Search Results for "high performance computing"
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Showing 13 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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Go from Code to Production URL in SecondsSkip the Kubernetes configs. Cloud Run handles HTTPS, scaling, and infrastructure automatically. Two million requests free per month.
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XNNPACK
High-efficiency floating-point neural network inference operators
XNNPACK is a highly optimized, low-level neural network inference library developed by Google for accelerating deep learning workloads across a variety of hardware architectures, including ARM, x86, WebAssembly, and RISC-V. Rather than serving as a standalone ML framework, XNNPACK provides high-performance computational primitives—such as convolutions, pooling, activation functions, and arithmetic operations—that are integrated into higher-level frameworks like TensorFlow Lite, PyTorch Mobile, ONNX Runtime, TensorFlow.js, and MediaPipe. The library is written in C/C++ and designed for maximum portability, efficiency, and performance, leveraging platform-specific instruction sets (e.g., NEON, AVX, SIMD) for optimized execution. ... -
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OpenTelemetry Collector distributions
OpenTelemetry Collector Official Releases
High-quality, ubiquitous, and portable telemetry to enable effective observability. OpenTelemetry is a collection of APIs, SDKs, and tools. Use it to instrument, generate, collect, and export telemetry data (metrics, logs, and traces) to help you analyze your software’s performance and behavior. Create and collect telemetry from your services and software, then forward it to a variety of analysis tools. -
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RLax
Library of JAX-based building blocks for reinforcement learning agents
RLax (pronounced “relax”) is a JAX-based library developed by Google DeepMind that provides reusable mathematical building blocks for constructing reinforcement learning (RL) agents. Rather than implementing full algorithms, RLax focuses on the core functional operations that underpin RL methods—such as computing value functions, returns, policy gradients, and loss terms—allowing researchers to flexibly assemble their own agents. It supports both on-policy and off-policy learning, as well as value-based, policy-based, and model-based approaches. RLax is fully JIT-compilable with JAX, enabling high-performance execution across CPU, GPU, and TPU backends. ... -
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.NET Core Home
Home repository for .NET Core
This is the dotnet/core repository and is a good starting point for .NET Core, an open source general-purpose development framework for building cross-platform apps. .NET Core lets you create apps for Windows, macOS or Linux, as well as ARM64 processors using various programming languages. It provides frameworks and APIs for cloud, client UI, IoT, and machine learning. The latest major release (as of this writing) is .NET Core 3.1. You must be on the latest patch release in order to get... -
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CityHash
Automatically exported from Google code CityHash
...The library emphasizes hashing performance and uniformity rather than cryptographic security, making it ideal for use in data structures like hash tables and distributed systems requiring rapid key lookups. CityHash has been rigorously tested using tools like SMHasher to ensure high-quality mixing and collision resistance across a wide range of inputs. Its speed and portability have made it a popular choice for developers needing dependable, lightweight hash functions. -
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Mundane
Mundane is a Rust cryptography library backed by BoringSSL
Mundane is a Rust cryptography library designed to be safe, ergonomic, and high-performance, with its cryptographic operations powered by BoringSSL. It aims to eliminate common security pitfalls by providing APIs that are intentionally difficult to misuse, prioritizing safety and correctness over flexibility. The library vendors its own copy of BoringSSL, ensuring consistent builds without external dependencies and avoiding symbol conflicts between different versions. ... -
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Tracing Framework
Web Tracing Framework libraries and extensions
The Web Tracing Framework (WTF) is an open source suite of tools, libraries, and visualizers created by Google to help developers analyze and optimize the performance of complex web applications. By adding lightweight instrumentation to their code, developers can trace execution flow, identify bottlenecks, and detect performance regressions. The framework provides fine-grained insights into application behavior, making it particularly useful for achieving smooth, high-performance experiences, such as maintaining 60fps animations in web interfaces. ... -
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RefineNet
RefineNet: Multi-Path Refinement Networks
RefineNet is a MATLAB-based framework for semantic image segmentation and general dense prediction tasks. It implements the architecture presented in the CVPR 2017 paper RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation and its extended version published in TPAMI 2019. The framework uses multi-path refinement and improved residual pooling to achieve high-quality segmentation results across multiple benchmark datasets. It provides trained models for datasets such as PASCAL VOC 2012, Cityscapes, NYUDv2, Person_Parts, PASCAL_Context, SUNRGBD, and ADE20k, with versions based on ResNet-101 and ResNet-152 backbones. ... -
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Schism
A self-hosting Scheme to WebAssembly compiler
...The project focuses on leveraging WebAssembly’s experimental capabilities, including reference types and tail calls, to test the limits of language portability and runtime performance. Though no longer actively maintained, Schism remains a valuable example of how high-level languages can be mapped to WebAssembly and offers insights into language implementation. -
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Caffe2
Caffe2 is a lightweight, modular, and scalable deep learning framework
...You can bring your creations to scale using the power of GPUs in the cloud or to the masses on mobile with Caffe2’s cross-platform libraries. Modularity and being designed for both scale and mobile deployments are the high-level answers to the first question. In many ways Caffe2 is an un-framework because it is so flexible and modular. The original Caffe framework was useful for large-scale product use cases, especially with its unparalleled performance and well tested C++ codebase. Caffe has some design choices that are inherited from its original use case: conventional CNN applications. -
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Slackless
Base Slackware Virtual Image
A slightly modified base package Slackware image suitable for VM performance testing. Project Goals: Development of production testing protocols Base Image Base +Apache Base + MySQL Base + node.js Base + AoE Standard Full Slackware Current VM image The goal of the Slackless project is really two-fold. To provide high quality virtual images of the original Slackware operating system suitable for use and testing in virtual environments in an effort to introduce users new to Slackware to the advantages of the K.I.S.S. development philosophy. ... -
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OpenESM project to promote software development of enterprise systems management. Focus areas are (Performance and Availability, Configuration Management, Data Warehousing, Reporting and Correlation).
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Tools, information, and practices for creating and operating dependable, complex, distributed, software-intensive systems. * Sense - Measure - Analyze - Respond - Optimize * Keywords: Performance Co-Pilot - PCP - PCPMon - Rjobd
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