Python Overheads Using Ackermann Function Benchmark: Performance and Power Consumption
The main focus of this manuscript is on the impact of running Python codes in two different environments. Firstly, the Python Integrated Development and Learning Environment (IDLE), and secondly, running the same scripts on Windows Command Prompt (CMD). Due to its complexity and demanding nature, on...
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| Vydáno v: | Integrated STEM Education Conference (Online) s. 1 - 4 |
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15.03.2025
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| ISSN: | 2473-7623 |
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| Abstract | The main focus of this manuscript is on the impact of running Python codes in two different environments. Firstly, the Python Integrated Development and Learning Environment (IDLE), and secondly, running the same scripts on Windows Command Prompt (CMD). Due to its complexity and demanding nature, one of the best-known benchmark procedures to inspect the resource bounds is the Ackermann function. This study investigates the differences in hardware and software resource consumption between two execution methods. The first method suggests executing the Ackermann function using a 32-bit Python 3.12.7 interpreter, and the second method would be its execution in the 64-bit CMD using the same 32-bit Python interpreter. The current paper investigates the overhead of Python's IDLE in terms of time and memory usage compared to executing the script under test from the command line interpreter, and how all this add further exploited resources (Overheads). Furthermore, the present work witnesses the power consumption of these two methods. Finally, the results show an insignificant time consumption of the CMD method compared to the first method (using IDLE), For instance, the IDLE takes 3 hours and 31 minutes while the CMD method took only 70 seconds to compute Ackermann A(3,6). Moreover, the optimal power and RAM usage is spotted using the CMD execution method. |
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| AbstractList | The main focus of this manuscript is on the impact of running Python codes in two different environments. Firstly, the Python Integrated Development and Learning Environment (IDLE), and secondly, running the same scripts on Windows Command Prompt (CMD). Due to its complexity and demanding nature, one of the best-known benchmark procedures to inspect the resource bounds is the Ackermann function. This study investigates the differences in hardware and software resource consumption between two execution methods. The first method suggests executing the Ackermann function using a 32-bit Python 3.12.7 interpreter, and the second method would be its execution in the 64-bit CMD using the same 32-bit Python interpreter. The current paper investigates the overhead of Python's IDLE in terms of time and memory usage compared to executing the script under test from the command line interpreter, and how all this add further exploited resources (Overheads). Furthermore, the present work witnesses the power consumption of these two methods. Finally, the results show an insignificant time consumption of the CMD method compared to the first method (using IDLE), For instance, the IDLE takes 3 hours and 31 minutes while the CMD method took only 70 seconds to compute Ackermann A(3,6). Moreover, the optimal power and RAM usage is spotted using the CMD execution method. |
| Author | Salhi, Abdenacer Es Ghabzouri, Mohammad El |
| Author_xml | – sequence: 1 givenname: Mohammad El surname: Ghabzouri fullname: Ghabzouri, Mohammad El email: elghabzouri.mad@gmail.com organization: Mohammed I University,Faculty of Sciences,LESPRE Department of Physics,Morocco – sequence: 2 givenname: Abdenacer Es surname: Salhi fullname: Salhi, Abdenacer Es organization: Mohammed I University,Faculty of Sciences,LESPRE Department of Physics,Morocco |
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| Snippet | The main focus of this manuscript is on the impact of running Python codes in two different environments. Firstly, the Python Integrated Development and... |
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| SubjectTerms | Ackermann Function Benchmark Benchmark testing Energy consumption Hardware Optimal Energy Consumption Optimization Power demand Python Python IDLE Overheads Random access memory Resource management Software Time complexity Time Complexity Optimization Windows Command Prompt |
| Title | Python Overheads Using Ackermann Function Benchmark: Performance and Power Consumption |
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