Optimizing Speed and Performance

Optimizing ASP.NET Core Startup Time: Tricks You Didn’t Know Struggling with slow ASP.NET Core startups? Try these tricks to boost performance and scalability while avoiding common pitfalls! 🔍 Start with Profiling Use tools like dotnet-trace or PerfView to identify bottlenecks and focus on optimizations with measurable impact. 1️⃣ Optimize Services and Dependencies 💡 Remove unused dependencies and unnecessary services to minimize startup overhead. Use tools like Dependency Analyzer and audit services like health checks or telemetry. 2️⃣ Lazy Load Heavy Services 💡 Postpone initializing heavy services until they’re needed. 👉 Tip: Use the Lazy<T> class to defer instantiation. Avoid lazy-loading services required for early requests. 3️⃣ Precompile Resources 💡 Reduce runtime overhead by precompiling Razor views and using Ready-to-Run (R2R) for assemblies. 👉 Tip: Skip precompilation during development for faster iteration. Use it primarily for production. 4️⃣ Optimize Middleware Order 💡 Middleware is processed sequentially. Place performance-critical middleware, like response compression, near the top of the pipeline. ✅ Test critical workflows (e.g., authentication) after reordering middleware. 5️⃣ Leverage Output Caching 💡 Use OutputCache middleware to cache frequently used data at startup. Final Tip Startup optimization is more than just speed—it’s about scalability and delivering a great first impression. Start small, measure results, and test thoroughly to avoid surprises. ⚡ Key Takeaway: Not every app needs every optimization. Profile first, focus on what matters, and ensure functionality stays intact. Got a favorite optimization tip? Share it in the comments!

With a background in data engineering and business analysis, I’ve consistently seen the immense impact of optimized SQL code on improving the performance and efficiency of database operations. It indirectly contributes to cost savings by reducing resource consumption. Here are some techniques that have proven invaluable in my experience: 1. Index Large Tables: Indexing tables with large datasets (>1,000,000 rows) greatly speeds up searches and enhances query performance. However, be cautious of over-indexing, as excessive indexes can degrade write operations. 2. Select Specific Fields: Choosing specific fields instead of using SELECT * reduces the amount of data transferred and processed, which improves speed and efficiency. 3. Replace Subqueries with Joins: Using joins instead of subqueries in the WHERE clause can improve performance. 4. Use UNION ALL Instead of UNION: UNION ALL is preferable over UNION because it does not involve the overhead of sorting and removing duplicates. 5. Optimize with WHERE Instead of HAVING: Filtering data with WHERE clauses before aggregation operations reduces the workload and speeds up query processing. 6. Utilize INNER JOIN Instead of WHERE for Joins: INNER JOINs help the query optimizer make better execution decisions than complex WHERE conditions. 7. Minimize Use of OR in Joins: Avoiding the OR operator in joins enhances performance by simplifying the conditions and potentially reducing the dataset earlier in the execution process. 8. Use Views: Creating views instead of results that can be accessed faster than recalculating the views each time they are needed. 9. Minimize the Number of Subqueries: Reducing the number of subqueries in your SQL statements can significantly enhance performance by decreasing the complexity of the query execution plan and reducing overhead. 10. Implement Partitioning: Partitioning large tables can improve query performance and manageability by logically dividing them into discrete segments. This allows SQL queries to process only the relevant portions of data. #SQL #DataOptimization #DatabaseManagement #PerformanceTuning #DataEngineering

Supercharge Your Model Training: Essential Techniques and Tricks 🚀 Are you tired of long model training times and inefficient training process? I have always struggled to understand which techniques can be chained together towards cumulative improvement and the order of magnitude improvement from each. Here is an array of powerful techniques to accelerate training with their effect size. The key in most cases is to know the memory architecture for the GPU  💾 and utilize it optimally by reducing data movement between on chip registers, cache, and off chip high-bandwidth memory. Frameworks like PyTorch make this pretty simple allowing you to do this in a few lines of code at most. - Switch to Mixed Precision: 🔢 Implementing bfloat16 can lead to a potential 3x speedup by reducing the amount of data transferred, thus enabling larger batch sizes. Although GPUs may promise up to an 8x improvement, actual gains could be lower due to memory constraints. Benchmarking is essential! - PyTorch Compile: 🖥️ Experience about a 2.5x speed increase by minimizing unnecessary memory bus traffic. This approach prepares your computations for more efficient execution. - Flash Attention: ⚡ Utilize a fused kernel specifically optimized for attention-heavy models, which can boost performance by up to 40% by enhancing memory hierarchy utilization. - Optimized Data Formats: 📊 Aligning your vocab size to a power of 2 can provide a straightforward 10% speed boost by improving memory access efficiency. - Hyperparameter Tuning: 🛠️ Gain an additional 5-10% speed by tweaking hyperparameters and employing fused kernels for optimizers like AdamW. Bespoke Fused Kernels: 🧩 Push the boundaries with custom kernels designed specifically for your model’s architecture to achieve optimal performance. Leverage Additional Optimizations: ➕ Employ vector operations (e.g., AVX-512) on CPUs or use sparse kernels for pruned models to further enhance memory efficiency. Scale Responsibly: 📈 Before moving to a multi-GPU setup, ensure you've maximized the potential of single-GPU optimizations to avoid inefficiencies. Once your setup is optimized, scaling across multiple GPUs can dramatically reduce training times by parallelizing the workload and minimizing data transfers. You can do this almost trivially by using things like Hugging Face Accelerate. Remember, the effectiveness of these techniques can vary based on your specific model, hardware setup, and other variables. Extensive benchmarking is crucial to find the perfect balance between speed and accuracy. Optimization is a continuous journey. Stay proactive in exploring new methods to reduce training times and remain competitive in the fast-evolving field of machine learning. For more insights, check out Karpathy’s latest video where he replicates GPT-2 on 8x A100s, astonishingly beating GPT-3 on Hellaswag. It’s incredible to see such advancements, allowing what once took months to be accomplished virtually overnight. 🌙✨

