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certik merged 3 commits into from
Jul 28, 2023
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Add authors and fix typo in **Conclusion** #16

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Add authors and typo fix
czgdp1807 Jul 28, 2023
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add two extra lines
Smit-create Jul 28, 2023
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Merge branch 'main' into fixes
certik Jul 28, 2023
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32 changes: 30 additions & 2 deletions content/blog/lpython_mvp.md
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Expand Up @@ -2,7 +2,7 @@
title: "LPython: Novel, Fast, Retargetable Python Compiler"
date: 2023-07-28
tags: ["Python", "Announcement"]
author: "[Ondřej Čertík](https://ondrejcertik.com/), [Brian Beckman](https://www.linkedin.com/in/brianbeckman), [Gagandeep Singh](https://github.com/czgdp1807), [Thirumalai Shaktivel](https://www.linkedin.com/in/thirumalai-shaktivel/), [Smit Lunagariya](https://www.linkedin.com/in/smit-lunagariya-356b93179/), [Ubaid Shaikh](https://Shaikh-Ubaid.github.io/), [Naman Gera](https://github.com/namannimmo10), [Pranav Goswami](https://www.linkedin.com/in/pranavgoswami1/), [Rohit Goswami](https://rgoswami.me), [Dominic Poerio](https://github.com/dpoerio), [Akshānsh Bhatt](https://github.com/akshanshbhatt)"
author: "[Ondřej Čertík](https://ondrejcertik.com/), [Brian Beckman](https://www.linkedin.com/in/brianbeckman), [Gagandeep Singh](https://github.com/czgdp1807), [Thirumalai Shaktivel](https://www.linkedin.com/in/thirumalai-shaktivel/), [Smit Lunagariya](https://www.linkedin.com/in/smit-lunagariya-356b93179/), [Ubaid Shaikh](https://Shaikh-Ubaid.github.io/), [Naman Gera](https://github.com/namannimmo10), [Pranav Goswami](https://www.linkedin.com/in/pranavgoswami1/), [Rohit Goswami](https://rgoswami.me), [Dominic Poerio](https://github.com/dpoerio), [Akshānsh Bhatt](https://github.com/akshanshbhatt), [Virendra Kabra] (https://www.linkedin.com/in/virendrakabra/)"
type: post
draft: false
---
Expand Down Expand Up @@ -718,6 +718,8 @@ We compare JIT compilation of LPython to Numba on **summation of all the element
| Python | 3.10.4 |

<br/>


<!-- Add your systems like System - Linux, System - Windows, etc and then add the results for your System in the tables that follow -->

**Summation of all the elements of a 1-D array**
Expand Down Expand Up @@ -779,6 +781,8 @@ test()

<br/>



| Compiler | Execution Time (s) | System | Relative |
|---|---|---|---|
| LPython | 0.013 | Apple M1 MBP 2020 | 1.00 |
Expand All @@ -791,6 +795,8 @@ test()
| Numba | 0.048 | AMD Ryzen 5 2500U (Ubuntu 22.04) | 1.00 |

<br/>


<!-- TODO: Write a summary of the results -->

**Pointwise multiplication of two 1-D arrays**
Expand Down Expand Up @@ -846,6 +852,8 @@ test()

<br/>



| Compiler | Execution Time (s) | System | Relative |
|---|---|---|---|
| Numba | 0.041 | Apple M1 MBP 2020 | 1.00 |
Expand All @@ -858,6 +866,8 @@ test()
| LPython | 0.21 | AMD Ryzen 5 2500U (Ubuntu 22.04) | 1.00 |

<br/>


<!-- TODO: Write a summary of the results -->

**Insertion sort on lists**
Expand Down Expand Up @@ -929,6 +939,8 @@ test()

<br/>



| Compiler | Execution Time (s) | System | Relative |
|---|---|---|---|
| LPython | 0.11 | Apple M1 MBP 2020 | 1.00 |
Expand All @@ -941,6 +953,8 @@ test()
| Numba | 0.36 | AMD Ryzen 5 2500U (Ubuntu 22.04) | 3.60 |

