Search Results for "virtual machine plugins"
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Showing 11 open source projects for "virtual machine plugins"
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Automate contact and company data extractionGenerate leads at scale without building or maintaining scrapers. Use 10,000+ ready-made tools that handle authentication, pagination, and anti-bot protection. Pull data from business directories, social profiles, and public sources, then export to your CRM or database via API. Schedule recurring extractions, enrich existing datasets, and integrate with your workflows.
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stress-ng
This is the stress-ng upstream project git repository.
This is the stress-ng upstream project git repository. stress-ng will stress test a computer system in various selectable ways. It was designed to exercise various physical subsystems of a computer as well as the various operating system kernel interfaces. stress-ng was originally intended to make a machine work hard and trip hardware issues such as thermal overruns as well as operating system bugs that only occur when a system is being thrashed hard. Use stress-ng with caution as some of... -
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Raspberry_Quemu_forWindows
This is QEMU virtual machine with Raspbian OS for students KNUCA
Це віртуальна машина QEMU із зібраною операційною системою Raspbian для емуляції Raspberry Pi на Windows 7, 8, 10. Використовується для занять з робототехніки в Київському національному університеті будівництва і архітектури. pi/raspberry -
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Real time micro benchmark multi-lingual suite (RTMB) provides a good way to measure the deterministic quality of hardware, an operating system, or a Java Virtual Machine. RTMB consists of benchmarks in C, C++ and Java
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FIRMAMENT runs, inside the Linux kernel, user supplied micro-programs over each processed message to emulate communication fault situations, using a script approach. The tool is appropriate to perform experiments over protocols and distributed systems.
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Say goodbye to broken revenue funnels and poor customer experiencesLeanData is a Demand Management solution that supports all go-to-market strategies such as account-based sales development, geo-based territories, and more. LeanData features a visual, intuitive workflow native to Salesforce that enables users to view their entire lead flow in one interface. LeanData allows users to access the drag-and-drop feature to route their leads. LeanData also features an algorithms match that uses multiple fields in Salesforce.
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Hardware Simulation and Vitualization Environment: Unfortunately I have lost my source code in a freak accident (My roommate dropped a bed onto my laptop) for this project. Please be patient and visit http://devsoft.ath.cx/crisis for more information.
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JDLabAgent is a dynamic link library which is loaded into the Java Virtual Machine via Java Virtual Machine Debug Interface (JVMDI). The JDLabAgent can be parameterized via config files and logs JVM events (e.g. breakpoints, exceptions).
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This is a dynamic slicing tool for Java programs. The tool modifies the Kaffe virtual machine to collect the execution trace, and compress the trace on-the-fly. Please proceed to the tool's website http://jslice.sourceforge.net/ for download.
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pyvix is a Python wrapper for the VMWare(R) VIX C API that allows Python to programmatically control VMWare(R) virtual machines. Example operations include: powering on; suspending; creating, reverting to, and removing snapshots; and running programs.
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Total Network Visibility for Network Engineers and IT ManagersThis means every device on your network, and every interface on every device is automatically analyzed for performance, errors, QoS, and configuration.
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Alchemist plugin
Alchemist GCC/LLVM plugin for code analysis and tuning
News: since 2015 we continue all related developments within Collective Knowledge Framework: http://github.com/ctuning/ck/wiki Alchemist plugin is a collection of plugins for GCC/LLVM for external and fine-grain code analysis and tuning. It is intended to to extract program properties for machine learning based optimization (see MILEPOST GCC); optimize programs at fine-grain level (such as unrolling, tiling, prefetching, etc); tune default optimization heuristic; gradually decompose program and detect performance or other anomalies; generate benchmarks particularly useful to train ML-based compilers. ...
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