You're facing performance issues with a new algorithm framework. How will you overcome them effectively?

A structured and methodological approach is essential to identify and address the root cause efficiently -Identify Performance Bottlenecks: Use performance profiling tools to analyze which parts of the algorithm are consuming the most resources (CPU,memory,I/O) -Compare the algorithm's current performance against expected benchmarks and establish clear metric -Optimize Critical Sections: Review time & space complexity of algorithms.Identify if more efficient data structure/algorithm can be substituted -Evaluate Framework Overhead: Explore Framework Specific Optimizations such as adjusting memory limits, optimizing garbage collection, or tweaking parallel execution configurations -Ensure that the latest version of the framework is being used

Understand what you got and where you are. Information is what you need at first. You can't optimize without information. Like optimize database, you need to understand it from the core (not the rows, the page/block is the material database use). Know how it work, how much does is cost, how many time it take... Understanding the baseline performance will give you a clear picture of the issues at hand and help you set realistic goals for improvement.

Before diving into optimization, make sure you have a clear baseline. Without it, you're flying blind, making changes without real insight. Profiling tools reveal where your code is actually slowing down—whether it's execution time, memory use, or other bottlenecks. Armed with that data, your optimizations will be precise and impactful.

To overcome performance issues with an algorithm framework, you need to: 1- Analyze execution flow to pinpoint bottlenecks. 2- Measure performance using profiling tools. 3- Optimize data structures and algorithms. 4- Minimize memory usage through caching, memorization, etc. 5- Leverage parallel processing and caching mechanisms. 6-Profile and iterate to refine optimizations.

The first thing to do here is to understand where we stand, meaning what the current performance metrics are for the algorithm in hand with the known data set. This gives us a broad idea about where we stand and what is to be achieved. We can use profiling tools like JProfiler and run that and see what the execution time is, memory usage, memory leaks and so on. This helps us in setting up goal requirements and also a clear picture of what is happening and what is to be done

You saw the picture now, right? You know every single step and how long does it takes to complete it. You can pin point where the problem is with your debug skill. You can also take advantage of other metrics, like the number or data you are processing, does it increase or reduce? Do we use multithread or any other solution? So let run some tests and apply binary search to reduce the search range (you can take advance of the rules you are making now to do it) Work with number, don't just 'look'

Remember to run profiling with the optimization flags turned on in the compiler. Otherwise you end up optimizing the wrong place. Even though it sounds trivial I've seen developers fall into the trap of profiling with optimization turned off so they didn't have to wait so long for the compiler in each iteration.

To identify bottlenecks, use profiling tools to monitor the algorithm's behavior during execution. Focus on areas where the algorithm spends the most time or consumes the most resources. Look for patterns such as high CPU usage, excessive memory consumption, or frequent I/O operations. Pay attention to specific functions or methods that are disproportionately affecting performance. Analyzing stack traces and logs can also provide insights into where the performance issues are concentrated.

To get the most relative impact in changes to your algorithm, you need to look at points in which there are dependencies and less efficient blocks. With complexity analysis, you can get an idea of how the overall algorithm is dependent on particular parts, especially when it comes to scale.

We all know the trade-off, but it's not always a trade off between time & memory... Optimize your code with all the option you had from your information you gathered. Sometimes it's customized or hardcoded for this solution only, not bring in the general-purpose algorithm. Like in Samsung, they do rewrite the lib sometime just to make use of it for a specific problem. Like do queue yourself with a fixed-size array and no circle, no addition move for value, just re-use with overwrite. Simple and correct with no addition feature.

It’s important to identify the type of bottleneck. If it’s due to the computational complexity, look for deterministic and defined parts of the code and simplify it. For example, if you’re using linear search but your data set is already sorted, you could switch to binary search. If a variable in the code always takes only a set of values, hardcode it instead of deriving it. If it’s due to frequent data operations like insertion, deletion, searching, look for efficient data structures. If it’s due to large scale data processing, use multiple channels (parallel processing), higher clocks when possible (FPGAs). If it’s due to memory, try to minimise unnecessary memory allocations, for example choosing the right data type.

We should check some points: 1. Reduce complexity! Simplify algorithms by analyzing their TC/SC. Replace inefficient ops with more efficient ones. 2. Use efficient data structures. Take collections that offer optimal access, insertion, and deletion times. 3. Specialized algos. Substitute general-purpose algos with ones specifically tailored to the problem. Custom solutions can often outperform. 4. Minimize redundant operations. Remove unnecessary calculations, loops, and checks. Consolidate multiple passes over data into a single process. 5. Leverage multi-threading. Divide tasks into independent units. Use async ops to get responsiveness. 6. Optimize resource management. Avoid over-allocation and ensure proper deallocation of resources.

Once bottlenecks are identified, start optimizing the code by focusing on the most resource-intensive parts. Refactor inefficient code, streamline algorithms, and replace suboptimal data structures with more efficient alternatives. Optimize loops and recursive functions to reduce unnecessary computations. Ensure that changes improve performance without introducing new bugs or altering the expected behavior. After optimizing, re-run benchmarks to validate that the changes have positively impacted performance.

Code optimization should be done every now and then whenever someone gets a chance. The reason being that, optimized code is better in readability and in performance. Identify how the complexity of the code can be reduced either by using proper collections/data structures according to the problem in hand or by simplifying the algorithm instead of having complex lengthy code. Tailor algorithm according to the problem statement you are trying to solve. Having a generic algorithm is all about maintaining the unwanted overhead, which you can get rid of. Have multi threading used in places where the need fits. Design algorithm such that multi threading can be implemented. This drastically improves performance.

