<ul data-eligibleForWebStory="true"><li>A benchmark program was created in RPython to test the speed of object allocation.</li><li>Instances of class A with an integer field were allocated and kept alive in a tight loop.</li><li>The RPython static optimizer's effect on object allocation was addressed by carefully managing object instances.</li><li>Measurements were taken to calculate memory usage and allocation speed on a 64-bit architecture.</li><li>Comparison with the Boehm GC highlighted differences in allocation efficiency and mechanism complexity.</li><li>Performance stats were obtained using 'perf' tool to analyze cache, cycles, instructions, and other metrics.</li><li>Details about RPython GC's minor collections and nursery size determination were discussed.</li><li>The machine code generated by the RPython GC's allocation fast path was analyzed and explained.</li><li>Running the benchmark as a regular Python code on PyPy and analyzing the JIT-generated machine code were also discussed.</li><li>Overall, the RPython GC's allocation fast path showed efficient allocation rates, and the importance of CPU architecture in performance was highlighted.</li></ul>

PyPy: How fast can the RPython GC allocate?

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