Search Results for "distributed shared memory" - Page 7
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Showing 295 open source projects for "distributed shared memory"
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SnappyData
Memory optimized analytics database, based on Apache Spark
SnappyData (aka TIBCO ComputeDB) is a distributed, in-memory optimized analytics database. SnappyData delivers high throughput, low latency, and high concurrency for a unified analytics workload. By fusing an in-memory hybrid database inside Apache Spark, it provides analytic query processing, mutability/transactions, access to virtually all big data sources and stream processing all in one unified cluster. -
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AWS IoT Over-the-air Update Library
Manage the notification of a newly available update
...This library is distributed under the MIT Open Source License. This library has gone through code quality checks including verification that no function has a GNU Complexity score over 8. -
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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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.NET Boxed
.NET project templates with batteries included
...Use the HTTPS scheme and TLS security across the entire site, redirects HTTP to HTTPS, and adds a Strict Transport Security (HSTS) HTTP header with preloading enabled. Both in-memory and distributed cache are configured. You do need to specify where the distributed cache stores its data. Allow configuring Kestrel security limits such as maximum request size via configuration and set sensible stricter defaults. -
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TensorFlowOnSpark
TensorFlowOnSpark brings TensorFlow programs to Apache Spark clusters
By combining salient features from the TensorFlow deep learning framework with Apache Spark and Apache Hadoop, TensorFlowOnSpark enables distributed deep learning on a cluster of GPU and CPU servers. It enables both distributed TensorFlow training and inferencing on Spark clusters, with a goal to minimize the amount of code changes required to run existing TensorFlow programs on a shared grid. -
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MXNet
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning
Apache MXNet is a scalable, efficient open-source deep learning framework—offering a flexible hybrid programming model (symbolic + imperative) and supporting a wide array of languages—designed for training and deploying neural networks across heterogeneous systems. Apache MXNet is a deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, MXNet contains a dynamic... -
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Apache MXNet (incubating)
A flexible and efficient library for deep learning
Apache MXNet is an open source deep learning framework designed for efficient and flexible research prototyping and production. It contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations. On top of this is a graph optimization layer, overall making MXNet highly efficient yet still portable, lightweight and scalable. -
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HipMer
A High performance distributed memory assembler for big genomic data
HipMer is the first end-to-end de novo genome assembler designed for extreme scale analysis via efficient parallelization. The single-genome assembly implementation is a high-performance parallelization and port of the Meraculous assembler (http://jgi.doe.gov/data-and-tools/meraculous/). The MetaHipMer extension is a recent addition to HipMer that is geared to large metagenomes and leverages iterative kmer sizes and a specialized scaffolding algorithm to produce increased contiguity and... -
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FreeRTOS AWS Reference Integrations
IoT operating system for microcontrollers.
...A microcontroller contains a simple, resource-constrained processor that can be found in many devices, including appliances, sensors, fitness trackers, industrial automation, and automobiles. Many of these small devices can benefit from connecting to the cloud or locally to other devices, but have limited compute power and memory capacity. -
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wholeaked
Tool that embeds identifiers in files to trace leak sources
wholeaked is an open source file distribution and tracking tool designed to help identify the source of leaked files. It works by generating unique versions of a file for each recipient and embedding identifying signatures or metadata into each distributed copy. If the file later appears in an unauthorized location, the embedded identifier can be analyzed to determine which recipient originally received that specific version. This approach allows organizations, researchers, or individuals to trace the origin of leaks in sensitive documents, binaries, or other shared resources. wholeaked automates the process of creating personalized file variants and associating them with a list of recipients. ... -
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PJON
Experimental, arduino-compatible, multi-master, multi-media network
PJON® (Padded Jittering Operative Network) is an experimental, Arduino-compatible, multi-master, multi-media, software-defined network protocol that can be easily cross-compiled on many microcontrollers and real-time operative systems like ATtiny, ATmega, SAMD, ESP8266, ESP32, STM32, Teensy, Raspberry Pi, Zephyr, Linux, Windows x86, Apple and Android. PJON operates on a wide range of media, data links and existing protocols like PJDL, PJDLR, PJDLS, Serial, RS485, USB, ASK/FSK, LoRa, UDP,... -
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JAMon API
Monitor Java applications - SQL, HTTP, Methods, Exceptions and more.
JAMon API is a free, simple, high performance, thread safe, Java API that allows developers to easily monitor the performance and scalability of production applications. JAMon tracks hits, execution times (total, avg, min, max, std dev), and more. * JAMon Users Manual: For more on the JAMon, including installing, configuring, and using it, see http://jamonapi.sourceforge.net/. * Support: If you have any questions about usage please post a question on the forum at ... -
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Gitcoin Web
Grow Open Source
...Build resilient projects, better coordination, and positive-sum outcomes. Growing networks with aligned incentives toward the wellbeing of each participant and the system as a whole. Through distributed funding and organizations, we build together toward our shared goals. -
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Wasmer Go
WebAssembly runtime for Go
...The runtime delivers near-native performance by leveraging Wasmer’s optimized execution engine while maintaining strong sandboxing guarantees for safe execution of untrusted code. Wasmer Go supports loading, compiling, and running WebAssembly modules, as well as interacting with them through imports, exports, and memory management. It relies on precompiled shared libraries and CGO to bridge between Go and the underlying Wasmer runtime, ensuring compatibility across multiple operating systems. -
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OpenTSDB
A scalable, distributed time series database
OpenTSDB is a distributed, scalable Time Series Database (TSDB) written on top of HBase. OpenTSDB was written to address a common need: store, index and serve metrics collected from computer systems (network gear, operating systems, applications) at a large scale, and make this data easily accessible and graphable. Store and serve massive amounts of time series data without losing granularity. -
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twemproxy
A fast, light-weight proxy for memcached and redis
twemproxy (pronounced "two-em-proxy"), aka nutcracker is a fast and lightweight proxy for memcached and redis protocol. It was built primarily to reduce the number of connections to the caching servers on the backend. This, together with protocol pipelining and sharding enables you to horizontally scale your distributed caching architecture. Fast and lightweight. Maintains persistent server connections. Keeps connection count on the backend caching servers low. Enables pipelining of requests... -
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Amadeus
Harmonious distributed data analysis in Rust
Amadeus is a high-performance, distributed data processing framework written in Rust, designed to offer an ergonomic and safe alternative to tools like Apache Spark. It provides both streaming and batch capabilities, allowing users to work with real-time and historical data at scale. Thanks to Rust’s memory safety and zero-cost abstractions, Amadeus delivers performance gains while reducing the complexity and bugs common in large-scale data pipelines. -
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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...
