Search Results for "distributed shared memory" - Page 8
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Showing 326 open source projects for "distributed shared memory"
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PyText
A natural language modeling framework based on PyTorch
...It achieves this by providing simple and extensible interfaces and abstractions for model components, and by using PyTorch’s capabilities of exporting models for inference via the optimized Caffe2 execution engine. We use PyText at Facebook to iterate quickly on new modeling ideas and then seamlessly ship them at scale. Distributed-training support built on the new C10d backend in PyTorch 1.0. Mixed precision training support through APEX (trains faster with less GPU memory on NVIDIA Tensor Cores). Extensible components that allows easy creation of new models and tasks. -
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SINGA
A distributed deep learning platform
Apache SINGA is an Apache Top Level Project, focusing on distributed training of deep learning and machine learning models. Various example deep learning models are provided in SINGA repo on Github and on Google Colab. SINGA supports data parallel training across multiple GPUs (on a single node or across different nodes). SINGA supports various popular optimizers including stochastic gradient descent with momentum, Adam, RMSProp, and AdaGrad, etc. -
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BPF Performance Tools
Official repository for the BPF Performance Tools book
BPF Performance Tools Book is the companion repository for Brendan Gregg’s book on Linux performance analysis using eBPF and BCC tracing technologies. The project contains scripts, examples, and reference material that demonstrate how to inspect kernel behavior, application performance, CPU usage, networking activity, file systems, and system bottlenecks in real time. It serves as both an educational resource and a practical toolkit for Linux engineers, SREs, and performance analysts working... -
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BytePS
A high performance and generic framework for distributed DNN training
BytePS is a high-performance and generally distributed training framework. It supports TensorFlow, Keras, PyTorch, and MXNet, and can run on either TCP or RDMA networks. BytePS outperforms existing open-sourced distributed training frameworks by a large margin. For example, on BERT-large training, BytePS can achieve ~90% scaling efficiency with 256 GPUs (see below), which is much higher than Horovod+NCCL. -
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Image Super-Resolution (ISR)
Super-scale your images and run experiments with Residual Dense
The goal of this project is to upscale and improve the quality of low-resolution images. This project contains Keras implementations of different Residual Dense Networks for Single Image Super-Resolution (ISR) as well as scripts to train these networks using content and adversarial loss components. Docker scripts and Google Colab notebooks are available to carry training and prediction. Also, we provide scripts to facilitate training on the cloud with AWS and Nvidia-docker with only a few... -
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PyTorch-BigGraph
Generate embeddings from large-scale graph-structured data
PyTorch-BigGraph (PBG) is a system for learning embeddings on massive graphs—think billions of nodes and edges—using partitioning and distributed training to keep memory and compute tractable. It shards entities into partitions and buckets edges so that each training pass only touches a small slice of parameters, which drastically reduces peak RAM and enables horizontal scaling across machines. PBG supports multi-relation graphs (knowledge graphs) with relation-specific scoring functions, negative sampling strategies, and typed entities, making it suitable for link prediction and retrieval. ... -
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Texar
Toolkit for Machine Learning, Natural Language Processing
Texar is a toolkit aiming to support a broad set of machine learning, especially natural language processing and text generation tasks. Texar provides a library of easy-to-use ML modules and functionalities for composing whatever models and algorithms. The tool is designed for both researchers and practitioners for fast prototyping and experimentation. Texar was originally developed and is actively contributed by Petuum and CMU in collaboration with other institutes. A mirror of this... -
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ApertusVR
Virtual Reality software library
The main feature of ApertusVR is the so called "Distributed Plugin-in Mechanism" which means that not only humans could be involved in a multi-user virtual reality scene but any element of the Internet of Things like hardware, software, robot or any kind of smart device. ApertusCore is a programming library written in C++11, that fulfills modern software requirements as it is modular, embeddable, platform-independent, and easily configurable. It contains basic software interfaces and modules... -
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SIGnature by MAFII
SIGnature by MAFII is an ultra-lightweight System Information Gatherer
The data gathered includes: * General system information, such as the Manufacturer, Model, and Serial Number of the system * BIOS information such as the Name, Version, and Release Date of the currently installed BIOS * Operating System information such as Name, Version, Build, Installed Service Pack * Information about the installed CPU(s) * Detailed information about the system memory, including the total number of RAM slots available on the motherboard * Information about Disks, including Network Drives * Information about File Shares which are shared from the computer * Data about local and shared / network printers, including the share path and IP address / port used * Information about Network Adapter configurations * Detailed information about installed software, including Info/About URLs and Help / Support URLs where available (does not pull product keys). -
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ELF (Extensive Lightweight Framework)
An End-To-End, Lightweight and Flexible Platform for Game Research
ELF (Extensive, Lightweight, and Flexible) is a high-performance platform for reinforcement learning research that unifies simulation, data collection, and distributed training. A C++ core provides fast environments and concurrent actors, while Python bindings expose simple APIs for agents, replay, and optimization loops. It supports both single-agent and multi-agent settings, with batched stepping and shared-memory queues that keep GPUs saturated during training. ELF introduced widely used reference systems, most notably ELF OpenGo, demonstrating at-scale self-play with strong analysis tooling and public checkpoints. ... -
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Prometheus Spec
Censorship-resistant trustless state channels for machine learning
Censorship resistant decentralised computing is a new topic in distributed systems theory. With the recent rise in popularity of Bitcoin as a censorship-resistant financial and transactional system a new technological field of blockchain and its applications appeared. This work addresses the problem of computational integrity proofs (correctness of the actual computations) made by some non-trusted (anonymous) third party without repeating the whole volume of the actual computing. The issue... -
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httest is a script based tool for testing and benchmarking web applications, web servers, proxy servers and web browsers. httest can emulate clients and servers in the same test script, very useful for testing proxys.
