#Programming #Python #C++ #Threads #PerformanceImprovement 🚀
Are you struggling with a slow program that’s taking forever to process data? If you’re like many programmers, you may have encountered the challenge of optimizing your code for improved speed and efficiency. In this article, we’ll explore some effective strategies to make your program faster and more responsive, especially when dealing with large datasets and complex calculations.
## Assessing the Current Performance
Before diving into optimization techniques, it’s crucial to understand the current limitations and bottlenecks in your program’s performance. In the case of processing user scores for a large online community, it’s evident that the current program is taking an excessive amount of time to complete the task. With 8.5 million users and a processing time of 77 seconds for every 150 scores, the prospect of completing the task within 50-70 days seems daunting.
### Identifying Potential Solutions
1. **Language Switch**
One potential solution to address the slow processing speed is to consider migrating from Python to C++. While Python is known for its simplicity and ease of use, C++ is renowned for its high performance and efficiency in handling complex computations. By leveraging the speed and optimization capabilities of C++, you could significantly reduce the processing time for large datasets.
2. **Implementing Multithreading**
Another effective approach to improve program speed is to incorporate multithreading into the code. By utilizing threads, you can parallelize the computation tasks, allowing multiple threads to execute simultaneously and distribute the workload across available CPU cores. This concurrent processing can lead to a substantial reduction in overall processing time, especially when handling massive amounts of data.
### Evaluating the Potential Impact
Now, let’s address the key question: How much faster would the program run by switching to C++ and integrating multithreading? While the exact speedup may vary based on the specific implementation and hardware resources, it’s reasonable to expect a significant improvement in performance.
### Leveraging External Resources
In addition to optimizing the code itself, consider leveraging external resources to further enhance the program’s speed and scalability. Hosting the computation tasks on a dedicated server with ample processing power and memory can minimize the impact on your local machine and expedite the data processing.
### Practical Considerations and Next Steps
As you contemplate these potential improvements, it’s essential to balance the trade-offs and practical considerations involved. While C++ and multithreading offer promising avenues for performance enhancement, they also entail learning curves and additional development efforts. Moreover, ensure that you thoroughly test and benchmark the optimized code to validate the expected performance gains before committing to substantial time and resources.
## Conclusion
In conclusion, optimizing the performance of your program involves a combination of strategic decisions, thoughtful implementation, and thorough testing. By transitioning to a high-performance language like C++, integrating multithreading, and leveraging external resources, you can significantly expedite the processing of user scores and achieve your project goals within a shorter timeframe.
In the spirit of collaboration and knowledge sharing, consider seeking guidance and insights from experienced developers who can provide valuable feedback on your project. You can also share your GitHub repository to engage with the programming community and receive constructive input.
As you embark on this journey to make your program faster, remember that every optimization effort brings you closer to achieving efficient and impactful solutions in your programming endeavors. Good luck, and may your code run faster than ever before!
[GitHub Repository: Voltaic Distribution Calculator](https://github.com/ballooner/VoltaicDistributionCalculator/tree/main)
The biggest thing that’s going to change your efficiency is optimizing the operations themselves. You’re doing a lot of work to yield those results. Python itself while slower than C++ isn’t necessarily the problem. A lot of the python libraries are actually written in C++ with the API for them written in python. So the actual amount available to gain there isn’t as substantial as you might expect.
You’ve got functions like “getIntermediateEnergy” that are using nested for loops with a bunch of math and operations. I can’t take the time to look at how needed that actually is, and while I don’t have time to do a deep dive and optimize it for you I can definitely say that the biggest thing will be finding the most efficient way to perform every operation.
This is actually the type of thing where LeetCode skills shine.
You should profile your code and see *which sections* are taking up most of the time, which will tell you where you need to optimize. You can do this manually by adding lots of calls to `time.time()`, or you can use [Python’s built-in profiler](https://docs.python.org/3/library/profile.html).
