Displaying 1 to 19 from 19 results

stackimpact-python - StackImpact Python Profiler - Production-Grade Performance Profiler: CPU, memory allocations, blocking calls, exceptions, metrics, and more

  •    Python

StackImpact is a production-grade performance profiler built for both production and development environments. It gives developers continuous and historical code-level view of application performance that is essential for locating CPU, memory allocation and I/O hot spots as well as latency bottlenecks. Included runtime metrics and error monitoring complement profiles for extensive performance analysis. Learn more at stackimpact.com. Learn more on the features page (with screenshots).

goappmonitor - Golang application performance data monitoring.

  •    Go

Golang application performance data monitoring.GoAppMonitor is a library which provides a monitor on your golang applications. It contains system level based monitoring and business level monitoring(custom monitoring).Just add the repository into your apps and register what you want to monitoring.

jamonapi - Another repo for jamonapi.com which is primarily hosted on sourceforge

  •    Java

The Java Application Monitor (JAMon) is a free, simple, high performance, thread safe, Java API that allows developers to easily monitor production applications. 1) It contains the ability to monitor JDBC/SQL, web page requests, ejb's, log4j, interfaces and more. 2) It tracks aggregate stats for each sql statement, page request etc. and also lets you look at this information and more via the JAMon war. 3) You can also access JAMon statistics via the api




simple-pt - Simple Intel CPU processor tracing on Linux

  •    C

simple-pt is a simple implementation of Intel Processor Trace (PT) on Linux. PT can trace all branches executed by the CPU at the hardware level with moderate overhead. simple-pt then decodes the branch trace and displays a function or instruction level trace. PT is supported on Intel 5th generation Core (Broadwell), 6th generation Core (Skylake) CPUs, and later, as well as Goldmont based Atom CPUs (Intel Joule, Apollo Lake) and later.

paper-synthesizing-benchmarks - 📝 "Synthesizing Benchmarks for Predictive Modeling" (CGO Best Paper 2017)

  •    Jupyter

Chris Cummins, Pavlos Petoumenos, Zheng Wang, Hugh Leather. Predictive modeling using machine learning is an effective method for building compiler heuristics, but there is a shortage of benchmarks. Typical machine learning experiments outside of the compilation field train over thousands or millions of examples. In machine learning for compilers, however, there are typically only a few dozen common benchmarks available. This limits the quality of learned models, as they have very sparse training data for what are often high-dimensional feature spaces. What is needed is a way to generate an unbounded number of training programs that finely cover the feature space. At the same time the generated programs must be similar to the types of programs that human developers actually write, otherwise the learning will target the wrong parts of the feature space.

iota - Fast [co]product types with a clean syntax. For Cats & Scalaz.

  •    Scala

Iota is a tiny library for fast coproduct types with a syntax that cleanly supports the disjunction of any number of types. Traditional coproduct implementations are implemented as binary trees or linked lists at both the type and value level. The syntax for traditional coproducts frequently becomes unwieldy as the number of disjunct types grows.

ck-wa - Collective Knowledge workflow for ARM's workload automation tool: an open framework for gathering and sharing knowledge about system design and optimization using real-world workloads

  •    Python

This Collective Knowledge repository provides high level front-end for ARM Workload Automation framework (WA). It includes unified JSON API to WA, automated experimentation, benchmarking and tuning across farms of machines (Android, Linux, MacOS, Windows), web-based dashboard, optimization knowledge sharing, etc. Please, read CK Getting Started Guide, DATE'16 paper and CPC'15 article for more details about CK and our vision of collaborative, reproducible and systematic experimentation. Relatively stable. Development is led by dividiti, the cTuning foundation and ARM.


sparklens - Qubole Sparklens tool for performance tuning Apache Spark

  •    Scala

Sparklens is a profiling tool for Spark with built-in Spark Scheduler simulator. Its primary goal is to make it easy to understand the scalability limits of spark applications. It helps in understanding how efficiently is a given spark application using the compute resources provided to it. May be your application will run faster with more executors and may be it wont. Sparklens can answer this question by looking at a single run of your application. It helps you narrow down to few stages (or driver, or skew or lack of tasks) which are limiting your application from scaling out and provides contextual information about what could be going wrong with these stages. Primarily it helps you approach spark application tuning as a well defined method/process instead of something you learn by trial and error, saving both developer and compute time.

owca - Orchestration-aware Workload Collocation Agent - a daemon that can help you collocate your workloads

  •    Python

This software is pre-production and should not be deployed to production servers. Orchestration-aware Workload Collocation Agent goal is to reduce interference between collocated tasks and increase tasks density while ensuring the quality of service for high priority tasks. Chosen approach is to enable real-time resource isolation management to ensure that high priority jobs meet their Service Level Objective (SLO) and best-effort jobs effectively utilize as many idle resources as possible.

stackimpact-go - StackImpact Go Profiler - Production-Grade Performance Profiler: CPU, memory allocations, blocking calls, errors, metrics, and more

  •    Go

StackImpact is a production-grade performance profiler built for both production and development environments. It gives developers continuous and historical code-level view of application performance that is essential for locating CPU, memory allocation and I/O hot spots as well as latency bottlenecks. Included runtime metrics and error monitoring complement profiles for extensive performance analysis. Learn more at stackimpact.com. Learn more on the features page (with screenshots).

stackimpact-nodejs - StackImpact Node

  •    Javascript

StackImpact is a production-grade performance profiler built for both production and development environments. It gives developers continuous and historical code-level view of application performance that is essential for locating CPU, memory allocation and I/O hot spots as well as latency bottlenecks. Included runtime metrics and error monitoring complement profiles for extensive performance analysis. Learn more at stackimpact.com. Learn more on the features page (with screenshots).

qoopido

  •    Javascript

Qoopido.demand is a modular, flexible and 100% async JavaScript module loader with a promise like interface that utilizes localStorage as a caching layer. It comes in a rather tiny package of ~7kB minified and gzipped. Qoopido.demand originated from my daily use of require.js for the initial development of my Qoopido.nucleus library which is strictly atomic by nature, unbundled.

powerstation - A Tool for Detecting Performance Bugs in Rails Applications

  •    Ruby

Powerstation is a tool that finds performance bugs in Rails applications, for example, API misuse, repeated query, etc. As a RubyMine plugin that you can download from the jetbrains website. The source code is included in this repo under powerstation/IDE_plugin.

Meteor-user-status - Reactively track user's [on|off]line status

  •    Javascript

Reactively setup user's [on|off]line and idle status into Meteor.user().profile.online, returns Boolean value. This package is meant to work only within accounts-base package, when users will login/logout. Simply add and use with accounts-base and accounts-password packages, ostrio:user-status will work silently behind it in the background, - doesn't require any setting up.

Java-performance-mind-map - A Java performance mind map based on different presentations, video and other information sources

  •    

Compilation from some information resources and presentations related to Java. It contains information which tools to use to check performance of Java application and receipts to resolve some detected issues. It is format of NBMindMap plugin, there are plugins for Intellij and NetBeans IDE, also there is standalone editor SciaReto. Also there is prerendered PNG version of the mind map in the repository.





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