Search Results for "compute"
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42 projects for "compute" with 2 filters applied:
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Add Two Lines of Code. Get Full APM.Works out of the box for Rails, Django, Express, Phoenix, and more. Monitoring exceptions and performance in no time.
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Gemini 3 and 200+ AI Models on One PlatformBuild, govern, and optimize agents and models with Gemini Enterprise Agent Platform.
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Bacalhau
Community-driven, simple, yet powerful framework
Bacalhau is a decentralized compute platform for running jobs on data stored across distributed networks, like IPFS or Filecoin, without moving the data to centralized cloud environments. It allows developers to run containerized workloads close to where the data lives, reducing latency, cost, and privacy risks. Bacalhau supports various runtime environments and is designed to make decentralized data processing as accessible as traditional cloud computing. -
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Hono
Web framework built on Web Standards
Hono is a small, simple, and ultrafast web framework built on Web Standards. It works on any JavaScript runtime: Cloudflare Workers, Fastly Compute, Deno, Bun, Vercel, AWS Lambda, Lambda@Edge, and Node.js. The router RegExpRouter is really fast. The hono/tiny preset is under 14kB. Using only Web Standard APIs. Works on Cloudflare, Fastly, Deno, Bun, AWS, or Node.js. The same code runs on all platforms. Hono has built-in middleware, custom middleware, third-party middleware, and helpers. ... -
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VulkanSceneGraph
Vulkan & C++17 based Scene Graph Project
VulkanSceneGraph (VSG), is a modern, cross-platform, high-performance scene graph library built upon Vulkan graphics/compute API. The software is written in C++17 and follows the CppCoreGuidelines and FOSS Best Practices. The source code is published under the MIT License, with the exception of vulkan.h, used for Vulkan extensions, which is under Apache License 2.0. This repository contains C++ headers and source and CMake build scripts to build the libvsg library. -
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Google Cloud's buildpacks
Builders and buildpacks designed to run on Google Cloud's container
...It is built around full compatibility with the Cloud Native Buildpacks specification, which means it can participate in standardized build workflows while still being tailored to Google Cloud environments. The project supports major runtime destinations such as Cloud Run, GKE, Anthos, and Compute Engine with Container-Optimized OS, and it also powers build flows for App Engine and Cloud Functions. One of its strengths is that it does not only provide buildpacks themselves, but also builder images that work with tools such as pack, kpack, Tekton, and Skaffold, giving developers flexibility in how they incorporate it into CI/CD systems. -
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Argo Workflows
Workflow engine for Kubernetes
...Define workflows where each step in the workflow is a container. Model multi-step workflows as a sequence of tasks or capture the dependencies between tasks using a directed acyclic graph (DAG). Easily run compute intensive jobs for machine learning or data processing in a fraction of the time using Argo Workflows on Kubernetes. Run CI/CD pipelines natively on Kubernetes without configuring complex software development products. Argo Workflows is the most popular workflow execution engine for Kubernetes. It can run 1000s of workflows a day, each with 1000s of concurrent tasks. ... -
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FlashMLA
FlashMLA: Efficient Multi-head Latent Attention Kernels
...The library supports both BF16 and FP16 data types, and includes a paged KV cache implementation with a block size of 64 to efficiently manage memory during decoding. On very compute-bound settings, it can reach up to ~660 TFLOPS on H800 SXM5 hardware, while in memory-bound configurations it can push memory throughput to ~3000 GB/s. The team regularly updates it with performance improvements; for example, a 2025 update claims 5 % to 15 % gains on compute-bound workloads while maintaining API compatibility. -
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Edit Banana
Edit Banana: A framework for converting statistical figures
...The tool focuses on accessibility, giving hobbyists, content creators, and small teams a way to produce polished visuals without downloading heavyweight software or managing local compute resources. Through AI-driven features like content-aware fill and stylistic adjustments, users can modify or replace regions of an image with contextually relevant content that blends seamlessly with the rest of the composition. -
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rspirv
Rust implementation of SPIR-V module processing functionalities
rspirv is a Rust-based parser, builder, and disassembler for SPIR-V, the intermediate binary format used in Vulkan and OpenCL for shaders and compute kernels. It’s part of the gfx-rs ecosystem, a suite of graphics tools aiming to provide cross-platform rendering capabilities in Rust. rspirv enables manipulation and inspection of SPIR-V modules, which is useful in shader compilers, graphics drivers, and development tools for low-level GPU programming. The library strictly follows the SPIR-V specification and is used in projects that need to generate, analyze, or modify shaders dynamically. -
