Search Results for "memory management"
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Showing 81 open source projects for "memory management"
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Train ML Models With SQL You Already KnowBuild and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.
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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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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... -
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beszel
Lightweight server monitoring hub with historical data, docker stats
A lightweight server resource monitoring hub with historical data, docker stats, and alerts. -
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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. -
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Coverage.jl
Take Julia code coverage and memory allocation results, do useful thin
Julia can track how many times, if any, each line of your code is run. 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... -
Ship Agents FasterGemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
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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. -
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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... -
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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... -
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FileTrees.jl
Parallel file processing made easy
Easy everyday parallelism with a file tree abstraction. Read a directory structure as a Julia data structure, (lazy-)load the files, apply map and reduce operations on the data while not exceeding available memory if possible. Make up a file tree in memory, create some data to go with each file (in parallel), write the tree to disk (in parallel). FileTrees is a set of tools to lazy-load, process and save file trees. Built-in parallelism allows you to max out all threads and processes that... -
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cuDF
GPU DataFrame Library
Built based on the Apache Arrow columnar memory format, cuDF is a GPU DataFrame library for loading, joining, aggregating, filtering, and otherwise manipulating data. cuDF provides a pandas-like API that will be familiar to data engineers & data scientists, so they can use it to easily accelerate their workflows without going into the details of CUDA programming. For additional examples, browse our complete API documentation, or check out our more detailed notebooks. cuDF can be installed... -
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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. Space Radar works fast, and is... -
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Explorer
Series (one-dimensional) and dataframes (two-dimensional)
Explorer brings series (one-dimensional) and data frames (two-dimensional) to Elixir for fast data exploration. -
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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. 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. ... -
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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. -
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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. -
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InvertibleNetworks.jl
A Julia framework for invertible neural networks
Building blocks for invertible neural networks in the Julia programming language. -
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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. -
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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. -
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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. -
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Redisson
Valkey & Redis Java client. Real-Time Data Platform
Redisson is a Java client library for Redis that offers distributed data structures, services, and frameworks to build scalable and reliable applications. It simplifies Redis usage by providing in-memory Java objects like maps, sets, locks, queues, and semaphores that are backed by Redis. Redisson supports advanced features like distributed locking, asynchronous APIs, and integrates with frameworks like Spring and Quarkus for reactive and cloud-native development. -
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Dask
Parallel computing with task scheduling
Dask is a Python library for parallel and distributed computing, designed to scale analytics workloads from single machines to large clusters. It integrates with familiar tools like NumPy, Pandas, and scikit-learn while enabling execution across cores or nodes with minimal code changes. Dask excels at handling large datasets that don’t fit into memory and is widely used in data science, machine learning, and big data pipelines. -
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Malli
High-performance data-driven data specification library
Malli is a powerful, data-driven schema library for Clojure and ClojureScript, offering rich support for specification, validation, parsing, error reporting, and generative testing. Designed for performance, Malli leverages efficient runtime representations and code generation, seamlessly integrating with Clojure’s data-oriented architecture. It supports function schemas, JSON transformation, and OpenAPI generation for strong API contracts. -
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POCO
Cross-platform C++ libraries for building network applications
The POCO C++ Libraries are powerful cross-platform C++ libraries for building network- and internet-based applications that run on desktop, server, mobile, IoT, and embedded systems. Whether building automation systems, industrial automation, IoT platforms, air traffic management systems, enterprise IT application and infrastructure management, security and network analytics, automotive infotainment and telematics, financial or healthcare, C++ developers have been trusting the POCO C++... -
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SDGym
Benchmarking synthetic data generation methods
The Synthetic Data Gym (SDGym) is a benchmarking framework for modeling and generating synthetic data. Measure performance and memory usage across different synthetic data modeling techniques – classical statistics, deep learning and more! The SDGym library integrates with the Synthetic Data Vault ecosystem. You can use any of its synthesizers, datasets or metrics for benchmarking. You also customize the process to include your own work. Select any of the publicly available datasets from the... -
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collapse
Advanced and Fast Data Transformation in R
collapse is a high-performance R package designed for fast and efficient data transformation, aggregation, reshaping, and statistical computation. Built to offer a more performant alternative to dplyr and data.table, it is particularly well-suited for large datasets and econometric applications. It operates on base R data structures like data frames and vectors and uses highly optimized C++ code under the hood to deliver significant speed improvements. collapse also includes tools for... -
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Checkmate
Checkmate is an open-source, self-hosted tool
Checkmate is an open-source, self-hosted infrastructure monitoring platform that provides real-time visibility into server health, uptime, response times, and incident activity through a modern web interface. The application continuously checks whether websites and services are accessible and performing optimally, generating alerts and reports when availability or performance degrades. It supports detailed infrastructure monitoring through an optional agent called Capture, which collects...
