Search Results for "memory"
Sort By:
Showing 194 open source projects for "memory"
View related business solutions-
Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
-
Paessler: Easy to Use With Enterprise Power. Free TrialYou shouldn't have to choose between a monitoring tool that's easy to use and one that's powerful enough for a complex environment. PRTG's low-code interface lets any admin build dashboards, set alerts and monitor devices without scripting, while custom sensors and full API access are there when your team needs deeper control. One platform, no compromise. Download a free 30-day trial now.
-
1
TaskExplorer
Powerful system task manager
...It includes advanced panels: a Thread Panel with stack traces (helpful for diagnosing deadlocks or hangs), a Memory Panel that lets you view or even edit process memory (with search capabilities), a Socket Panel showing open network connections, data rates, pseudo-UDP connections (via ETW), and a Modules Panel listing all loaded DLLs and memory-mapped files. -
2
OnlineStats.jl
Single-pass algorithms for statistics
OnlineStats does statistics and data visualization for big/streaming data via online algorithms. High-performance single-pass algorithms for statistics and data viz. Updated one observation at a time. Algorithms use O(1) memory. Algorithms use O(1) memory. -
3
MemProcFS Analyzer
Automated Forensic Analysis of Windows Memory Dumps for DFIR
MemProcFS-Analyzer is a forensic analysis toolset that builds on the MemProcFS virtual filesystem to make volatile memory artefacts easier to browse and interpret. By exposing process memory, kernel objects, and derived artifacts as regular files, the framework lets analysts use familiar filesystem operations and standard tools (editors, grep, diff) to explore memory snapshots. The Analyzer layer adds higher-level parsing and extraction routines—for example, carving strings, locating injected modules, enumerating handles, or reconstructing network sockets—so investigators can go from raw memory to actionable evidence more quickly. ... -
4
FileTrees.jl
Parallel file processing made easy
...When computing lazy trees, these values are held in distributed memory and operated on in parallel. -
Build Data Resilience - Take the Assessment TodayIs your recovery strategy as strong as you think? Take this quick self-assessment to check your recovery readiness and gain tailored insights. In only 2 minutes, you'll learn where you fall on the recovery readiness scale.
-
5
Windows Calculator
A simple yet powerful calculator that ships with Windows
Calculator is the open source version of the Microsoft Windows Calculator application. It is a modern Windows app written in C++ and C#, offering standard, scientific, and programmer modes, unit/currency conversion, date calculations, and history/memory operations. It ships preinstalled with Windows and is actively developed on GitHub. Standard Calculator functionality, which offers basic operations and evaluates commands immediately as they are entered. Scientific Calculator functionality, which offers expanded operations and evaluates commands using the order of operations. Programmer Calculator functionality, which offers common mathematical operations for developers, including conversion between common bases. -
6
Coverage.jl
Take Julia code coverage and memory allocation results, do useful thin
...This is useful for measuring how much of your code base your tests actually test, and can reveal the parts of your code that are not tested and might be hiding a bug. You can use Coverage.jl to summarize the results of this tracking or to send them to a service like Coveralls.io or Codecov.io. Julia can track how much memory is allocated by each line of your code. This can reveal problems like type instability, or operations that you might have thought were cheap (in terms of memory allocated) but aren't (i.e. accidental copying). -
7
beszel
Lightweight server monitoring hub with historical data, docker stats
A lightweight server resource monitoring hub with historical data, docker stats, and alerts. -
8
DataFrames.jl
In-memory tabular data in Julia
DataFrames.jl is a powerful Julia package for working with in-memory tabular data. It provides a familiar, flexible, and efficient interface for handling datasets, making it easy to load, manipulate, join, and analyze structured data. With syntax inspired by data frames in R and pandas in Python, it offers intuitive tools while taking advantage of Julia’s speed and type system. The package is actively maintained by the JuliaData community, with contributions from over 200 developers worldwide. ... -
9
BTOP++
BTOP++ for Windows
BTOP++ is the Windows port of btop++, bringing the same modern, graphical terminal resource monitor experience to Windows systems. It shows detailed and live statistics for processor usage, memory consumption, disk activity, network traffic, and both processes and services, all in an interactive terminal UI. The project is implemented as a native Windows build of the C++ btop++ continuation of bashtop and bpytop, optimized for Windows consoles. It relies on ANSI escape sequences for its rich graphical interface; as a result, it needs at least Windows 10 version 1607 for proper support in the standard CMD terminal or a custom terminal emulator with ANSI support. ... -
Build Securely on AWS with Proven FrameworksMoving to the cloud brings new challenges. How can you manage a larger attack surface while ensuring great network performance? Turn to Fortinet’s Tested Reference Architectures, blueprints for designing and securing cloud environments built by cybersecurity experts. Learn more and explore use cases in this white paper.