Our App Was Crawling at Snail Speed… Until I Made This One Mistake 🚀 A few months ago, I checked our Lighthouse scores—30s. That’s like running an F1 race on a bicycle. 🏎️➡️🚲 𝐀𝐧𝐝 𝐭𝐡𝐞 𝐰𝐨𝐫𝐬𝐭 𝐩𝐚𝐫𝐭? We did everything right—modern stack, top framework, best practices. Yet, our app was sluggish. ❌ AI-powered search engines ignored us. ❌ Users kept waiting. ❌ Something was off. So, we did what every dev does—optimize. 🔧 Cut dependencies 🔧 Shrunk bundles 🔧 Tweaked configs We went from 30s to 70s. Better, but still not great. Then, I made a 𝐦𝐢𝐬𝐭𝐚𝐤𝐞. A glorious, game-changing mistake. One deploy, I accidentally removed JavaScript. And guess what? Lighthouse: 91. 😳 Sure, nothing worked. No buttons, no interactivity. But it proved our app could be fast. 💡 The lesson? Stop making JavaScript do everything. 𝐒𝐨 𝐰𝐞 𝐫𝐞𝐛𝐮𝐢𝐥𝐭: ✅ JavaScript only where needed ✅ No unnecessary hydration ✅ No bloated client-side rendering 𝐓𝐡𝐞 𝐫𝐞𝐬𝐮𝐥𝐭? 🚀 From 30s to consistent 90+ scores 🚀 Faster load times 🚀 Better search engine visibility Sometimes, the problem isn’t a lack of optimization—it’s an excess of complexity. Not every app needs a heavy framework. Not every UI should be hydrated. If you’re struggling with performance, ask yourself: ❓ Do I really need this much JavaScript? ❓ Can I pre-render more? ❓ What happens if I strip everything back to basics? You might be surprised by what you find. 👀

https://lnkd.in/gf2_khwd Optimizing I/O Performance: Are We Really Tuning the Right Knobs? I/O performance is more than just disk speed—it’s about how data flows through the entire system. Many tune buffer sizes, block I/O scheduling, and NUMA policies, but do we measure real-world impact? For high-performance systems, it's more than reducing latency but predictability. Random spikes from misaligned page caching, unnecessary journaling, or poorly tuned RAID setups can create hidden bottlenecks. Tools like iostat, blktrace, and perf expose these inefficiencies, but are we reacting to numbers or solving root causes? Instead of chasing lower latencies, what if we optimized for workload resilience—ensuring consistent performance under load spikes? This is where CPU affinity, NUMA awareness, and disk scheduling strategies play a crucial role. If your I/O performance drops under high concurrency, the question isn't just what to tune, but what the system is really telling you. What’s your biggest I/O performance challenge? #Linux #IOPerformance #SystemOptimization #NUMA #Latency #HPC #DatabaseTuning #StorageArchitecture

How to Improve API Performance? If you’ve built APIs, you’ve probably faced issues like slow response times, high database load, or network inefficiencies. These problems can frustrate users and make your system unreliable. But the good news? There are proven techniques to make your APIs faster and more efficient. Let’s go through them: 1. Pagination - Instead of returning massive datasets in one go, break the response into pages. - Reduces response time and memory usage - Helps when dealing with large datasets - Keeps requests manageable for both server and client 2. Async Logging - Logging is important, but doing it synchronously can slow down your API. - Use asynchronous logging to avoid blocking the main process - Send logs to a buffer and flush periodically - Improves throughput and reduces latency 3. Caching - Why query the database for the same data repeatedly? - Store frequently accessed data in cache (e.g., Redis, Memcached) - If the data is available in cache → return instantly - If not → query the DB, update the cache, and return the result 4. Payload Compression - Large response sizes lead to slower APIs. - Compress data before sending it over the network (e.g., Gzip, Brotli) - Smaller payload = faster download & upload - Helps in bandwidth-constrained environments 5. Connection Pooling - Opening and closing database connections is costly. - Instead of creating a new connection for every request, reuse existing ones - Reduces latency and database load - Most ORMs & DB libraries support connection pooling If your API is slow, it’s likely because of one or more of these inefficiencies. Start by profiling performance and identifying bottlenecks Implement one optimization at a time, measure impact A fast API means happier users & better scalability. 𝐅𝐨𝐫 𝐌𝐨𝐫𝐞 𝐃𝐞𝐯 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬 𝐉𝐨𝐢𝐧 𝐌𝐲 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲 : Telegram - https://lnkd.in/d_PjD86B Whatsapp - https://lnkd.in/dvk8prj5 Happy learning !

