Search Results for "high performance computing"
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Showing 2158 open source projects for "high performance computing"
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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.
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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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high-performance-go
high performance coding with golang
High Performance Go is an educational repository focused on writing faster and more memory-efficient Go programs. It teaches developers how to measure performance before attempting optimization. Benchmarking and pprof are used to identify bottlenecks and compare implementation choices. The material examines strings, slices, loops, reflection, empty structs, and memory alignment. -
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Minecraft High-Performance Redstone
A multithreaded Minecraft server built for redstone
A Minecraft 1.20.4 creative server built for Redstone. Each 256x256 plot runs on a separate thread, allowing for less lag, more concurrency, and many awesome extra features. MCHPRS is very different from traditional servers. Because this server is tailored to the use of computation redstone, many things that are a part of Vanilla Minecraft servers don't exist here. That being said, MCHPRS comes with many of its own unique features. -
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The Julia Programming Language
High-level, high-performance dynamic language for technical computing
Julia is a fast, open source high-performance dynamic language for technical computing. It can be used for data visualization and plotting, deep learning, machine learning, scientific computing, parallel computing and so much more. Having a high level syntax, Julia is easy to use for programmers of every level and background. Julia has more than 2,800 community-registered packages including various mathematical libraries, data manipulation tools, and packages for general purpose computing. ... -
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NVIDIA Warp
A Python framework for accelerated simulation, data generation
NVIDIA Warp is a high-performance Python framework developed by NVIDIA for building and accelerating simulation, graphics, and physics-based workloads using GPU computing. It enables developers to write kernel-level code in Python that is automatically compiled into efficient CUDA kernels, combining ease of use with near-native performance. The framework is designed for applications such as robotics, reinforcement learning, physical simulation, and differentiable computing, where performance and flexibility are critical. ... -
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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Sogou C++ Workflow
C++ parallel computing and asynchronous networking engine
As Sogou`s C++ server engine, Sogou C++ Workflow supports almost all back-end C++ online services of Sogou, including all search services, cloud input method, online advertisements, etc., handling more than 10 billion requests every day. This is an enterprise-level programming engine in light and elegant design which can satisfy most C++ back-end development requirements. -
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Chitu
High-performance inference framework for large language models
Chitu is a high-performance inference engine designed to deploy and run large language models efficiently in production environments. The framework focuses on improving efficiency, flexibility, and scalability for organizations that need to run LLM inference workloads across different hardware platforms. It supports heterogeneous computing environments, including CPUs, GPUs, and various specialized AI accelerators, allowing models to run across a wide range of infrastructure configurations. ... -
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CUDA Python
Performance meets Productivity
...The toolkit also includes utilities for profiling, memory management, distributed computing, and numerical operations, making it suitable for scientific computing, AI, and data processing workloads. -
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AssemblyScript
A TypeScript-like language for WebAssembly
...Its design bridges the gap between high-level scripting and low-level execution, allowing developers to achieve near-native performance without switching to languages like C or Rust. AssemblyScript integrates with the WebAssembly ecosystem, enabling seamless interaction with JavaScript through standard APIs. It is widely used in scenarios where performance and portability are essential, such as gaming, data processing, and edge computing. -
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N64 Wasm
A web based N64 Emulator
...It focuses on accessibility, allowing users to play retro games on any device with a compatible browser. The emulator integrates input handling and rendering pipelines optimized for web performance. It also serves as a technical showcase of WebAssembly’s capabilities for high-performance applications. Overall, N64Wasm bridges retro gaming and modern web platforms. -
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ObjectBox Go Database
Embedded Go Database, the fast alternative to SQLite, gorm, etc.