-
10
Zenith
Sort of like top or htop but with zoom-able charts, CPU, GPU
In terminal graphical metrics for your *nix system written in Rust. The make file provides for building fully static versions on Linux against the musl C library. It requires musl-gcc to be installed on the system. Install "musl-tools" package on debian/ubuntu derivatives, "musl-gcc" on fedora and equivalent on other distributions from their standard repos. If one needs to build with NVIDIA support in a virtual environment, then it requires some more setup since typically the VM software is... -
11
cuDF
GPU DataFrame Library
...The RAPIDS suite of open-source software libraries aims to enable the execution of end-to-end data science and analytics pipelines entirely on GPUs. It relies on NVIDIA® CUDA® primitives for low-level compute optimization but exposing that GPU parallelism and high-bandwidth memory speed through user-friendly Python interfaces. -
12
Space Radar Electron
Disk And Memory Space Visualization App built with Electron & d3.js
Space Radar Electron is an application that offers an interactive and comprehensive visualization of disk space and memory usage of your computer. Built with Electron & d3.js, it currently offers visualizations in the form of Sunburst, Treemap and Flamegraph charts. As it scans the contents of your disk, it produces a preview visualization so you can already see what's been scanned. It allows for drilldown of directories, breadcrumbs and navigation. -
13
LinearOperators.jl
Linear Operators for Julia
Operators behave like matrices (with some exceptions - see below) but are defined by their effect when applied to a vector. They can be transposed, conjugated, or combined with other operators cheaply. The costly operation is deferred until multiplied with a vector. -
14
Explorer
Series (one-dimensional) and dataframes (two-dimensional)
Explorer brings series (one-dimensional) and data frames (two-dimensional) to Elixir for fast data exploration. -
15
PartitionedArrays.jl
Vectors and sparse matrices partitioned into pieces
This package provides distributed (a.k.a. partitioned) vectors and sparse matrices in Julia. See the documentation for further details. -
16
EzXML.jl
XML/HTML handling tools for primates
EzXML.jl is a package to handle XML/HTML documents for primates. This package depends on libxml2, which will be automatically installed as an artifact via XML2_jll.jl if you use Julia 1.3 or later. Currently, Windows, Linux, macOS, and FreeBSD are now supported. -
17
claude-reflect
A self-learning system for Claude Code that captures corrections
...It watches what you correct Claude about — such as preferring a particular model, style, or workflow — and automatically queues those learnings, then lets you review and sync them back into configuration files like CLAUDE.md and agent definitions so Claude remembers them across sessions. Over time, this creates a personalized memory that helps the AI align more closely with your conventions, avoiding repeated misunderstandings and reducing friction in long-running or recurring tasks. In addition to capturing corrections, claude-reflect analyzes session history to identify repeated patterns and propose reusable commands or workflows, which can be converted into skills that accelerate productivity. -
18
dashdot
A simple, modern server dashboard
dashdot is a modern server dashboard primarily used by smaller private servers, offering real-time monitoring of system metrics through a sleek and customizable web interface. -
19
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. Granular management of data parsing and routing. Filtering and enrichment to optimize security and minimize cost. The lightweight, asynchronous design optimizes resource usage: CPU, memory, disk I/O, network. ... -
20
huey
A little task queue for python
...It gives developers a clean API for running background jobs outside the main request or execution flow. The project supports several storage backends, including Redis, Valkey, Redict, SQLite, the file system, and in-memory storage. It can execute tasks with processes, threads, or greenlets, which makes it adaptable to different workloads. Huey also supports scheduled tasks, recurring tasks, retries, task priorities, result storage, expiration, locking, rate limits, timeouts, pipelines, groups, and chords. Overall, it is a practical option for Python teams that want background processing without the complexity of a heavier distributed task system. -
21
Bumper.jl
Bring Your Own Stack
Bumper.jl is a package that aims to make working with bump allocators (also known as arena allocators) easier and safer. You can dynamically allocate memory to these bump allocators, and reset them at the end of a code block, just like Julia's stack. Allocating to a bump allocator with Bumper.jl can be just as efficient as stack allocation. Bumper.jl is still a young package, and may have bugs. Let me know if you find any. -
22
InMemoryDatasets.jl
Multithreaded package for working with tabular data in Julia
InMemoryDatasets.jl is a multithreaded package for data manipulation and is designed for Julia 1.6+ (64-bit OS). The core computation engine of the package is a set of customized algorithms developed specifically for columnar tables. -
23
Caddy
Powerful, enterprise-ready, open source web server w/ automatic HTTPS
Caddy is a powerful, extensible, enterprise-ready server platform that uses TLS by default. Everything you would require in your infrastructure, from TLS certificate renewals and OCSP stapling, to reverse proxying and ingress, Caddy simplifies it all. Its modular architecture lets you do more with just a single static binary that compiles for any platform. Caddy is the only web server that uses HTTPS automatically and by default. It automatically renews TLS certificates, staples OCSP... -
24
SDGym
Benchmarking synthetic data generation methods
...Choose from any of the SDV synthesizers and baselines. Or write your own custom machine learning model. In addition to performance and memory usage, you can also measure synthetic data quality and privacy through a variety of metrics. Install SDGym using pip or conda. We recommend using a virtual environment to avoid conflicts with other software on your device. -
25
GridDB
GridDB is a next-generation open source database
...Multi-model architecture capable of supporting various data stores with time-series data-oriented and pluggable data stores for efficient real-time processing and management of huge amounts of time-series data at high frequency. Various architectural innovations, such as in-memory orientation with "memory as the main unit and disk as the secondary unit" and event-driven design with minimal overhead, have been incorporated to achieve processing capabilities that can handle petabyte-scale applications.
