![]() We share the results with our audience when announcing the support for a new data format. When introducing support for new data formats, we strive to compare the figures achieved in our tools against theoretical limits on as many different hardware configurations as possible. Such tests not only allow us to highlight the improved efficiency resulting from algorithm optimization but also reveal potential performance inconsistencies across different hardware configurations. These benchmarks are performed using various hardware setups, focusing solely on a single algorithm. We compare the performance of the current (optimized) algorithm with its previous iteration, aiming to identify any enhancements but also inconsistencies across different hardware configurations. One of the key purposes of conducting performance tests is to gather data on the performance improvements achieved by internal optimizations. When benchmarking password recovery speeds, we pursue a diverse set of objectives. Internally, we do a much more thorough and comprehensive testing than could be fit ingo any reasonable graphical representation. While aesthetically pleasing graphs and benchmarks showcasing our products’ performance are readily available to our blog readers and website visitors, these nice looking graphs are not the sole objective. Today, we delve deeper into the objectives and methodologies behind our password cracking speed tests. However, these graphical representations merely scratch the surface of a much broader scope. ![]() These tests yield results that are visually represented through graphs clearly demonstrating the performance of our products. It is a customary practice to gauge the speed of attacks on various data formats using diverse hardware configurations. In the realm of password recovery, benchmarking the speed of attacks holds significant importance.
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