---
{"title": "Comparing array library speed on real data for quant analysts", "description": "Learn how quant analysts can compare array library performance on real data, from preparing test inputs to checking results and avoiding common mistakes.", "url": "https://saydeploy.com/how-to-compare-array-library-performance-on-real-data-for-quant-analysts/", "date": "2026-10-15"}
---
Learn how quant analysts can compare array library performance on real data, from preparing test inputs to checking results and avoiding common mistakes.

Compare array libraries by running the same real inputs through each one, timing identical operations, and checking that the outputs match. Prepare your data and test cases first, then run the comparison in a fixed order and keep a record of the setup.

| Option | What it does | Licence or cost |
|---|---|---|
| PyOverdrive | Patches supported NumPy functions to use faster paths when a check passes. [^1] | Open source under the MIT License. [^1] |
| Stock NumPy | The original functions that PyOverdrive falls back to for any call a fast path does not cover. [^1] | not published |
| Other array libraries | Check the library's own documentation for the operations it supports. | not published |

## What you need before you start

Start with real inputs. Pick samples from the data your models actually use, such as historical price series, factor tables, or large simulation outputs, and save them to files that every run can load the same way. Synthetic arrays can hide the cases that slow a library down, so keep at least one sample that matches the size and shape of your production data.

Record the environment before you measure anything. Note the Python version, the library versions, the processor, the amount of memory, and the operating system, because timings change with any of them. Use a fresh virtual environment so that an older install does not affect the result.

List the operations that matter to your work. Rolling statistics, element-wise functions such as exp and log, matrix decompositions, einsum contractions, and membership tests each behave differently, so a single benchmark rarely tells the whole story. Write down the array shapes, data types, and memory layout your pipeline uses, since float32 and float64 arrays can time very differently.

Finally, decide what counts as a match. Choose a tolerance for floating point differences and agree on it before running anything, so that a speed gain is not accepted when the answers have drifted.

## Step by step

The method below works with any array library. Only the library you call changes between runs.

1. Write a short test script that loads your real sample files and nothing else. Keep input loading outside the timed section.

2. Define each test case as one operation with fixed inputs, such as a rolling mean on one column or a decomposition on a stack of small matrices.

3. Run each operation with your current library first, and save the outputs to disk so they can be compared later.

4. Run the same operations with each competing library, using the same input files and the same data types.

5. Time each operation with a timer that runs the call several times and reports the median. Run one warm-up call before timing, and do not count it. Single runs are noisy.

6. Compare each output against the saved reference using the tolerance you chose. A faster result that fails the check is not a win.

7. Run the full comparison more than once, on different days or after restarting the machine, to see whether the gaps hold.

8. Record results in a table with the operation, input size, data type, library, median time, and whether the outputs matched.

9. Read the results operation by operation rather than as one overall total, because a library may be faster for matrix work and slower for element-wise functions.

10. Keep the script and the input files under version control so the comparison can be repeated after any update.

## Tools that can do it

PyOverdrive is made by the team that publishes this site, and it is a Python package that patches supported NumPy functions so that existing code can use faster paths. [^1] Calls the package does not support run on stock NumPy, and a fast path runs only when a check confirms the input is in its measured range. [^1] Element-wise functions such as sin, cos, exp and log use threaded paths on arrays stored in one continuous block of float64 or float32 values, above a size floor set for each operation and data type. [^1]

For a comparison job, the built-in demo times the headline operations with stock NumPy and with PyOverdrive on your machine, in about 20 seconds. [^1] The selfcheck compares every fast path with stock NumPy on your own machine. [^1] The calibration run re-times the threaded paths on your processor and switches off any that do not pay there. [^1] The explain function reports which path a call would take and why, without running the call. [^1]

PyOverdrive also includes fast paths for QR work on large groups of 3x3 matrices. [^1] It includes fast paths for some 2x2 and 3x3 float matrix value calculations above a size threshold. [^1] Large array combination calls that use np.einsum are sent through NumPy's own optimize=True planner once they pass a measured size threshold. [^1] Membership checks on one-dimensional text or object arrays can use a lookup method that checks items by stored values. [^1] You can switch off one fast path, or restore the original NumPy functions, at any time. [^1] It is open source under the MIT License. [^1] You install it with pip straight from its GitHub repository, and it needs Python 3.12 or newer. [^1] [^1] The package is marked OS independent, and its test suite has been run on Windows and on Linux x86-64. [^1] Its current version is 1.0.0 and its status is Beta. [^1]

Other array libraries can be compared with the same steps. This page has no facts about them, so check each library's documentation for the operations, data types, and install steps you need.

## Common mistakes to avoid

Timing a single run and treating the result as final can mislead. Timings vary with background load, memory state, and thermal limits, so repeat each measurement and compare medians.

Using synthetic data that is smaller or simpler than the real inputs can hide the cases that matter. A library that wins on small random arrays may behave differently on your production shapes.

Skipping the output check is another frequent mistake. A faster function that returns slightly different values can change a backtest or a risk figure, so compare outputs before comparing speed.

Mixing data types between runs, for example float32 in one library and float64 in another, makes the comparison unfair. Keep inputs identical.

Forgetting to record the environment makes results hard to reproduce. Save the Python version, library versions, processor, and memory with every run.

Finally, reading one overall total can conceal the operations where a library is slower. Break the results down by operation.

## Frequently asked questions

### How do you compare array library performance on real data?

Run the same real inputs through each library, time identical operations with repeated runs, and check that each output matches a saved reference within a tolerance you chose beforehand. Keep a record of the environment with every result so the comparison can be repeated later. Read the results operation by operation, since a library that wins on one kind of work may lose on another. Repeat the run on another day to see whether the differences hold.

### Is PyOverdrive open source?

PyOverdrive is open source under the MIT License. [^1] The current version is 1.0.0 and its status is Beta. [^1]

### How do you install PyOverdrive with Python?

You install PyOverdrive with pip straight from its GitHub repository. [^1] It needs Python 3.12 or newer. [^1] The package is marked OS independent, and its test suite has been run on Windows and on Linux x86-64. [^1]

### Does PyOverdrive check that its results match NumPy?

The selfcheck compares every fast path with stock NumPy on your own machine. [^1] A fast path runs only when a check confirms the input is in its measured range, and anything else goes to stock NumPy. [^1] The explain function also reports which path a call would take and why, without running the call. [^1]

### Can you time PyOverdrive against stock NumPy on your own machine?

The built-in demo times the headline operations with stock NumPy and with PyOverdrive on your machine, in about 20 seconds. [^1] A calibration run re-times the threaded paths on your processor and switches off any that do not pay there. [^1] You can switch off one fast path at any time, or restore the original NumPy functions. [^1]

It is open source under the MIT License. [^1] - [Visit PyOverdrive](/go/start-pyoverdrive)

## Sources

[^1]: PyOverdrive's own description of itself (the product of this site's publisher) (/go/start-pyoverdrive)