NetStress is a DDoS and network stress testing tool.
Syn Flood Attacks
SYNFlood with static source port
SYNFlood with random source port
SYNFlood with static source ip address
SYNFlood with random source address
SynFlood with fragmented packets
ACK Flood Attacks
ACK Flood with static source port
ACK Flood with random source port
ACK Flood with static source ip address
ACK Flood with random source address
ACK Flood with fragmented packets
FIN Flood Attacks
FIN Flood with static source port
FIN Flood with random source port
FIN Flood with static source ip address
FIN Flood with random source address
FIN Flood with fragmented packets
UDP Flood Attacs
Static source port udp flood
UDP flood with random source port
UDP Flood with static source ip address
UDP Flood with random source address
UDP Flood with fragmented packets
ICMP Flood
ICMP Flood with all options random(source ip, icmp type, code)
HTTP Flood
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More info: http://sf.net/p/netstressng/wiki/Home/
Ingres Dynamic Playback is a tool to play back prerecorded SQL statements to do functional and stress testing on the Ingres Database. More information here: http://community.ingres.com/wiki/Dynamic_Playback
This project contains test software and tutorials for Analog Devices' Blackfin DSP device drivers and system services. See the wiki (in the menu bar above) for more info.
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Built on open standards like Prometheus and OpenTelemetry, Grafana Cloud includes Kubernetes Monitoring, Application Observability, Incident Response, plus the AI-powered Grafana Assistant. Get started with our generous free tier today.
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. ...