Search Results for "throughput" - Page 8
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Showing 350 open source projects for "throughput"
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LLaMA-MoE
Building Mixture-of-Experts from LLaMA with Continual Pre-training
LLaMA-MoE is an open-source project that builds mixture-of-experts language models from LLaMA through expert partitioning and continual pre-training. The repository is centered on making MoE research more accessible by offering smaller and more affordable models with only about 3.0 to 3.5 billion activated parameters, which helps reduce deployment and experimentation costs. Its architecture works by splitting LLaMA feed-forward networks into sparse experts and adding gating mechanisms so... -
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HPN-SSH is a series of performance patches for OpenSSH. By addressing network limitations and CPU limitations significant throughput performance can be realized. Gains of close to two orders of magnitude are possible on long fat network paths. The official git repo is now available at http://github.com/rapier1/openssh-portable. The Sourceforge repository should not be seen as the canonical repository for HPN-SSH. We will update it as we can but users should look to github to generate patches We also support Ubuntu packages. ...
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RethinkDB
The open-source database for the realtime web
...RethinkDB can be used for a number of different things: building realtime trading and optimization engines; simplifying the data infrastructure in multiplayer games to produce low latency, high throughput realtime interactions; building reactive web and mobile apps with a lot less effort, and so much more. -
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rathole
A lightweight and high-performance reverse proxy for NAT traversal
A secure, stable and high-performance reverse proxy for NAT traversal, written in Rust. rathole, like frp and ngrok, can help to expose the service on the device behind the NAT to the Internet, via a server with a public IP. High Performance Much higher throughput can be achieved than frp, and more stable when handling a large volume of connections. Low Resource Consumption Consumes much fewer memory than similar tools. See Benchmark. The binary can be as small as ~500KiB to fit the constraints of devices, like embedded devices as routers. Security Tokens of services are mandatory and service-wise. ... -
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Medusa
Framework for Accelerating LLM Generation with Multiple Decoding Heads
Medusa is a framework aimed at accelerating the generation capabilities of Large Language Models (LLMs) by employing multiple decoding heads. This approach allows for parallel processing during text generation, significantly enhancing throughput and reducing response times. Medusa is designed to be simple to implement and integrates with existing LLM infrastructures, making it a practical solution for scaling LLM applications. -
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LMAX Disruptor
High performance inter-thread messaging library
LMAX aims to be the fastest trading platform in the world. Clearly, in order to achieve this we needed to do something special to achieve very low-latency and high-throughput with our Java platform. Performance testing showed that using queues to pass data between stages of the system was introducing latency, so we focused on optimising this area. The Disruptor is the result of our research and testing. We found that cache misses at the CPU-level, and locks requiring kernel arbitration are both extremely costly, so we created a framework which has "mechanical sympathy" for the hardware it’s running on, and that’s lock-free. ... -
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Dragonboat
A feature complete and high performance multi-group Raft library in Go
...It also appears to clients as a single entity with strong data consistency always provided. All Raft replicas can be used to handle read requests for aggregated read throughput. Dragonboat handles all technical difficulties associated with Raft to allow users to just focus on their application domains. It is also very easy to use, our step-by-step examples can help new users to master it in half an hour. Easy to use pure-Go APIs for building Raft based applications. Feature complete and scalable multi-group Raft implementation. ... -
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FastViT
This repository contains the official implementation of research
FastViT is an efficient vision backbone family that blends convolutional inductive biases with transformer capacity to deliver strong accuracy at mobile and real-time inference budgets. Its design pursues a favorable latency-accuracy Pareto curve, targeting edge devices and server scenarios where throughput and tail latency matter. The models use lightweight attention and carefully engineered blocks to minimize token mixing costs while preserving representation power. Training and inference recipes highlight straightforward integration into common vision tasks such as classification, detection, and segmentation. The codebase provides reference implementations and checkpoints that make it easy to evaluate or fine-tune on downstream datasets. ... -
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FastQC
A quality control analysis tool for high throughput sequencing data
FastQC is a quality control analysis tool designed to spot potential problems in high throughput sequencing datasets. Its goal is to provide a simple way by which to check the quality of raw sequence data coming from high throughput sequencing pipelines. It does this by running a modular set of analyses on one or more raw sequence files in fastq or bam format. It then produces a report summarizing the results, and highlighting any areas where the library may appear unusual. ... -
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FFCV
Fast Forward Computer Vision (and other ML workloads!)
