Search Results for "memory management"
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Showing 12 open source projects for "memory management"
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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... -
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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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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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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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Query.jl
Query almost anything in julia
Query is a package for querying julia data sources. It can filter, project, join and group data from any iterable data source, including all the sources supported in IterableTables.jl. One can for example query any of the following data sources: any array, DataFrames, DataStreams (including CSV, Feather, SQLite, ODBC), DataTables, IndexedTables, TimeSeries, Temporal, TypedTables and DifferentialEquations (any DESolution). The package currently provides working implementations for in-memory... -
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IndexedTables.jl
Flexible tables with ordered indices
IndexedTables provides tabular data structures where some of the columns form a sorted index. It provides the backend to JuliaDB, but can be used on its own for efficient in-memory data processing and analytics.
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