Hardware constraints are even more pronounced with today’s AI-focused workloads. It’s like trying to run a high-resolution video game on an outdated, underpowered computer—the game struggles because the hardware can’t keep up. Similarly, AI models need robust memory and processing power to perform at their best. Upgrading your hardware or adopting scalable cloud solutions ensures that your algorithms have the resources they need to deliver top performance.

Have your algorithm written in a way that it supports scaling, take an example of Kafka consumers. Once you have consumer groups defined, it's just a matter of creating n number of consumers to consume events. With consumer group and every consumer under this consumer group will make sure that only one of them reads the event and others won't. Thus helping us scale the application. In similar terms have your algorithm designed to be Scalable. A Scalable algorithm has great performance capabilities and adding multi threading just sky rockets your performance.

If code optimizations alone do not resolve performance issues, consider scaling resources to accommodate increased load. This could involve upgrading hardware, such as adding more CPU cores or increasing memory, or deploying the algorithm in a more powerful environment. Explore parallel processing or distributed computing solutions to handle large-scale data or complex computations. Ensure that the system architecture supports scaling efficiently and that resource increases are aligned with performance needs. Monitor the impact of resource scaling to confirm improvements.

Scale Resources. In some cases, the issue may not be with the algorithm itself but with the resources allocated to it. If your algorithm requires more memory or processing power than what is available, it will naturally perform poorly. Assess your hardware and infrastructure to ensure it meets the requirements of your algorithm framework. Upgrading servers or moving to a cloud-based solution with scalable resources could be the answer to your performance woes.

Let's take a photo editing app. It struggles on older devices. The problem isn't the algorithm but limited resources. First, evaluate device constraints. Older phones have less RAM and slower CPUs, so dynamically reduce image resolution or limit concurrent threads based on available memory. If local processing fails, offload heavy tasks to the cloud. When a user applies a complex filter, send the image to a server for processing. This keeps the app responsive. For local optimization, manage memory efficiently. Recycle bitmaps and use Android Profiler to monitor usage. By adapting to device capabilities or leveraging cloud resources, you ensure smooth performance across a range of devices, turning resource-hungry tasks into manageable ones.

After all, your algorithms working fine, it's correct, it's kind of fast, it's small in size, it's easy to implement, it's easy to maintain... but if it's still not enough? We will need to take a step back, look at the problem again! And try other ways (don't do the same and expect new result :| ) to solve this. Just it, success or not, at least you tried.

Always have algorithm auditing and monitoring done at proper interval. This helps in identifying problems early and provides us enough time to debug and fix. Keeping reviewing your algo. See if tweaking the core fixes the problem along with future needs be covered. If you see that the problem going ahead will not be fixed, go ahead and find alternative solutions that fit in, that solves the problem in hand and also takes care of the future need.

Reevaluate the algorithm to determine if it’s the most suitable approach for the problem at hand. Sometimes, a fundamentally different algorithm or technique can offer significant performance improvements. Consider alternative algorithms that may offer better time complexity or more efficient data handling. Assess whether the current algorithm's design aligns with the problem requirements and constraints. If necessary, implement and test alternative algorithms to compare their performance against the current one.

I find that these days general solutions such as yolov8 can usually get the job done. I honestly can’t remember the last time a problems “make it or break it” moment boiled down to choosing a different model architecture lol This is some thing you’ll only face when dealing with extremely high usage production models powering something like ChatGPT

Imagine an e-commerce app that uses a complex algo to sort products based on user preferences. The algo analyzes user behavior, purchase history, and even seasonal trends. However, on lower-end devices, this "smart" sorting drags the app's performance down to a crawl. The team initially optimized the code, used caching, and even offloaded some processing to the cloud. Yet, the app still struggled on devices with limited processing power. After a review, the team decided to switch to a simpler, heuristic-based sorting method. Instead of crunching all the data on the device, the app now uses precomputed recommendations and a lightweight sorting function that ranks products based on recent user actions.

Performance optimization is an iterative process, so continuously test and refine the algorithm based on the changes made. After implementing optimizations or scaling resources, re-assess performance using the same benchmarks and profiling tools as before. Validate that improvements are consistent across different scenarios and do not introduce regressions. Gather feedback from testing to identify any remaining issues or areas for further enhancement. Repeat the process as needed to achieve optimal performance and adapt to evolving requirements.

Test benches can be very useful for this step. Unlike ad-hoc testing, which might suffer from inconsistencies due to varying external conditions, test benches enable to replicate the exact scenario needed to assess the impact of each optimization. They help in identifying hidden performance bottlenecks by simulating real-world conditions, such as high user loads or network latency, which could otherwise go unnoticed. Using test benches gives a proactive approach to managing the performance, maintaining the expected performance over time regardless of the varying usage patterns and system demands.

Take, for example, a food delivery app struggling with slow map loading times. The team makes initial changes: they compress images, optimize the route-finding algorithm, and cache frequent locations. Great! The app now runs faster...for a while. But soon, a new feature rolls out—real-time driver tracking—and the app slows down again. The cycle begins anew. The team tests under the same conditions as the baseline, tweaking the real-time update intervals and optimizing data payloads. They iterate, test, and iterate again. This process is endless because what works today may not be optimal tomorrow. As new features and devices emerge, regular review and testing are crucial. In mobile development, iteration isn’t a phase; it's a lifestyle.

If there is a performance issue and it is discarded that the problem is related to an external element (network, cpu, memory), conducting a thorough test will tell me exactly how the code is working and where the possible bottleneck is.

After implementing changes, retest using the same baseline metrics. Measure the impact of each iteration to confirm that the changes have a positive effect and do not introduce new performance issues. Be prepared to revisit earlier steps as new bottlenecks surface or requirements evolve. Regular performance reviews and continuous testing are key to maintaining optimal performance in dynamic environments.