Rust Performance Benchmarking Tool

Performance benchmarking is crucial for optimizing code and ensuring efficiency. This challenge asks you to implement a simple, command-line performance benchmarking tool in Rust. The tool should allow users to define a function to benchmark, run it multiple times, and report the average execution time.

Problem Description

You are tasked with creating a Rust program that benchmarks the execution time of a given function. The program should accept the function to benchmark as an argument (passed as a string representing the function name), the number of iterations to run the function, and optionally, a set of input arguments to pass to the function. The program should then execute the function the specified number of times, measure the total execution time, and calculate the average execution time per iteration. The result should be printed to the console in a clear and readable format.

Key Requirements:

• Function Input: The program must accept the function to benchmark as a string. This string will be used to dynamically call the function.
• Iteration Control: The program must accept the number of iterations as an integer.
• Input Arguments (Optional): The program should optionally accept a variable number of arguments to pass to the function being benchmarked. These arguments should be parsed and passed correctly.
• Time Measurement: The program must accurately measure the execution time of the function.
• Average Calculation: The program must calculate and display the average execution time per iteration.
• Error Handling: The program should handle potential errors gracefully, such as invalid function names, incorrect argument types, or other runtime exceptions.
• Clear Output: The output should be formatted clearly, displaying the function name, number of iterations, and average execution time.

Expected Behavior:

The program should take the following command-line arguments:

1. function_name: A string representing the name of the function to benchmark.
2. iterations: An integer representing the number of times to run the function.
3. arg1 arg2 ...: (Optional) A variable number of arguments to pass to the function. The types of these arguments should be inferred based on the function's signature.

Edge Cases to Consider:

• Invalid Function Name: The specified function does not exist.
• Incorrect Argument Types: The provided arguments do not match the function's signature.
• Zero Iterations: The user specifies zero iterations.
• Very Large Number of Iterations: Consider potential performance implications of extremely large iteration counts.
• Functions with Side Effects: The benchmarking tool should ideally measure the pure execution time of the function, minimizing the impact of side effects.

Examples

Example 1:

Input: benchmark add 1000000
Output: Function: add, Iterations: 1000000, Average Time: 1.2345ms
Explanation: The `add` function is benchmarked 1,000,000 times. The average execution time is 1.2345 milliseconds.

Example 2:

Input: benchmark calculate_area 500000 5.0 10.0
Output: Function: calculate_area, Iterations: 500000, Average Time: 0.5678ms
Explanation: The `calculate_area` function is benchmarked 500,000 times with arguments 5.0 and 10.0. The average execution time is 0.5678 milliseconds.

Example 3: (Edge Case)

Input: benchmark non_existent_function 1000
Output: Error: Function 'non_existent_function' not found.
Explanation: The program handles the case where the specified function does not exist.

Constraints

• Function Signature: The functions to be benchmarked must be fn functions that take no arguments or a variable number of arguments.
• Iteration Count: The number of iterations must be a positive integer.
• Execution Time: The average execution time should be reported in milliseconds (ms) with up to 4 decimal places.
• Performance: The benchmarking process itself should not significantly impact the accuracy of the measurements. Avoid unnecessary allocations or complex operations within the benchmarking loop.
• Error Handling: Provide informative error messages to the user.

Notes

• You'll need to use Rust's reflection capabilities (e.g., std::any::Any) to dynamically find and call the function. Consider using a HashMap to store available functions for efficient lookup.
• The std::time::Instant struct is useful for measuring execution time.
• Think about how to handle different argument types safely and efficiently. Consider using a macro to simplify argument parsing.
• This is a simplified benchmarking tool. Real-world benchmarking tools often incorporate more sophisticated techniques, such as statistical analysis and warm-up iterations. Focus on the core functionality for this challenge.
• Consider using the clap crate for command-line argument parsing. This is not required, but it can simplify the process.