Search Results for "cpu scheduling"
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Showing 33 open source projects for "cpu scheduling"
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TileLang
Domain-specific language designed to streamline the development
TileLang is a Pythonic domain-specific language for writing high-performance GPU, CPU, and accelerator kernels without dropping fully into low-level vendor code. Its compiler infrastructure is built on TVM and targets workloads such as GEMM, dequantization, FlashAttention, and linear attention. Developers can work at different abstraction levels while still controlling memory layout, scheduling, pipelines, and hardware-specific behavior. -
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AGI (Android GPU Inspector)
Android GPU Inspector
...Beyond per-frame analysis, AGI correlates GPU activity with CPU threads and system events to diagnose contention, scheduling issues, and thermal or power constraints. The tool is designed for reproducible performance investigations, making it easier to iterate, compare captures, and ship smoother, more efficient Android graphics experiences. -
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JobRunr
An extremely easy way to perform background processing in Java
Incredibly easy way to perform fire-and-forget, delayed, scheduled and recurring jobs inside Java applications using only Java 8 lambda's. CPU and I/O intensive, long-running and short-running jobs are supported. Persistent storage is done via either RDBMS (e.g. Postgres, MariaDB/MySQL, Oracle, SQL Server, DB2 and SQLite) or NoSQL (ElasticSearch, MongoDB and Redis). JobRunr provides a unified programming model to handle background tasks in a reliable way and runs them on shared hosting,... -
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Polyaxon
MLOps tools for managing & orchestrating the ML LifeCycle
Polyaxon is an open-source machine learning operations (MLOps) platform built to help individuals, teams, and organizations develop, train, orchestrate, and monitor machine learning and deep learning workflows at scale with reproducibility and automation as core principles. It provides a unified solution for tracking experiments, managing datasets, scheduling jobs, and comparing results across runs, which greatly improves productivity and collaboration in data science teams. Polyaxon integrates seamlessly with Kubernetes and container orchestration so that workloads can be scheduled efficiently, GPU and CPU resources are shared, and distributed training across multiple nodes is straightforward. ... -
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ANTIRTOS
Function pointers queues classes library for Arduino
ANTIRTOS is a compact real-time operating system (RTOS) written in ANSI C for microcontrollers and embedded systems. It is designed to be simple, portable, and efficient, making it ideal for resource-constrained environments such as IoT devices, robotics, and educational hardware projects. ANTIRTOS features a basic scheduler, support for cooperative and preemptive multitasking, and offers portability across different CPU architectures with minimal changes. Its simplicity and clarity make it... -
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Bend
A massively parallel, high-level programming language
Bend is an interactive programming environment (REPL) built on top of the Kotlin language, designed to allow users to explore, experiment, and learn Kotlin in a live, feedback-driven manner. The tool lets you define variables, functions, or values at the prompt and iteratively refine them—immediately seeing output and types—while preserving state across commands. It emphasizes discoverability and experimentation: users can inspect functions, call them on sample inputs, and evolve logic... -
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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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Yazi
Blazing fast terminal file manager written in Rust, based on async I/O
Blazing fast terminal file manager written in Rust, based on async I/O. All I/O operations are asynchronous, CPU tasks are spread across multiple threads, making the most of available resources. Provides real-time progress updates, task cancellation, and internal task priority assignments. Also integrated with Überzug++, covering almost all terminals. Combined with the pre-caching mechanism, greatly accelerates image and normal file loading. Provides real-time progress updates, task... -
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observer_cli
Visualize Erlang/Elixir Nodes On The Command Line
...Total scheduler utilization will equal 1.0 when all schedulers have been active all the time between the two refresh intervals. The result being that there is a decent chunk of CPU usage that would be mostly free for scheduling actual Erlang work (assuming the schedulers are busy waiting more than trying to select tasks to run). -
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UIforETW
User interface for recording and managing ETW traces
UIforETW is a Windows performance tracing companion that wraps the Event Tracing for Windows (ETW) toolchain in an approachable GUI. It standardizes trace collection profiles, launches WPR/xperf with the right providers, and organizes the resulting .etl files for repeatable investigations. The tool streamlines the entire loop—record, annotate, open in WPA/XperfView—so engineers can focus on finding scheduling stalls, I/O bottlenecks, GC pauses, or GPU hitches instead of memorizing... -
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cocoyaxi
A go-style coroutine library in C++11 and more
...It contains a series of high-quality base components, such as a go-style coroutine, coroutine-based network programming framework, command line, and config file parser, high-performance log library, unit testing framework, JSON library, etc. co has implemented a go-style coroutine, which has the following features. Multi-thread scheduling, the default number of threads is the number of system CPU cores. Shared stack, coroutines in the same thread share several stacks (the default size is 1MB), and the memory usage is low. Simple test on Linux shows that 10 millions of coroutines only take 2.8G of memory (for reference only). There is a flat relationship between coroutines, and new coroutines can be created from anywhere (including in coroutines). ... -
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Ultra Performance Tuning
Debian-based distribution for deterministic, RT and low-latency works
Ultra Performance Tuning is a Debian-based Linux distribution for deterministic, real-time (Real-Time) and low-latency workloads. At the core of the distribution is a fully preemptive PREEMPT_RT patched kernel, ensuring strict microsecond-level scheduling determinism. By employing CPU scheduler tuning, hardware interrupt (IRQ) affinity steering, and optimized virtual memory management parameters, Ultra Performance Tuning minimizes system and input latencies. The distribution features a lightweight, custom-tailored XFCE4 desktop environment that reduces system overhead. Ultra Performance Tuning is designed specifically for environments where timing precision and operational predictability are critical, such as professional digital audio workstations (DAW/Studio), high-precision robotics, industrial automation, and real-time data streaming. ... -
