Search Results for "python data analysis"

PerfView is a free performance analysis tool that helps isolate CPU and memory-related performance issues. It is a Windows tool, but it also has some support for analyzing data collected on Linux machines. It works for a wide variety of scenarios, but has a number of special features for investigating performance issues in code written for the .NET runtime. If you are unfamiliar with PerfView, there are PerfView video tutorials.

...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. The system supports multiple data backends and ingestion pipelines to collect data from JVM, C/C++, Python, and other ecosystems. With the collected data, Cat supports analysis of hotspots, trending anomalies, and capacity planning to drive continuous reliability improvements.

Hiper is an open-source command-line tool designed for statistical analysis of web performance and page load behavior during performance testing. The tool repeatedly loads a specified webpage multiple times and gathers detailed timing metrics in order to produce more reliable performance measurements than single-run benchmarks. By averaging data across multiple page loads, Hiper helps developers understand whether performance optimizations actually improve real-world page loading behavior. ...

AwkStatistics is a script which is used to generate statistical data for csv data by using awk. It takes two columns from the input file, one is used to identify the row(option -i) and the other one is the column where the values are taken from (option -v). The script was designed for performance engineers to be able to analyze CSV-Data on the fly and have a good possibility to automate the analysis process.

A collection of software benchmarks developed to compare the performance of Java with C on identical code. No language libraries were used to avoid implementation differences. Some of the benchmarks are also implemented in Python and Scala. There are benchmarks for bit twiddling, numerical computing, data structure manipulation, concurrent computing, callouts to native libraries, and, graphics processing units (GPU) utilization.