Search Results for "processor cpu"
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Showing 26 open source projects for "processor cpu"
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OSHI
Native Operating System and Hardware Information
OSHI is a free JNA-based (native) Operating System and Hardware Information library for Java. It does not require the installation of any additional native libraries and aims to provide a cross-platform implementation to retrieve system information, such as OS version, processes, memory and CPU usage, disks and partitions, devices, sensors, etc. -
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Telegraf
The plugin-driven server agent for collecting and reporting metrics
Telegraf is an agent for collecting, processing, aggregating, and writing metrics. Design goals are to have a minimal memory footprint with a plugin system so that developers in the community can easily add support for collecting metrics. Telegraf is plugin-driven and has the concept of 4 distinct plugin types. Input Plugins collect metrics from the system, services, or 3rd party APIs. Processor Plugins transform, decorate, and/or filter metrics. Aggregator Plugins create aggregate metrics... -
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fluentbit
Fast and Lightweight Logs and Metrics processor for Linux, BSD, OSX
Fluent Bit is a super-fast, lightweight, and highly scalable logging and metrics processor and forwarder. It is the preferred choice for cloud and containerized environments. A robust, lightweight, and portable architecture for high throughput with low CPU and memory usage from any data source to any destination. Proven across distributed cloud and container environments. Highly available with I/O handlers to store data for disaster recovery. -
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Intel PCM
Intel® Performance Counter Monitor (Intel® PCM)
Intel® Performance Counter Monitor (Intel® PCM) is an application programming interface (API) and a set of tools based on the API to monitor performance and energy metrics of Intel® Core™, Xeon®, Atom™ and Xeon Phi™ processors. PCM works on Linux, Windows, Mac OS X, FreeBSD, DragonFlyBSD and ChromeOS operating systems. -
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Perfetto
Production-grade client-side tracing, profiling, and analysis
...It’s designed around a low-overhead producer/consumer model: instrumented components (“producers”) write binary events into shared memory buffers and a collector (“service”) reliably streams them to storage. The data model spans kernel and userspace, so you can stitch together CPU scheduling, app lifecycles, binder/IPC hops, GPU work, power and thermal signals, file I/O, heap samples, and more into a single coherent timeline. Perfetto’s ecosystem includes a web-based UI that can load multi-GB traces directly in the browser and an offline “trace processor” that exposes the trace as a queryable SQL-like table schema for deep analysis and automation. ... -
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OpenVINO
OpenVINO™ Toolkit repository
...Reduce resource demands and efficiently deploy on a range of Intel® platforms from edge to cloud. This open-source version includes several components: namely Model Optimizer, OpenVINO™ Runtime, Post-Training Optimization Tool, as well as CPU, GPU, MYRIAD, multi device and heterogeneous plugins to accelerate deep learning inferencing on Intel® CPUs and Intel® Processor Graphics. It supports pre-trained models from the Open Model Zoo, along with 100+ open source and public models in popular formats such as TensorFlow, ONNX, PaddlePaddle, MXNet, Caffe, Kaldi. -
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Phalcon
High performance, full-stack PHP framework delivered as a C extension
Its innovative architecture makes Phalcon the fastest PHP framework ever built! Developers do not need to know C to use Phalcon. Its functionality is exposed as PHP classes and interfaces under the Phalcon namespace, ready to be used. Zephir/C extensions are loaded together with PHP one time on the web server's daemon start process. Classes and functions provided by the extension are ready to use for any application. The code is compiled and isn't interpreted because it's already compiled to... -
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Jittor
Jittor is a high-performance deep learning framework
Jittor is a high-performance deep learning framework based on JIT compiling and meta-operators. The whole framework and meta-operators are compiled just in time. A powerful op compiler and tuner are integrated into Jittor. It allowed us to generate high-performance code specialized for your model. Jittor also contains a wealth of high-performance model libraries, including image recognition, detection, segmentation, generation, differentiable rendering, geometric learning, reinforcement... -
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powerMAX
powerMAX is a CPU and GPU burn-in test
powerMAX is a CPU and GPU burn-in tool designed to push your hardware to its absolute thermal and power limits. It helps users uncover stability issues, cooling weaknesses, and power delivery problems by applying maximum, sustained stress to both the processor and graphics card. The utility supports dedicated CPU tests—SSE or AVX—and a demanding GPU 3D rendering test, with the option to run both simultaneously for full-system power load evaluation. -
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Sys6801
Resources for soft-core systems using 6801/6803 like cpu core
...The materials in this project may be useful to others interested in such softcore processor design. -
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LDPartEditor
Create and edit LDraw™ parts quick and easy.
