab: https://gitlab.com/sulinos Github: https://github.com/sulinos Telegram: https://t.me/sulinos powered by donkey team

XNNPACK is a highly optimized, low-level neural network inference library developed by Google for accelerating deep learning workloads across a variety of hardware architectures, including ARM, x86, WebAssembly, and RISC-V. Rather than serving as a standalone ML framework, XNNPACK provides high-performance computational primitives—such as convolutions, pooling, activation functions, and arithmetic operations—that are integrated into higher-level frameworks like TensorFlow Lite, PyTorch Mobile, ONNX Runtime, TensorFlow.js, and MediaPipe. The library is written in C/C++ and designed for maximum portability, efficiency, and performance, leveraging platform-specific instruction sets (e.g., NEON, AVX, SIMD) for optimized execution. It supports NHWC tensor layouts and allows flexible striding along the channel dimension to efficiently handle channel-split and concatenation operations without additional cost.

Yes, finally. The author decide to schedule some of his busy time to release the "Chinese Language Formula"- Step By Step. It will take a while, but eventually Chinese Language Processing is no longer a problem.

FastFlow is a C/C++ programming framework supporting the development of pattern-based parallel programs on multi/many-core, GPUs and distributed platforms. FastFlow run-time is built upon non-blocking threads and lock-free queues. Thanks to its very efficient CAS-free communication/synchronization support (e.g. few clock cycles core-to-core latency), FastFlow effectively supports the exploitation of fine grain parallelism, e.g. parallel codes managing very high frequency streams on commodity multi-core. FastFlow works on x86, x86_64, PPC, ARM, and Linux, Mac and Win XP/7. From Jan 1, 2018 code development migrated on GitHub at https://github.com/fastflow/fastflow

An introduction to assembly on Apple Silicon Macs. In this repository, I will code along with the book Programming with 64-Bit ARM Assembly Language, adjusting all sample code for Apple's ARM64 line of computers. While Apple's marketing material seems to avoid a name for the platform and talks only about the M1 processor, the developer documentation uses the term "Apple Silicon". You need Xcode 12.2 or later, and to make things easier, the command line tools should be installed. This ensures that the tools are found in default locations. All application samples also require at least macOS Big Sur, iOS 14 or their respective watchOS or tvOS equivalents. Especially for the later three systems it is not a necessity per-se (neither is Xcode 12.2), but it makes things a lot simpler. Finally, while all samples can be adjusted to work on the iPhone and all other of Apple's ARM64 devices, for best results you should have access to an Apple Silicon Mac.