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Google Cloud Platform Go Samples
Sample apps and code written for Google Cloud
Google Cloud Platform Go Samples repository is a comprehensive collection of Go-based code examples that demonstrate how to build applications and services using Google Cloud Platform. It provides developers with practical implementations that cover a wide spectrum of cloud functionalities, including storage, compute, networking, and machine learning services. Each sample is designed to be easily reusable, allowing developers to copy code directly into their own projects as a starting point for development. The repository includes both simple quickstart examples and more advanced application patterns, often accompanied by documentation guides that explain how to deploy and run them in different environments. ... -
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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bitnet.cpp
Official inference framework for 1-bit LLMs
...At its core is bitnet.cpp, a highly optimized C++ backend that supports fast, low-memory inference on both CPUs and GPUs, enabling models such as BitNet b1.58 to run without requiring enormous compute infrastructure. The project’s focus on extreme quantization dramatically reduces memory footprint and energy consumption compared with traditional 16-bit or 32-bit LLMs, making it practical to deploy advanced language understanding and generation models on everyday machines. BitNet is built to scale across architectures, with configurable kernels and tiling strategies that adapt to different hardware, and it supports large models with impressive throughput even on modest resources. -
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Micronaut Data
Ahead of Time Data Repositories
Micronaut Data is a database access toolkit that uses Ahead of Time (AoT) compilation to pre-compute queries for repository interfaces that are then executed by a thin, lightweight runtime layer. Both GORM and Spring Data maintain a runtime meta-model that uses reflection to model relationships between entities. This model consumes significant memory and memory requirements grow as your application size grows. The problem is worse when combined with Hibernate which maintains its own meta-model as you end up with duplicate meta-models. ... -
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GenAI Processors
GenAI Processors is a lightweight Python library
...The library offers built-in processors for classic turn-based Gemini calls as well as Live API streaming, so you can mix “batch” and real-time interactions in the same graph. It leans on Python’s asyncio to coordinate concurrency, handle network I/O, and juggle background compute threads without blocking. -
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ngraph.path
Path finding in a graph
ngraph.path is a JavaScript library that implements efficient pathfinding algorithms for graphs, primarily designed to compute shortest paths in weighted or unweighted networks. It provides a clean API for constructing graph models, assigning weights to edges, and querying for optimal routes between nodes, making it useful for routing, games, maps, and network optimization. The library includes several algorithm implementations such as A*, Dijkstra’s, and breadth-first search, each suited to different types of graph structure and performance needs. ... -
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sebastian/diff
Diff implementation
diff is a standalone PHP library for computing and rendering textual differences, widely used to display “expected vs actual” output in testing tools. It supports line-based and character/word-granular comparisons so you can get both coarse and fine-grained views of changes. Output builders can produce formats like unified or context diffs, or more human-friendly renderings suitable for terminals and HTML reports. Internally, the library splits input into hunks and manages edge cases such as... -
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Flutter Rust Bridge
Rust binding generator, feature-rich, but seamless and simple
...The project supports passing complex types, handling async operations and streams, and integrating with Flutter across mobile and desktop targets. By leaning on Rust’s memory safety and zero-cost abstractions, it enables compute-heavy tasks—parsing, crypto, image/audio processing, and more—without sacrificing Flutter’s developer experience. Build scripts and templates streamline packaging and distribution so the Rust side fits cleanly into CI and multi-platform releases. In practice, teams gain a maintainable way to share one performant Rust core across multiple Flutter apps while keeping the UI reactive and fast. -
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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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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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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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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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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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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.
