Search Results for "throughput"
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Showing 17 open source projects for "throughput"
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fluentbit
Fast and Lightweight Logs and Metrics processor for Linux, BSD, OSX
Fluent Bit is a super-fast, lightweight, and highly scalable logging and metrics processor and forwarder. It is the preferred choice for cloud and containerized environments. A robust, lightweight, and portable architecture for high throughput with low CPU and memory usage from any data source to any destination. Proven across distributed cloud and container environments. Highly available with I/O handlers to store data for disaster recovery. Granular management of data parsing and routing. Filtering and enrichment to optimize security and minimize cost. The lightweight, asynchronous design optimizes resource usage: CPU, memory, disk I/O, network. ... -
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GenStage
Producer and consumer actors with back-pressure for Elixir
...Because stages are OTP processes, you gain fault tolerance, supervised restarts, and concurrency tuned via configurable demand and partitioning. GenStage underpins higher-level libraries like Flow and Broadway, but it can also be used directly for custom pipelines where timing and throughput matter. Its clear separation of concerns encourages testable, composable stages that can be rearranged as requirements evolve. In production, this leads to predictable, resilient dataflows for event ingestion, batching, and parallel processing. -
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clusterProfiler
A universal enrichment tool for interpreting omics data
clusterProfiler is an R/Bioconductor package that provides a unified workflow for functional enrichment analysis to interpret high-throughput omics results. It supports both over-representation analysis and gene set enrichment analysis, letting you work with unranked gene lists or ranked statistics from differential pipelines. The package connects to multiple knowledge bases—such as Gene Ontology, KEGG, Reactome, Disease Ontology, MeSH and others—through a consistent interface so you can query different biological lenses without rewriting code. ... -
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ParallelStencil.jl
Package for writing high-level code for parallel stencil computations
...For example, a 2-D shallow ice solver presented at JuliaCon 2020 [1] achieved a nearly 20 times better performance than a corresponding GPU Array programming implementation; in absolute terms, it reached 70% of the theoretical upper performance bound of the used Nvidia P100 GPU, as defined by the effective throughput metric, T_eff. ParallelStencil relies on the native kernel programming capabilities of CUDA.jl and AMDGPU.jl and on Base.Threads for high-performance computations on GPUs and CPUs, respectively. It is seamlessly interoperable with ImplicitGlobalGrid.jl, which renders the distributed parallelization of stencil-based GPU and CPU apps. -
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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ElasticJob
Distributed scheduled job framework
...It uses a unified job API for each project. Developers only need code one time and can deploy at will. Support job sharding and high availability in distributed system. Scale out for throughput and efficiency improvement. Job processing capacity is flexible and scalable with the allocation of resources. Execute job on suitable time and assigned resources. Aggregation same job to same job executor. Append resources to newly assigned jobs dynamically. Using ElasticJob can make developers no longer worry about the non-functional requirements such as jobs scale out, so that they can focus more on business coding. -
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Synapse Machine Learning
Simple and distributed Machine Learning
...With the HTTP on Spark project, users can embed any web service into their SparkML models. For production-grade deployment, the Spark Serving project enables high throughput, sub-millisecond latency web services, backed by your Spark cluster. -
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Nebula Graph
A distributed, fast open-source graph database
The graph database built for super large-scale graphs with milliseconds of latency. Optimized SUBGRAPH and FIND PATH for better performance. Optimized query paths to reduce redundant paths and time complexity. Optimized the method to get properties for better performance of MATCH statements. Nebula Graph adopts the Apache 2.0 license, one of the most permissive free software licenses in the world. Free as in freedom, because, under the Apache 2.0 license, you can use, copy, modify and... -
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Greenplum Database
Massive parallel data platform for analytics, machine learning and AI
