Efficient profiling techniques can help dramatically improving the performance of your Python* code by detecting time, CPU, and memory bottlenecks. This session discusses the need, advantages, and common tools and techniques for profiling Python applications, followed by a demo of Intel® VTune Amplifier and its capabilities to profile both pure Python code and code heavily relying on C extensions.
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Benchmark results were obtained prior to the implementation of recent software patches and firmware updates intended to address exploits referred to as "Spectre" and "Meltdown". Implementation of these updates may make these results inapplicable to your device or system.
Software and workloads used in performance tests may have been optimized for performance only on Intel microprocessors. Performance tests, such as SYSmark and MobileMark, are measured using specific computer systems, components, software, operations and functions. Any change to any of those factors may cause the results to vary. You should consult other information and performance tests to assist you in fully evaluating your contemplated purchases, including the performance of that product when combined with other products. For more information, see Performance Benchmark Test Disclosure.