Search Results for "compute" - Page 3
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Showing 115 open source projects for "compute"
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future
R package: future: Unified Parallel and Distributed Processing in R
...It allows R expressions to be scheduled for future evaluation, with the result retrieved later, in a way decoupled from the specific backend used. This lets code be written in a way that works with sequential execution, multicore, multisession, cluster, or remote compute backends, without changing the high-level code. It handles automatic exporting of needed global variables/functions, managing of packages, RNG, etc. Automatic detection and export of global objects and functions needed by future expressions, so the user doesn’t need to manage that manually. Ability to control how futures are resolved. -
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MetricFlow
MetricFlow allows you to define, build, and maintain metrics in code
...It works alongside a data stack—typically built with dbt—and allows you to express metrics as YAML‐based definitions tied to semantic models and dimension tables, rather than embedding logic ad-hoc across many dashboards or scripts. When a user or tool requests a metric (e.g., “monthly revenue by region”), MetricFlow generates optimized, warehouse-specific SQL to compute that metric, handling joins, filters, time grains, offsets, and other complexities under the hood. Because metric definitions live centrally, you avoid duplication across teams and tools, reduce risk of inconsistent numbers, and make it easier to audit and evolve the logic over time. The project emphasizes explainability, performance and portability: you define metrics once and then they can be consumed in BI tools, notebooks, or even AI/agent-driven workflows. -
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libTheSky
Fortran library to compute positions of celestial bodies
libTheSky is a Fortran library to compute positions of celestial bodies (Moon, planets, stars) and events (conjunctions, eclipses). It forms the core of the software that produces the Dutch popular-astronomy website http://hemel.waarnemen.com -
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Win32 and Win64 dll to compute very fast the checksum of strings or files using RSP32 , Adler32 , CRC32 , MD5 , SHA-1 , SHA-256 , MD5_32 , SHA-512, RIPEMD160 and now (march/2024) the fast new checksum ds32, it is in the file rsp32.c Before using our new muti-thread mode read the documentation of the function checksums_multi_thread_v
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Acorn
A simple application deployment framework built on Kubernetes
...The Acornfile is built and packaged up into a single artifact that contains everything needed to deploy your application and can be shared with a link that will allow it's recipients to have a one-click deployment into a sandbox environment. Acorn Sandboxes are free compute environments in the cloud for anyone with a GitHub account to run, test, and develop Acorns. Sandboxes can be used as often as you like to run Acorns for up to 2 hours at a time. Sandbox environments allow for consuming up to 4GB of ram and up to 10GB of storage space. Acorns launched in the sandbox environment can be accessed via a public URL with SSL. ... -
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UniSIMD-assembler
SIMD macro assembler unified for ARM, MIPS, PPC and x86
UniSIMD assembler is a high-level C/C++ macro assembler framework unified across ARM, MIPS, POWER and x86 architectures. It establishes a subset of both BASE and SIMD instruction sets with clearly defined common API, so that application logic can be written and maintained in one place without code replication. The assembler itself isn't a separate tool, but rather a collection of C/C++ header files, which applications need to include directly in order to use. At present, Intel... -
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SolTrack
A free, fast and accurate routine to compute the position of the Sun
SolTrack is a simple, free, fast and accurate C/C++ routine to compute the position of the Sun. The code can be used to track the Sun on low-specs machine, such as a PLC or a microcontroller, and can be used for e.g. (highly) concentrated (photovoltaic) solar power. SolTrack has been developed by Marc van der Sluys, Paul van Kan and Jurgen Reintjes, of the Lectorate of Sustainable Energy at the HAN University of Applied Sciences in Arnhem, The Netherlands. -
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Neural Tangents
Fast and Easy Infinite Neural Networks in Python
...It lets researchers define architectures from familiar building blocks—convolutions, pooling, residual connections, and nonlinearities—and obtain not only the finite network but also the corresponding Gaussian Process (GP) kernel of its infinite-width limit. With a single specification, you can compute NNGP and NTK kernels, perform exact GP inference, and study training dynamics analytically for infinitely wide networks. The library closely mirrors JAX’s stax API while extending it to return a kernel_fn alongside init_fn and apply_fn, enabling drop-in workflows for kernel computation. Kernel evaluation is highly optimized for speed and memory, and computations can be automatically distributed across accelerators with near-linear scaling. -
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Open Food Facts
Native version of Open Food Facts on Android
...Open Food Facts contributors gather information and data on food products from around the world, using mobile apps. Open Food Facts is a database of food products with ingredients, allergens, and nutrition facts… which allow us to compute scores like Nutri-Score, NOVA groups, and Eco-Score. Open Food Facts is a non-profit association of volunteers. 25,000+ contributors like you have added 3M+ products from 150 countries using our Android or iPhone apps to scan barcodes and upload pictures of products and their labels. Data about food is of public interest and has to be open. ... -
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Jadecy
Java lib to compute (code) deps, SCCs, and cycles (Johnson algorithm).