...Minimum System Requirements: - OpenGL 2.1 compatible Graphics Card - Operating System (64-bit): Windows [7 or newer], Mac OS X [>=14], Linux [e.g. Linux Mint >=22] - CPU: Multicore-Processor e.g. Intel Core 2 Duo or AMD Athlon II (>2.0Ghz) - RAM: 4 GB - Video-Memory: 1 GB - Free Disk Space: 150 MB Recommended Requirements: - OpenGL 3.3 compatible Graphics Card - Operating System (64-bit): Windows 7,8,10,11, Mac OS X [>=14], Linux [e.g. Linux Mint >=22] - CPU: Multicore-Processor with 4 cores (or more) - RAM: >4 GB - Video-Memory: >1 GB - Free Disk Space: 512 MB The sourcecode is now hosted on GitHub here: http://nilsschmidt1337.github.io/ldparteditor/ -
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SageMaker TensorFlow Serving Container
A TensorFlow Serving solution for use in SageMaker
...Amazon SageMaker uses Docker containers to run all training jobs and inference endpoints. The Docker images are built from the Dockerfiles in docker/. The Dockerfiles are grouped based on the version of TensorFlow Serving they support. Each supported processor type (e.g. "cpu", "gpu", "ei") has a different Dockerfile in each group. If your are testing locally, building the image is enough. But if you want to your updated image in SageMaker, you need to publish it to an ECR repository in your account. You can also run your container locally in Docker to test different models and input inference requests by hand. -
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FARM
Fast & easy transfer learning for NLP
...With FARM you can build fast proofs-of-concept for tasks like text classification, NER or question answering and transfer them easily into production. Easy fine-tuning of language models to your task and domain language. AMP optimizers (~35% faster) and parallel preprocessing (16 CPU cores => ~16x faster). Modular design of language models and prediction heads. Switch between heads or combine them for multitask learning. Full Compatibility with HuggingFace Transformers' models and model hub. Smooth upgrading to newer language models. Integration of custom datasets via Processor class. Powerful experiment tracking & execution. -
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UDOO Board
UDOO: Android Linux Arduino in a tiny single-board computer
...UDOO merges different computing worlds in one; each world has its strengths and weaknesses, and all of them are useful today in education as well as Do-It-Yourself (DIY) and rapid prototyping endeavours. UDOO is an open hardware, low-cost computer equipped with an ARM i.MX6 Freescale processor for Android and Linux, alongside Arduino DUE’s ARM SAM3X, both CPU integrated on the same board! UDOO’s size is 4.33 inch x 3.35 inch (11 cm x 8.5 cm) and it has low power consumption. -
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...The threads of the user application server are distributed evenly on all CPUs and running independently in parallel. The memory model chosen is NUMA without pagination. During the initialization, the memory is divided proportionally for each processor installed on the system. When a thread needs memory, the memory allocator returns a free block of memory depending on which CPU the thread is running. In the same way, TORO can dedicate resources to specific processor, i.e. a FileSystem. This only CPU then can access to this instance of FileSystem. The scheduler is based on the cooperative threading model, therefore due to this design, TORO can migrate threads between CPUs and send messages between threads without using any lock instruction. ...
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tiedot
A rudimentary implementation of a basic document database in Go
tiedot is a document database engine that uses JSON as document notation; it has a powerful query processor that supports advanced set operations; it can be embedded into your program, or run a stand-alone server using HTTP for an API. It runs on *nix and Windows operating systems. tiedot has fault-tolerant data structures that put your data safety first, while easily scales to 4+ CPU cores. tiedot has very stable performance, even with millions of records! -
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armvd
ARM Visual Disassembler
Created with Flat Assembler + Z macro language. -
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LeopardAssist
Install OS X Leopard on some unsupported Macs.
LeopardAssist assists in the process of install OS X Leopard on machines that did not meet the minimum system requirement of an 867MHz or faster processor. It achieves this by temporarily writing a script into the NVRAM of your G4-based Mac. Executed at startup, this script will temporarily force the system to report that a faster processor is installed, allowing the Leopard installer to successfully complete its minimum system requirements check. This allows Leopard to be installed from... -
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CPU Simulation Utility
A utility to simulate CPU's, only the necessary functionality included
This utility has one input file and no more; the .cpu file. For input to the simulated processor you can use other files, but these are referenced to inside the .cpu file. There is no functionality other than this, to make the utility run fast and decrease the chance of errors. -
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GENIE (GEne-geNe IntEraction)
GPU based Parallel Gene-Gene Interaction Analysis
...However, currently there are no genetic analysis software packages that allow users to fully utilize the computing power of these multi-core devices for genetic interaction analysis for binary traits. Here we present a novel software package GENIE, which utilizes the power of multiple GPU or CPU processor cores to parallelize the interaction analysis. Citation: Chikkagoudar, S., Wang, K., & Li, M. (2011). GENIE: a software package for gene-gene interaction analysis in genetic association studies using multiple GPU or CPU cores. BMC research notes, 4(1), 158. -
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Smart Autorun Cleaner
Smart Autorun Cleaner, Most powerful gadget against autorun-malwares.
...Please carefully read help and you will find how is great Smart Autorun Cleaner. Minimum system requirements: Platforms: Microsoft Windows XP (SP3) or higher (No Linux, Mac) 128 MB of ram memory (SD-DDR) 800 MHZ CPU processor (P3) or higher 80 MB free disk space -
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LBP in multiple platforms
LBP implementation in multiple computing platforms (ARM,GPU, DSP...)
...When selecting a suitable LBP implementation platform, the specific application and its requirements in terms of performance, size, energy efficiency, cost and developing time has to be carefully considered. This is a software toolbox that collects software implementations of the Local Binary Pattern operator in several platforms: - OpenCL for CPU & GPU - OpenCL for GPU (branchless) - C code optimized for ARM - OpenGL ES 2.0 shaders mobile GPUs - C code for TI C64x DSP core (branchless) - C code for TTA processor synthesis If you use the code somewhere, please cite: Bordallo López M., Nieto A., Boutellier J., Hannuksela J., and Silvén O. "Evaluation of real-time LBP computing in multiple architectures," Journal of Real Time Image Processing, 2014 -
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Microtouch: AVR homebrew device with iPhone aspirations. Can you make an iPhone out of an AVR? No. Can you get a surprising amount of functionality out of a humble 8 bit processor and a cheap touch LCD? Yes.
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Procbench is a x86 CPU information tool and CPU benchmark that tests math capabilities of your processor. It will measure the performance of your CPU, FPU and the Cache/Memory subsystem.
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Wavelet Transform Spectrum Analyzer: a tool for analyzing the power spectrum of a signal (typically from a photon-counting device) by using an external board, with a Digital Signal Processor outside the CPU.