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Eta
The Eta Programming Language, a dialect of Haskell on the JVM
A powerful programming language to build concurrent & distributed systems on the JVM. Eta is a pure, lazy, strongly typed functional programming language on the JVM. It brings two big ecosystems, the JVM and Haskell, together. This allows you to harness the best of both ecosystems to build your applications quickly and effectively. Eta's concurrency support helps you to build highly scalable systems. -
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Wally
Distributed Stream Processing
...By eliminating infrastructure complexity, going from prototype to production has never been simpler. When we set out to build Wally, we had several high-level goals in mind. Create a dependable and resilient distributed computing framework. Take care of the complexities of distributed computing "plumbing," allowing developers to focus on their business logic. Provide high-performance & low-latency data processing. Be portable and deploy easily (i.e., run on-prem or any cloud). Manage in-memory state for the application. Allow applications to scale as needed, even when they are live and up-and-running. ... -
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Pathephone
Distributed audio player
Imagine streaming an application without a single server, yet able to discover music for you. There is no way to control, to block or even to shut down Pathephone network. You are free to share and listen vinyl quality music if you wish. Any music shared by others is available to you, any music shared by you is available to others. No regional limits, works even behind Great Chinese Firewall. There is no one in control, and no one will ever charge a fee. Bugfixes and new features are... -
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OpenSeq2Seq
Toolkit for efficient experimentation with Speech Recognition
...Mixed-precision support (float16) is optimized for NVIDIA Volta and Turing GPUs, allowing significant speedups and memory savings without sacrificing model quality. The project comes with configuration-driven training scripts, documentation, and examples that demonstrate how to set up pipelines for tasks. -
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R3ETelemetry
Open source telemetry tool for R3E
R3ETelemetry is a telemetry tool for the PC racing simulation RaceRoom Racing Experience (R3E) from Sector3 Studio. It helps to record and analyze your driving in R3E. R3ETelemetry uses the API from R3E (shared memory) to gather data in form of telemetry data. This data then can be analyzed inside R3ETelemetry. The tool also helps to prepare for long distance races, as it also records fuel consumption and tire wear (per lap). -
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RSMQ
Redis Simple Message Queue
A lightweight Redis-backed message queue for handling simple distributed messaging and job processing. -
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JCSprout
Basic, concurrent algorithm
JCSprout is a curated learning path for Java engineers that mixes concise notes, diagrams, and runnable examples to cover core computer science and JVM topics. It walks readers through data structures and algorithms, networking fundamentals, Java concurrency, JVM memory model and GC, and common interview problem patterns. The repository emphasizes understanding over memorization, linking conceptual summaries with small code artifacts that can be compiled and profiled. It also highlights best... -
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Napa.js
Napa.js, a multi-threaded JavaScript runtime
...Node.js compatible module architecture with NPM support. API for object transportation, object sharing and synchronization across JavaScript threads. API for pluggable logging, metric and memory allocator. Distributed as a Node.js module, as well as supporting embed scenarios. Most of the time, there is no need to build Napa.js. When consuming Napa.js via npm install napajs, npm will first try to download the pre-build binaries for the current platform. -
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Easy Machine Learning
Easy Machine Learning is a general-purpose dataflow-based system
Machine learning algorithms have become the key components in many big data applications. However, the full potential of machine learning is still far from being realized because using machine learning algorithms is hard, especially on distributed platforms such as Hadoop and Spark. The key barriers come from not only the implementation of the algorithms themselves but also the processing for applying them to real applications which often involve multiple steps and different algorithms. Our... -
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popt4jlib
Parallel Optimization Library for Java
...A fast parallel implementation of the network simplex method, and some full-fledged parallel/distributed MIP solvers will be added in the next version. In general, emphasis is given in improving the efficiency of the algorithms in shared-memory models via java threads, since multi-core machines are so wide-spread today. -
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Lists all the processes, executables, and shared libraries that are using up virtual memory. It's helpful to see how the shared memory is used and which 'old' libs are loaded.
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Extended Memory Semantics (EMS)
Persistent shared object memory and parallelism for Node.js and Python
EMS makes possible persistent shared memory parallelism between Node.js, Python, and C/C++. Extended Memory Semantics (EMS) unifies synchronization and storage primitives to address several challenges of parallel programming. A modern multi-core server has 16-32 cores and nearly 1TB of memory, equivalent to an entire rack of systems from a few years ago. As a consequence, jobs formerly requiring a Map-Reduce cluster can now be performed entirely in shared memory on a single server without using distributed programming. -
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TORO is demonstrating an innovative operating system by integrating at the same ring level both kernel and the user application server. The threads of the user application server are distributed evenly on all CPUs and running independently in parallel. The memory model chosen is NUMA without pagination. During the initialization, the memory is divided proportionally for each processor installed on the system. When a thread needs memory, the memory allocator returns a free block of memory depending on which CPU the thread is running. ...