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rt-analyzer
Linux real-time configuration analyzer with scoring and fixes
rt-analyzer reads /proc and /sys and applies 61 expert rules across 7 categories to produce a weighted 0–100 score for Linux real-time (PREEMPT_RT) systems, with prioritized, copy-pasteable remediation. It catches the failures that look fine but aren't: isolcpus set without nohz_full, C-state exit latency that silently bounds worst-case wakeup time, and effective_affinity quietly overriding the smp_affinity you requested. /proc/interrupts is sampled twice to separate interrupts merely... -
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Asset Management System for Intranets
The Free & Popular Snipe-IT Web Software in a Complete Virtual Machine
This VM is created for 2 reasons: 1. Very little initial setup work required to make an Asset Management System live, within minutes. 2. This system should keep running for Years, without requiring Updates / Breakages. If you are new to Virtual Machines, then please watch the Video below ( taken from my other project. just replace td with am wherever mentioned ) After starting this VM, please login to its administration panel with: Website Address: https://am.local/ ( Accept Any... -
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Capacity Planning & Management Software
Free & Open Capacity Planning Software in a Complete Virtual Machine
Every Profession has some sort of Resource to manage, be it materials, money, physical or logical resources & with it some vital queries to be answered: At what Rate is my Resource getting consumed? How Long will current stock last? What will be near future requirement? In Just Two Simple steps of Defining Your Resource & Actively Tracking its Consumption, you will be in total control of your Resource, with this Software. After starting this VM, from any PC on your Local Network,... -
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BPYTOP
Linux/OSX/FreeBSD resource monitor
BPYTOP is a feature-rich, terminal-based resource monitor written in Python 3 that provides a highly visual overview of system performance. It displays real-time usage and statistics for CPU, memory, disks, network, and processes, with colorful graphs and widgets that update at configurable intervals. Users can drill into a process list, sort by various metrics, view tree hierarchies, and quickly spot heavy resource consumers. The tool is highly configurable through both an in-app options... -
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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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Tdarr V2
Distributed transcode automation using FFmpeg/HandBrake
Tdarr is a distributed media transcoding and automation system designed to manage and optimize large audio and video libraries across multiple machines. It operates through a central server and multiple worker nodes that process files in parallel, leveraging both CPU and GPU resources for efficient transcoding. The system uses FFmpeg and HandBrake to convert media files according to user-defined rules, such as codec standardization, bitrate optimization, or format compatibility. Tdarr also includes advanced features like health checks, plugin-based processing pipelines, and automated scheduling to maintain libraries continuously. ... -
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Bashtop
Linux/OSX/FreeBSD resource monitor
Bashtop is a resource monitor written primarily in Bash with some Python support, displaying real-time usage of system resources: CPU, memory, disks, network, and processes. It provides an interactive, visually pleasant UI in the terminal (graphs, colors, etc.), process list filtering, sorting, and controls to send signals to processes. Bashtop supports themes, configurable update intervals, and has optional dependencies to augment its functionality (e.g. for temperature, extended stats). It... -
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XFreq
a UNIX GUI to monitor Core i7 & Core 2 processors
XFreq provides a GUI to monitor the Processor frequencies (including Turbo Boost), the temperatures, C-States; and to alter the operational features of the Intel Core i7 processor. XFreq is also programmed for Core 2 and other Intel 64-bits architectures. -
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task
Efficient thread pool task scheduling framework
...You don't have to worry about the task-running-overtime, because the task framework has been implemented to monitor the timeout task, once the task is running out of time, the task monitor will remove it from the thread pool, but also to help you automatically end the timeout task. 3. The task scheduling framework has excellent performance, if the task queue is empty, the framework will automatically enter into the waiting state until the task queue has a task to add. This will greatly reduce the consumption of CPU. 4.The framework is very easy to use, for example: TaskQueue queue = new TaskQueue(); taskAssigner = new TaskAssigner(queue, 10); taskAssigner.start(); queue.add(task); -
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neuranep
Neural Network Engineering Platform
...NeuraNEP handles microthread scheduling, synchronization, distribution and communication. This project is a fork of SpikeOS (sourceforge.net/projects/spikeos) and represents a major update to that code base, including a scripting interface and low-level rewrite of several components. SpikeOS was oriented towards computational modeling. NeuraNEP is oriented toward neural network research. -
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...To fill this gap in tools for evaluation and modeling of Cloud environments and applica- tions, we propose CloudSched. CloudSched can help developers identify and explore appropriate solutions considering different resource scheduling algorithms. Unlike traditional scheduling algorithms considering only one factor such as CPU, which can cause hotspots or bottlenecks in many cases, CloudSched treats multi-dimensional resource such as CPU, memoryt
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A parallel-programming framework for concurrently running large numbers of small autonomous jobs, or microthreads, across multiple cores in a CPU or CPUs in a cluster. Each microthread is conceptually similar to a task in Ada and it is much lighter weight than an operating system thread. SpikeOS was designed to handle millions of microthreads, for example in a neural network hosting millions of spiking model neurons. SpikeOS handles microthread scheduling, synchronization, distribution and communication. *** This project has been forked. ...