<br/>


<!-- TODO: Write a summary of the results -->

**Quadratic-time implementation of the Dijkstra shortest-path algorithm on a fully connected graph**
Expand Down Expand Up @@ -1065,6 +1079,8 @@ test()

<br/>



| Compiler | Execution Time (s) | System | Relative |
|---|---|---|---|
| LPython | 0.23 | Apple M1 MBP 2020 | 1.00 |
Expand All @@ -1077,6 +1093,8 @@ test()
| Numba | 1.95 | AMD Ryzen 5 2500U (Ubuntu 22.04) | 2.24 |

<br/>


<!-- TODO: Write a summary of the results -->

### Ahead-of-Time (AoT) Compilation
Expand All @@ -1095,6 +1113,8 @@ Next, we see how LPython compares to other AoT compilers and to the standard CPy

<br/>



**Quadratic-time implementation of the Dijkstra shortest-path algorithm on a fully connected graph**

```python
Expand Down Expand Up @@ -1235,6 +1255,8 @@ int main() {

<br/>



Note the optimization flags furnished to each compiler.

| Compiler/Interpreter | Optimization flags used |
Expand All @@ -1246,6 +1268,8 @@ Note the optimization flags furnished to each compiler.

<br/>



<!-- TODO: Write a summary of the results -->

**Floyd-Warshall algorithm on array representation of graphs**
Expand Down Expand Up @@ -1347,6 +1371,8 @@ int main() {
| Python | 440.588 | AMD Ryzen 5 2500U (Ubuntu 22.04) | 633.94 |

<br/>


<!-- TODO: Remove above line if the following table looks good.-->

Note the optimization flags furnished to each compiler.
Expand All @@ -1359,6 +1385,8 @@ Note the optimization flags furnished to each compiler.
| Python | - |

<br/>

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@Thirumalai-Shaktivel Thirumalai-Shaktivel Jul 28, 2023

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Yup, this might work, Let's see!


<!-- TODO: Write a summary of the results -->

### Interoperability with CPython
Expand Down Expand Up @@ -1599,6 +1627,6 @@ Done.

## Conclusion

The benchmarks support the claim that LPython is competitive with its competitors in all features it offers. In JIT, the execution times of LPython-compiled functions are at least as short as equivalent Numba functions.The speed of JIT compilation, itself, is slow in some cases because it depends on a C compiler to generate optimal binary code. For algorithms with rich data structures like `dict` (hash maps) and `list`, LPython shows much faster speed than Numba. In AoT compilation for tasks like the Dijkstra algorithm, LPython beats equivalent C++ code very comfortably. For an array-based implementation of the Floyd-Warshall algorithm, LPython generates code almost as fast as doess C++.
The benchmarks support the claim that LPython is competitive with its competitors in all features it offers. In JIT, the execution times of LPython-compiled functions are at least as short as equivalent Numba functions.The speed of JIT compilation, itself, is slow in some cases because it depends on a C compiler to generate optimal binary code. For algorithms with rich data structures like `dict` (hash maps) and `list`, LPython shows much faster speed than Numba. In AoT compilation for tasks like the Dijkstra algorithm, LPython beats equivalent C++ code very comfortably. For an array-based implementation of the Floyd-Warshall algorithm, LPython generates code almost as fast as C++ does.

The main takeaway is that LPython/LFortran generate fast code by default. Our benchmarks show that it's straightforward to write high-speed LPython code. We hope to raise expectations that LPython output will be in general at least as fast as the equivalent C++ code. Users love Python because of its many productivity advantages: great tooling, easy syntax, and rich data structures like lists, dicts, sets, and arrays. Because any LPython program is also an ordinary Python program, all the tools -- debuggers and profilers, for instance -- just work. Then, LPython delivers run-time speeds, even with rich data structures at least as short as alternatives in most cases. In the future, LPython will allow user-defined implementations of data structures for those rare cases where the versions shipped with LPython are not good enough.