Search Results for "cpu speed"
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Showing 14 open source projects for "cpu speed"
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MNN
MNN is a blazing fast, lightweight deep learning framework
MNN is a highly efficient and lightweight deep learning framework. It supports inference and training of deep learning models, and has industry leading performance for inference and training on-device. At present, MNN has been integrated in more than 20 apps of Alibaba Inc, such as Taobao, Tmall, Youku, Dingtalk, Xianyu and etc., covering more than 70 usage scenarios such as live broadcast, short video capture, search recommendation, product searching by image, interactive marketing, equity... -
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CEmu emulator
Third-party TI-84 Plus CE / TI-83 Premium CE emulator
Developer-oriented emulator of the eZ80-based TI-84 Plus CE / TI-83 Premium CE calculators. CEmu is a third-party TI-84 Plus CE / TI-83 Premium CE calculator emulator, focused on developer features. The core is programmed in C and the GUI in C++ with Qt, for performance and portability reasons. CEmu works natively on Windows, macOS, and Linux! Easy setup - get running by doing a one-time-only connection of your calculator! Accurate and fast emulation. Customizable speed/throttling. Resizable... -
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Shumai
Fast Differentiable Tensor Library in JavaScript & TypeScript with Bun
...It allows seamless integration of machine learning, deep learning, and custom differentiable programs into web-based or server-side environments without relying on Python frameworks. The library supports matrix operations, gradient computation, and tensor conversions with intuitive APIs and near-native speed, thanks to Bun’s low-overhead FFI bindings. It can automatically leverage GPU acceleration on Linux (via CUDA) and CPU computation on macOS. -
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MegEngine
Easy-to-use deep learning framework with 3 key features
...You can represent quantization/dynamic shape/image pre-processing and even derivation in one model. After training, just put everything into your model and inference it on any platform at ease. Speed and precision problems won't bother you anymore due to the same core inside. In training, GPU memory usage could go down to one-third at the cost of only one additional line, which enables the DTR algorithm. Gain the lowest memory usage when inferencing a model by leveraging our unique pushdown memory planner. NOTE: MegEngine now supports Python installation on Linux-64bit/Windows-64bit/MacOS(CPU-Only)-10.14+/Android 7+(CPU-Only) platforms with Python from 3.5 to 3.8. ... -
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Bandicoot
fast C++ library for GPU linear algebra & scientific computing
* Fast GPU linear algebra library (matrix maths) for the C++ language, aiming towards a good balance between speed and ease of use * Provides high-level syntax and functionality deliberately similar to Matlab * Provides an API that is aiming to be compatible with Armadillo for easy transition between CPU and GPU linear algebra code * Useful for algorithm development directly in C++, or quick conversion of research code into production environments * Distributed under the permissive Apache 2.0 license, useful for both open-source and proprietary (closed-source) software * Can be used for machine learning, pattern recognition, computer vision, signal processing, bioinformatics, statistics, finance, etc * Downloads: http://coot.sourceforge.io/download.html * Documentation: http://coot.sourceforge.io/docs.html * Bug reports: http://coot.sourceforge.io/faq.html * Git repo: https://gitlab.com/conradsnicta/bandicoot-code -
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HighwayHash
Fast strong hash functions: SipHash/HighwayHash
...Although not a replacement for collision-resistant digests like SHA-2/3, it strikes a pragmatic balance of speed, simplicity, and resistance to common abuse patterns seen in production backends. -
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CityHash
Automatically exported from Google code CityHash
...CityHash has been rigorously tested using tools like SMHasher to ensure high-quality mixing and collision resistance across a wide range of inputs. Its speed and portability have made it a popular choice for developers needing dependable, lightweight hash functions. -
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MACE
Deep learning inference framework optimized for mobile platforms
Mobile AI Compute Engine (or MACE for short) is a deep learning inference framework optimized for mobile heterogeneous computing on Android, iOS, Linux and Windows devices. Runtime is optimized with NEON, OpenCL and Hexagon, and Winograd algorithm is introduced to speed up convolution operations. The initialization is also optimized to be faster. Chip-dependent power options like big.LITTLE scheduling, Adreno GPU hints are included as advanced APIs. UI responsiveness guarantee is sometimes... -
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libfacedetection
Library for face detection in images
...What you need is just a C++ compiler. You can compile the source code under Windows, Linux, ARM and any platform with a C++ compiler. SIMD instructions are used to speed up the detection. You can enable AVX2 if you use Intel CPU or NEON for ARM. The model file has also been provided in directory ./models/. The file examples/detect-image.cpp and examples/detect-camera.cpp show how to use the library. The library was trained by libfacedetection.train. You can copy the files in directory src/ into your project, and compile them as the other files in your project. ... -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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progrep
Utility to show live progress, status & stats for running simulations
progrep is a command-line tool (Linux) to show live progress report, status & stats of a running simulation or compute job that executes a given number of iterations. It shows % completed, time remaining, time elapsed, number of threads, MPI_Rank(if any), CPU usage & speed (FPS). The FPS measures may be used in benchmarking, e.g. while optimizing HPC algorithms for performance. progrep supports both single-threaded and parallel (multicore/multinode - e.g. OpenMP/MPI) jobs. progrep can also report for jobs running on remote hosts, e.g. jobs running on Linux Clusters. progrep works in client-server model. ... -
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Zopfli
Zopfli Compression Algorithm is a compression library
...The codebase includes both a reusable library and ready-to-use CLI tools for bulk optimization in build pipelines. It is frequently used offline—e.g., as a final step in release builds—because decode speed remains normal while files get smaller. -
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Caffe Framework
Caffe, a fast open framework for deep learning
Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR) and by community contributors. Yangqing Jia created the project during his PhD at UC Berkeley. Caffe is released under the BSD 2-Clause license. Expressive architecture encourages application and innovation. Models and optimization are defined by configuration without hard-coding. -
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CUSHAW2: Parallel Gapped Read Alignment
One of the leading short-/long-read aligner to large genomes
...The performance evaluation, by aligning simulated and real datasets to the human genome, shows that CUSHAW2 is consistently among the highest-ranked aligners in terms of alignment quality for both single-end and paired-end alignment, while demonstrating highly competitive speed. Furthermore, our aligner shows good parallel scalability with respect to the number of CPU threads. -
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This project is a Neural Network Training Library implemented on CUDA. It's compatible with the most used libraries but allows to exploit the full power of NVIDIA graphic cards. Experimental results show speed ups over 100 times against CPU libraries
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