...Since version 1.1, can also compute shortest cycles covering all edges of SCCs. Usage from command line is also possible but with reduced flexibility (cf. JadecyMain class). API entry point for Java code dependencies is the Jadecy class, or DepUnit that makes use of it and is designed for unit tests. API entry point for general graph computations is the net.jadecy.graph package. -
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Veldrid
A low-level, portable graphics library for .NET
Veldrid is a low-level, portable graphics library for .NET, providing a unified API over multiple graphics backends such as Direct3D, Vulkan, OpenGL, and Metal. It enables developers to write high-performance, cross-platform graphics applications without being tied to a specific graphics API. Veldrid is suitable for game development, simulations, and other applications requiring advanced graphics capabilities. -
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FairScale
PyTorch extensions for high performance and large scale training
FairScale is a collection of PyTorch performance and scaling primitives that pioneered many of the ideas now used for large-model training. It introduced Fully Sharded Data Parallel (FSDP) style techniques that shard model parameters, gradients, and optimizer states across ranks to fit bigger models into the same memory budget. The library also provides pipeline parallelism, activation checkpointing, mixed precision, optimizer state sharding (OSS), and auto-wrapping policies that reduce... -
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fog
The Ruby cloud services library
Whether you need compute, dns, storage, or a multitude of other services, fog provides an accessible entry point and facilitates cross-service compatibility. Just getting started working with cloud resources? You are not alone, and having so many complicated options makes it hard to know where to start. fog delivers the knowledge of cloud experts to you, helping you to bootstrap your cloud usage and guiding you as your own expertise develops. -
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Rocket Chip
Rocket Chip Generator
...A diplomacy framework (LazyModules) lets designers wire components with negotiated parameters, enabling reuse and rapid exploration of different cache sizes, port counts, and memory hierarchies. The generator supports custom accelerators through the RoCC interface, allowing domain-specific compute units to be plugged into the pipeline with shared cache and memory semantics. Tooling integrates with FIRRTL, Verilator, and commercial EDA flows, and the ecosystem around Rocket Chip (e.g., Chipyard) adds harnesses, peripherals, and verification infrastructure. -
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SuperEdge
An edge-native container management system for edge computing
SuperEdge is an open-source container management system for edge computing to manage compute resources and container applications in multiple edge regions. These resources and applications, in the current approach, are managed as one single Kubernetes cluster. A native Kubernetes cluster can be easily converted to a SuperEdge cluster. SuperEdge extends the powerful container orchestration and scheduling capabilities of Kubernetes to the edge. -
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Augmentor.jl
A fast image augmentation library in Julia for machine learning
A fast library for increasing the number of training images by applying various transformations. Augmentor is a real-time image augmentation library designed to render the process of artificial dataset enlargement more convenient, less error prone, and easier to reproduce. It offers the user the ability to build a stochastic image-processing pipeline (or simply augmentation pipeline) using image operations as building blocks. In other words, an augmentation pipeline is little more but a... -
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Fairseq
Facebook AI Research Sequence-to-Sequence Toolkit written in Python
...These ideas are encapsulated in the new FullyShardedDataParallel (FSDP) wrapper provided by fairscale. Fairseq can be extended through user-supplied plug-ins. Models define the neural network architecture and encapsulate all of the learnable parameters. Criterions compute the loss function given the model outputs and targets. Tasks store dictionaries and provide helpers for loading/iterating over Datasets, initializing the Model/Criterion and calculating the loss. -
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Singularity
Open source container platform designed to be simple, fast, and secure
Singularity is an open-source container platform designed to be simple, fast, and secure. Many container platforms are available, but Singularity is designed for ease of use on shared systems and in high-performance computing (HPC) environments. -
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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. -
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git-sizer
Compute various size metrics for a Git repository
git-sizer computes various size metrics for a local Git repository, flagging those that might cause you problems or inconvenience. Is the repository too big overall? Ideally, Git repositories should be under 1 GiB, and (without special handling) they start to get unwieldy over 5 GiB. Big repositories take a long time to clone and repack, and take a lot of disk space. Avoid storing generated files (e.g., compiler output, JAR files) in Git. It would be better to regenerate them when necessary,... -
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Terraform Inventory
Terraform State, Ansible dynamic inventory
This is a little Go app that generates a dynamic Ansible inventory from a Terraform state file. It allows one to spawn a bunch of instances with Terraform, then (re-)provision them with Ansible. This library is stable, but I've been neglecting it somewhat on account of no longer using Ansible at work. On OSX, install it with Homebrew. Alternatively, you can download a release suitable for your platform and unzip it. Make sure the terraform-inventory binary is executable, and you're ready to... -
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constructs-go
Golang bindings for constructs
...A construct can represent a single AWS resource, such as an Amazon Simple Storage Service (Amazon S3) bucket, or it can be a higher-level abstraction consisting of multiple AWS related resources. Examples of such components include a worker queue with its associated compute capacity, or a scheduled job with monitoring resources and a dashboard. The AWS CDK includes a collection of constructs called the AWS Construct Library, containing constructs for every AWS service. -
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Amazon SageMaker Examples
Jupyter notebooks that demonstrate how to build models using SageMaker
...If you’re new to SageMaker we recommend starting with more feature-rich SageMaker Studio. It uses the familiar JupyterLab interface and has seamless integration with a variety of deep learning and data science environments and scalable compute resources for training, inference, and other ML operations. Studio offers teams and companies easy on-boarding for their team members, freeing them up from complex systems admin and security processes. Administrators control data access and resource provisioning for their users. Notebook Instances are another option. They have the familiar Jupyter and JuypterLab interfaces that work well for single users, or small teams where users are also administrators. ... -
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Sparse Attention
"Generating Long Sequences with Sparse Transformers" examples
...Though archived, it remains a key reference for efficient transformer research, influencing many later architectures that aim to extend sequence length while reducing compute. -
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PerfKit Benchmarker
PerfKit Benchmarker (PKB) contains a set of benchmarks
...It simplifies the process of running complex benchmarks by providing unified command-line workflows that handle resource provisioning, execution, and result collection. The framework includes a comprehensive set of predefined benchmarks covering areas such as compute, storage, networking, and distributed systems workloads. It is widely used by researchers, engineers, and organizations to evaluate cloud architectures and make informed infrastructure decisions.