Just from a quick glance through your code, I don’t see anything that looks like the actual *computation* should be slow. I think it’s highly likely that the only slow part of your code is the call you’re making to the third-party API you’re using. And if that’s the case, then optimizing or rewriting in a “faster” language won’t make any difference. Your options are to look for another way to download the data in larger batches, or to speed up the download by doing lots of requests in parallel (and hope they don’t block you for abuse).
But like I said, the only way to know for sure is to actually measure it.
Optimization is the art of doing less. You need to think carefully about how you can take advantage of certain things you know about the information you processing and the math you trying to do to simply run less code.
For example: multiplication can be done by adding a number over and over in a loop. You want to look for places where you’re looping when there’s another way to calculate the same answer using some kind of mathematical equivalency.
You want to make sure you’re not calculating a bunch of data you’ll never actually need either. If you only need a certain piece of data about a player in certain situations, calculate it on the spot when you need it, don’t pre-calculate it for everyone.
Also, keep a look out for places where your are unnecessarily calculating the same thing more than once, those are prime candidates for memoization.
These are all general tips that apply to essentially any optimization situation.
I can’t immediately see anything that jumps out to me here, because I simply don’t know the math and data your working with well enough to know how to optimize it, but I see at least one way you can reduce the code complexity a bit.
Your `getXyzEnergy` functions look like they could be merged into a single function.
If the only difference is the thresholds, you simply make one function with all 3 sets of thresholds in it and add a parameter which determines which set of thresholds you’re supposed to use. That way you don’t need to manually modify 3 different functions of you find a way to simplify or optimize that function.
Also, APIs tend to be rate limited to avoid denial of service by simply swamping the API with requests, so even a multithreaded optimized c++ version is likely limited by that rate anyway. That said, it appears this script is written in a way where it could be run using Python’s [multiprocessing](https://docs.python.org/3/library/multiprocessing.html) which is more or less the equivalent to multithreading in c++. So assuming you don’t get rate limited to hell you could consider trying to run a few of these in parallel for an overall speedup even if you can’t optimize the actual processing a refile lot. You might need to spend some time figuring out how to schedule it so it doesn’t just slam them with requests though.
Your problem is actually that you are using too much nested loops. Take for example function `getScores` You have 2 nested for loops in that function and that function is called from inside of yet another 2 nested loops.
that is quadratic complexity! No wonder this works slow.
Assuming that this is highest complexity and that you do not have more nested loops, your program will run with speed of `O(scenArray * scenarios * 100 (I’m assuming this is number of users) * array (leaderboard))`
Try measuring the time it takes for 1 pass to complete. Then use that time to calculate the time it would take for n users to finish. Use highest numbers for `scenArray`, `scenarios` and `array (leaderboard)` that you expect to have.
Note that `scenArray`, `scenarions` and `array (leaderboard)` that I write about is actually count of those arrays.
To optimize this program you should think long and hard how you can “flatten” those loops so that they would not be nested anymore. Perhaps you can calculate some stuff prior and use dictionaries or hash sets to reference data you need (dictionary and hash set both has O(1) time complexity for access so they are optimal for use inside loop).
Depends what the problem is.
Optimization is mostly about eliminating loops and unnecesary work. If it’s mostly network time there aint much you can do unless the server allows you make ‘bulk’ calls, fetching more than one at a time.
I’m sorry that this is unrelated to your question, but dude, I’ve been hoping someone would compile statistics on voltaic ranks for ages! Thank you for making this, i hope it turns out well
Can you split the users into groups and parallelize the work, then finally reduce them into a single result pool?
If you can make an API call that gets all your data and stores it in a file in a single or a handful of calls, then having your code access that file, it will be a lot quicker.
Space is cheap, time is expensive. Once it’s done if you have redundant data you can always delete it or replace it when a new API call is made.
You could use a producer-consumer pattern to have a bunch of threads performing requests concurrently and a bunch of processes processing the data in parallel, this is possible in Python.
Also learn to work with dataframes, you’re wasting time rearanging information you already have into less efficient data structures.
Are you making a graphql request for everyone of the 8.5 million players? If you are I would try to get a bunch of players at once.