...With its unique cost-based query optimizer designed for large-scale data workloads, Greenplum scales interactive and batch-mode analytics to large datasets in the petabytes without degrading query performance and throughput. Based on PostgreSQL, Greenplum provides you with more control over the software you deploy, reducing vendor lock-in, and allowing open influence on product direction. Greenplum reduces data silos by providing you with a single, scale-out environment for converging analytic and operational workloads, like streaming ingestion. All major Greenplum contributions are part of the Greenplum Database project and share the same database core, including the MPP architecture, analytical interfaces, and security capabilities. -
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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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TensorBase
TensorBase is a new big data warehousing with modern efforts
TensorBase hopes the open source not become a copy game. TensorBase has a clear-cut opposition to fork communities, repeat wheels, or hack traffic for so-called reputations (like Github stars). After thoughts, we decided to temporarily leave the general data warehousing field. For people who want to learn how a database system can be built up, or how to apply modern Rust to the high-performance field, or embed a lightweight data analysis system into your own big one. You can still try, ask... -
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TSA_CRAFT
Automatic command line tool for TSA data analysis
DSF is a high-throughput platform of TSA assay to screen various conditions that affect protein stability. To facilitate TSA data analysis, we developed an automatic tool "TSA-CRAFT". TSA-CRAFT was developed by integrating PERL script and Gnuplot. PERL scripts manage the entire workflow and data processing procedures of TSA data analysis. On the other hand, Gnuplot takes over the curve fitting and result presentation works. -
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GlycoPAT
Comprehensive, open-source tool for glycoproteomics MS data analysis
GlycoPAT is a modular, open-source MATLAB based toolbox for shotgun mass spectrometry based glycoproteomics data analysis. This program provides a novel platform for the streamlined analysis of traditional LC-MSn based high-throughput experimental data for the identification of site-specific N- and O-linked glycosylation on various proteins. More details are available at https://www.VirtualGlycome.org/glycopat Source code also available at https://github.com/kaichengub/GlycoPAT If you use this software please cite: Gang Liu, Kai Cheng, Chi Y. Lo, Jun Li, Jun Qu and Sriram Neelamegham. " A comprehensive, open-source platform for mass spectrometry based glycoproteomics data analysis", Molecular Cell Proteomics, 2017 Sep 8, in press. https://doi.org/10.1074/mcp.M117.068239 -
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Bamformatics
Toolkit and GUI for sequencing data analysis
The Bamformatics project aims to provide a coherent and consistent approach to analysis of high-throughput sequencing data. Its toolkit includes, among others, programs to identify variants and to compute various types genomic tracks. It also provides a graphical user interface to facilitate general bioinformatic workflows. The project wiki contains further details. -
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Apache Kafka
Mirror of Apache Kafka
Apache Kafka is a distributed streaming platform built around durable, partitioned logs called topics, enabling high-throughput, fault-tolerant event pipelines. Producers append records to partitions, brokers replicate them for durability, and consumer groups read them at their own pace while balancing work across instances. The commit/offset model and retention policies support patterns from real-time processing to event sourcing and audit trails. Exactly-once processing semantics, idempotent producers, and transactions help prevent duplicates across complex dataflows. ... -
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eRNA
A RNA data analysis tool
eRNA: A RNA data analysis tool for high-throughput RNA sequencing experiments -
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BriPy
A BRIC-seq data analysis package written in Python.
...It employs metabolic labeling by BrU of newly synthesized RNAs to chase chronological decreases of BrU-labeled RNA (Pulse-chase labeling), followed by immunoprecipitation with antibody for BrU, construction of cRNA library for High-throughput sequencing and computational data analysis. -What is BriPy? BriPy package is a BRIC-seq data analysis tool written in Python. It can calculate RNA half-life for each transcript and draw graphs describing the kinetics of RNA decay. In the future, it will provide several useful modules(e.g. calculating RPKM of each transcript from BAM file or ELAND output file). ... -
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Thea, Tools for High-throughput Experiment Analysis, is an integrated information processing system dedicated to the annotation of data issued from classification systems with biological information coming from a knowledge base.
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