Why is Python 150X slower than C?

Python's performance is significantly slower than C due to factors including the overhead of interpreted languages, runtime checks, and a lack of compiler optimizations. The difference in speed can be as high as 150 times, especially in cases like running a billion nested loops. While other languages like JavaScript offer similar performance to C in certain situations, Python falls behind largely because its interpreter must process each line of code as it runs, resulting in slower execution.

Language Performance

• 00:00:00 The visual shows C and Rust as the fastest languages for running a billion nested loops, with Python being the slowest. Python is about 150 times slower than C in this specific benchmark, due to multiple reasons discussed in this video. JavaScript demonstrates relatively good performance in this specific test case compared to Python, although it's not as fast as C.

Interpreted vs. Compiled

• 00:00:48 The video explains the concept of interpreted and compiled languages, where compiled languages are generally faster because the code is pre-processed into machine code before execution. Python is interpreted, while C is compiled, which contributes to Python's slower performance. The video briefly touches upon JIT-compiled languages (JavaScript and Java) that are closer to C and C++ in performance.

Runtime Checks and Guardrails

• 00:05:00 The speaker explains how languages like JavaScript and Java have built-in mechanisms to check for errors and ensure safe memory access, e.g., returning 'undefined' for out-of-bounds array access. C does not have these 'guardrails', and relies on the operating system to manage potentially unsafe operations, leading to potential crashes. These runtime checks add overhead to languages like JavaScript and Java but are necessary for increased security and predictable behavior.

Assembly and Opcode

• 00:02:30 All high-level languages eventually get translated into assembly and then machine code (opcodes) that the CPU can understand and execute. Each language has its own way of generating these intermediate representations, including how much optimization is done during the compilation/interpretation process. The speaker describes how C's compiled code and potentially high-level optimization in compilers like O3 can generate more efficient opcodes, resulting in faster execution compared to interpreted languages such as Python.

Python's Performance Bottlenecks

• 00:09:04 Python's slow performance, especially compared to C, is attributed to its interpreted nature and the need for the CPython runtime to process each line of code as it's executed. While there are implementations of Python that can improve performance, in general, the overhead of interpreted execution creates a noticeable performance penalty for complex tasks like the billion nested loop benchmark. The lack of extensive compile-time optimization further contributes to slower performance.