ffcv is a drop-in data loading system that dramatically increases data throughput in model training. From gridding to benchmarking to fast research iteration, there are many reasons to want faster model training. Below we present premade codebases for training on ImageNet and CIFAR, including both (a) extensible codebases and (b) numerous premade training configurations. -
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FasterTransformer
Transformer related optimization, including BERT, GPT
FasterTransformer is a high-performance inference library designed to accelerate transformer-based models such as BERT, GPT, and T5 on NVIDIA GPUs. It provides optimized implementations of transformer encoder and decoder layers using CUDA, cuBLAS, and custom kernels to maximize throughput and minimize latency. The library supports multiple deep learning frameworks, including TensorFlow, PyTorch, and Triton, allowing developers to integrate it into existing pipelines without major changes. It includes advanced optimization techniques such as mixed precision, tensor parallelism, and efficient memory management, enabling large models to run across multiple GPUs and nodes. ... -
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ixy-languages
A high-speed network driver written in C, Rust, C++, Go, C#, Java
ixy-languages is a repository exploring the implementation of the ixy network driver (originally written in C) in multiple programming languages, showing how the same core logic can be expressed across language boundaries. The ixy driver is a minimal DPDK-based user-space NIC driver intended for education, demonstration, and controlled experimentation. This project reimplements the ixy driver architecture in Rust, Go, C++, Zig, and possibly other languages, maintaining the same driver... -
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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. One common use case for SnappyData is to provide analytics at interactive speeds over large volumes of data with minimal or no pre-processing of the dataset. ... -
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LightSeq
A High Performance Library for Sequence Processing and Generation
Lightseq is a high-performance library focused on efficient inference and training for deep learning models, especially large language models (LLMs) and transformer-based architectures. Its goal is to optimize both memory usage and computational throughput, enabling faster training or inference on limited hardware while maintaining model quality. Lightseq provides optimized CUDA kernels, quantization strategies, and runtime optimizations tailored for transformer operations — which often are bottlenecks in conventional frameworks — thereby reducing memory footprint, improving speed, and making deployment of large-scale models more accessible. ... -
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OmicSelector
Feature selection and deep learning modeling for omic biomarker study
OmicSelector is an environment, Docker-based web application, and R package for biomarker signature selection (feature selection) from high-throughput experiments and others. It was initially developed for miRNA-seq (small RNA, smRNA-seq; hence the name was miRNAselector), RNA-seq and qPCR, but can be applied for every problem where numeric features should be selected to counteract overfitting of the models. Using our tool, you can choose features, like miRNAs, with the most significant diagnostic potential (based on the results of miRNA-seq, for validation in qPCR experiments). -
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Apple Neural Engine (ANE) Transformers
Reference implementation of the Transformer architecture optimized
...The repository targets practitioners who want to keep familiar PyTorch modeling while preparing models for Core ML/ANE execution paths. Documentation highlights reported improvements in throughput and memory residency, while releases track incremental fixes and packaging updates. The project sits alongside related Apple ML repos that focus on deploying attention-based models efficiently to ANE-equipped hardware. In short, it’s a practical blueprint for adapting Transformers to Apple’s dedicated ML accelerator without rewriting entire model stacks. -
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CAT
CAT is the basic component of the server project
CAT is a distributed application performance monitoring and alerting system that specializes in tracking request flows, exceptions, and metrics across microservice ecosystems. It offers real-time dashboards showing throughput, response times, error rates, and service dependency graphs to help operations and development collaborate on reliability issues. In addition to metrics, it enables tracing—propagating context across RPC boundaries so problems like latency spikes or failed calls can be traced end-to-end. Alert rules and anomaly detection can be defined to notify teams proactively. ... -
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sRNAWorkbench
The UEA sRNA Workbench
...Including expression profiling of known mirco RNA (miRNA), identification of novel miRNA in deep-sequencing data and identification of other interesting landmarks within high-throughput genetic data -
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Poem Framework
A full-featured and easy-to-use web framework
...A front-end automated deployment platform based on continuous integration of aws. Hik-ProConnect project for Hikvision. A smart SSH bastion host that works with any SSH clients. A fast, auto-optimizing image server designed for high throughput and caching. Building the safest and most scalable Layer 1 blockchain. Casbin access control middleware for poem framework. -
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Dispatch-Proxy
Combine internet connections, increase your download speed
dispatch-proxy is a Node.js tool that exposes a local proxy and distributes outgoing connections across multiple network interfaces to combine bandwidth for parallel transfers. Instead of trying to accelerate a single TCP flow, it improves aggregate throughput by load-balancing many connections—useful for download managers, package managers, or browsers that open multiple requests at once. It can bind new outbound sockets to different interfaces, letting a machine take advantage of Wi-Fi and Ethernet (or multiple uplinks) simultaneously. The utility runs as a simple proxy endpoint, so existing applications can benefit without code changes by pointing their traffic to it. ... -
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Peptide Pattern Recognition
Software for non-alignment based analysis of DNA and protein sequences
Software for analysis, grouping and functional prediction of DNA and protein sequences as described in: Busk P.K. (2017). Peptide Pattern Recognition for high-throughput protein sequence analysis and clustering. BioRxiv. doi: https://doi.org/10.1101/181917. Busk P.K. and Lange L. (2013). Function-based classification of carbohydrate-active enzymes by recognition of short, conserved peptide motifs. Appl Environ Microbiol. 79(11), 3380-91. Busk P.K. Lange M. Pilgaard B. and Lange L. (2014). Several genes encoding enzymes with the same activity are necessary for aerobic fungal degradation of cellulose in nature. ... -
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musicForProgramming
Unofficial port of musicforprogramming.net to be hosted on IPFS
Listen to music while you program, streamed via the peer-to-peer technology IPFS. The music comes from musicforprogramming.net, and has been carefully chosen to maximize concentration by providing just the right amount of interest to occupy the parts of your brain that would otherwise be left free to wander and lead to distraction during your work. -
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hora
Efficient approximate nearest neighbor search algorithm collections
...The library is written in Rust and emphasizes performance, safety, and efficient memory management, making it suitable for production-grade applications requiring low latency and high throughput. -
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sirius signing server
Centralized signing and verification
Sirius-Sign is a signing and verification server with it's focus on high throughput and easy integration into an existinig landscape. For signature creation smartcards with OCF and PKCS11 interfaces are supported. An EJB container is required. -
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DockStream
A Docking Wrapper to Enhance De Novo Molecular Design
...The flexilibity to specifiy a large variety of docking configurations allows tailored protocols for diverse end applications. DockStream can also parallelize docking across CPU cores, increasing throughput. DockStream is integrated with the de novo design platform, REINVENT, allowing one to incorporate docking into the generative process, thus providing the agent with 3D structural information.