ObjectBox Go is a lightweight and high-performance embedded NoSQL database designed for Go applications, providing efficient data storage and retrieval with minimal overhead. -
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Quantum++
Modern C++ quantum computing library
...The main design factors taken in consideration were ease of use, high portability, and high performance. The library's simulation capabilities are only restricted by the amount of available physical memory. On a typical machine (Intel i5 8Gb RAM) Quantum++ can successfully simulate the evolution of 25 qubits in a pure state or of 12 qubits in a mixed state reasonably fast. -
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Google Highway
Performance-portable, length-agnostic SIMD with runtime dispatch
Google Highway is a high-performance C++ library designed to provide portable SIMD (Single Instruction, Multiple Data) vectorization across multiple CPU architectures while maintaining predictable and efficient behavior. It abstracts low-level vector intrinsics into a consistent API that maps closely to hardware instructions, allowing developers to write high-performance code without relying heavily on compiler auto-vectorization. -
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CubeCL
Multi-platform high-performance compute language extension for Rust
CubeCL is a low-level compute language and compiler framework designed to simplify and optimize GPU programming for high-performance workloads, particularly in machine learning and numerical computing. It provides an abstraction layer that allows developers to write portable, hardware-efficient compute kernels without directly dealing with complex GPU APIs such as CUDA or OpenCL. CubeCL focuses on delivering predictable performance and composability by exposing explicit control over memory layouts, parallelism, and execution patterns while still maintaining a developer-friendly syntax. ... -
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Infinispan
Infinispan is an open source data grid platform
Infinispan is a distributed in-memory data grid and caching system designed for high-performance computing. It allows applications to scale dynamically by distributing data across multiple nodes, reducing latency and improving resilience. -
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plow
A high-performance HTTP benchmarking tool
Plow is an HTTP(S) benchmarking tool, written in Golang. It uses excellent fast HTTP instead of Go's default net/http due to its lightning-fast performance. Plow runs at specified connections (option -c) concurrently and real-time records summary statistics, histogram of execution time and calculates percentiles to display on Web UI and terminal. It can run for a set duration( option -d), for a fixed number of requests(option -n), or until Ctrl-C is interrupted. The implementation of real-time computing Histograms and Quantiles using stream-based algorithms inspired by Prometheus with low memory and CPU bounds. so it's almost no additional performance overhead for benchmarking. -
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Dagger.jl
A framework for out-of-core and parallel execution
Dagger.jl is a framework for out-of-core and parallel computing in Julia that allows users to construct and execute dynamic task graphs. It is designed for large-scale, distributed, and memory-efficient computations. Dagger supports lazy evaluation and scheduling across multiple threads or machines, enabling high-performance workflows for data processing, scientific computing, and machine learning. -
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HDF5
Official HDF5® Library Repository
HDF5 (Hierarchical Data Format v5) is a widely-used data management library and file format for storing large and complex scientific data sets efficiently. -
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RuleGo
Component orchestration rule engine framework for Go
RuleGo is a high-performance, component-based rule engine implemented in Go. It’s lightweight, embeddable, orchestration-ready, and built for flexible composition of business logic into reusable components. No external middleware dependencies, efficient data processing and linkage on low-cost devices, suitable for IoT edge computing. Embedded and Standalone Deployment modes. -
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MinIO
High performance object storage server compatible with Amazon S3 APIs
MinIO is a high performance object storage server that is API compatible with Amazon S3 cloud storage service. MinIO makes it easy to build high performance, cloud native data infrastructure for machine learning, analytics and application data workloads. It is incredibly fast, enabling object storage to operate as the primary storage tier for a diverse set of workloads. -
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Apache SeaTunnel
SeaTunnel is a distributed, high-performance data integration platform
SeaTunnel is a very easy-to-use ultra-high-performance distributed data integration platform that supports real-time synchronization of massive data. It can synchronize tens of billions of data stably and efficiently every day, and has been used in the production of nearly 100 companies. There are hundreds of commonly-used data sources of which versions are incompatible. With the emergence of new technologies, more data sources are appearing. -
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SIMD
C++ wrappers for SIMD intrinsics
SIMD is a C++ library that provides portable abstractions over SIMD (Single Instruction, Multiple Data) instructions, enabling developers to write high-performance vectorized code without dealing directly with architecture-specific intrinsics. SIMD instructions allow a single operation to be applied to multiple data elements simultaneously, significantly accelerating numerical and data-parallel computations. However, differences across CPU architectures and compilers make direct usage complex, which xsimd addresses by offering a unified API that maps efficiently to underlying hardware capabilities. ... -
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Hertz
Go HTTP framework with high-performance and strong-extensibility
Hertz [həːts] is a high-usability, high-performance and high-extensibility Golang HTTP framework that helps developers build microservices. It was designed with reference to other open-source frameworks like fasthttp, gin, echo and combined with the internal requirements in ByteDance. At present, it has been widely used inside ByteDance. Nowadays, more and more microservices use Golang. -
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ncnn
High-performance neural network inference framework for mobile
ncnn is a high-performance neural network inference computing framework designed specifically for mobile platforms. It brings artificial intelligence right at your fingertips with no third-party dependencies, and speeds faster than all other known open source frameworks for mobile phone cpu. ncnn allows developers to easily deploy deep learning algorithm models to the mobile platform and create intelligent APPs. -
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CubeFS
cloud-native file store
...Supports multi-tenant management and provides fine-grained tenant isolation policies. Supports multi-level caching, and multiple high-performance replication protocols, and optimizes specific performance for small files. -
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ModelingToolkit.jl
Modeling framework for automatically parallelized scientific ML
ModelingToolkit.jl is a modeling language for high-performance symbolic-numeric computation in scientific computing and scientific machine learning. It then mixes ideas from symbolic computational algebra systems with causal and acausal equation-based modeling frameworks to give an extendable and parallel modeling system. It allows for users to give a high-level description of a model for symbolic preprocessing to analyze and enhance the model.
