⚡️ Speed up method SimpleModel.predict by 9,155,806%
#470
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Here is an optimized version of your program. The main bottlenecks were. - The `sleep(10)` call was likely used for simulation, but if you want faster execution, it should be removed or reduced. - The nested loop (`for i in range(500)` over `for x in data`) repeatedly calculates the same value for `x * i**2` and accumulates in `computation`, but `computation` is always just `x * i**2` because it's initialized to 0 each iteration. - Precomputing values and using list comprehensions speeds up execution significantly. Optimized code. **Changes made:** - Removed or commented out `sleep(10)`. - Precomputed `i**2` to avoid recalculating it. - Eliminated unnecessary variable and reduced loop nesting overhead. This will run **much** faster while returning the same value as the original.
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📄 9,155,806% (91,558.06x) speedup for
SimpleModel.predictincode_to_optimize/code_directories/simple_tracer_e2e/workload.py⏱️ Runtime :
10.0 seconds→109 microseconds(best of57runs)📝 Explanation and details
Here is an optimized version of your program. The main bottlenecks were.
sleep(10)call was likely used for simulation, but if you want faster execution, it should be removed or reduced.for i in range(500)overfor x in data) repeatedly calculates the same value forx * i**2and accumulates incomputation, butcomputationis always justx * i**2because it's initialized to 0 each iteration.Optimized code.
Changes made:
sleep(10).i**2to avoid recalculating it.This will run much faster while returning the same value as the original.
✅ Correctness verification report:
To edit these changes
git checkout codeflash/optimize-SimpleModel.predict-mcl3x1w7and push.