Being a software developer, you should understand these strategies to optimize 𝐀𝐏𝐈 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞. 👇 📌 𝐒𝐨𝐦𝐞 𝐛𝐚𝐜𝐤𝐠𝐫𝐨𝐮𝐧𝐝. API => Application Programming Interface => a set of rules, protocols and tools that enable different software applications to communicate and interact with each other. Early forms of APIs emerged in the 1960s and 1970s, primarily used for communication between different components of mainframe systems. 1990s =>As the internet grew, APIs were needed to help different computer programs talk to each other over networks. Early 2000s => Web services (SOAP) and a concept called SOA made APIs more popular, allowing developers to build bigger and better apps by combining different pieces of software. 2005 =>REST APIs, which are simpler and easier to scale, were introduced and quickly became the standard way to build APIs. 2010s =>With the rise of smartphones and cloud computing, APIs became even more important. They're now essential for connecting mobile apps to online services and building applications that can handle a lot of users. 📌 𝐄𝐧𝐬𝐮𝐫𝐞 𝐲𝐨𝐮𝐫 𝐀𝐏𝐈𝐬 𝐨𝐩𝐞𝐫𝐚𝐭𝐞 𝐚𝐭 𝐩𝐞𝐚𝐤 𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲 𝐛𝐲 𝐟𝐨𝐥𝐥𝐨𝐰𝐢𝐧𝐠 𝐭𝐡𝐞𝐬𝐞 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐞𝐬. [1.] Caching Store frequently used data for lightning-fast retrieval. ◾ Server-Side Caching Use tools like => Redis, Memcached etc. ◾ Client-Side Caching Leverage HTTP headers => Cache-Control, ETag ◾ Content Delivery Networks (CDNs) [2.] Database Optimization ◾ Indexing Create indexes on frequently queried columns to speed up data retrieval. ◾ Query Tuning Analyze slow queries using tools like EXPLAIN and rewrite them for better performance. ◾ Batching ◾ ORM Optimization (be mindful of => N+1 query problem => application executes an initial query to retrieve a set of entities, then performs additional queries for each entity to fetch its related data) [3.] Data Serialization Choose the right format to balance speed and size. ◾ JSON Optimization ◾ Protocol Buffers (Protobuf) ◾ MessagePack [4.] Network Optimization ◾ Content Compression Enable Gzip or Brotli compression etc. ◾ Reduce Network Hops ◾ Upgrade to HTTP/2 multiplexing, header compression etc. [5.] Server-Side Optimizations ◾ Rate Limiting Avoid abuse and ensure fair usage. ◾ Asynchronous Processing Use background tasks or message queues. ◾ Load Balancing Distribute traffic across multiple servers. ◾ Connection Pooling Reuse database connections to reduce connection overhead. [6.] API Design ◾ Pagination Break down large datasets into smaller chunks for faster responses and reduced server load. ◾ Filtering and Sorting [7.] Architectural Considerations ◾ API Gateway ◾ Caching Authorization Cache authorization decisions to avoid redundant checks. => Never miss : - Monitoring - Profiling - Load Testing Optimizing API performance is an ongoing process. 👍 Pro Tip: Always document your API. It helps developers use it effectively. 😊 #softwaredevelopment #api

Optimizing Node.js performance is crucial for enhancing efficiency and scalability. Here are some key techniques to achieve optimal results: - **Lazy Loading:** Load modules only when needed to reduce initial load time and memory usage. - **Event Loop Monitoring:** Keep an eye on event loop lag to minimize its impact on performance. - **Caching:** Implement caching strategies to reduce redundant data processing and improve response times. - **Memory Management:** Monitor memory usage to fix memory leaks and optimize garbage collection. - **Asynchronous Programming:** Efficiently handle asynchronous operations using callbacks, promises, and async/await to reduce blocking. - **Reduce Function Overhead:** Optimize the implementation of frequently called functions to minimize overhead. - **Clustering and Scaling:** Take advantage of multi-core systems by using clustering and scaling applications horizontally. - **Database Optimization:** Improve data access times by tuning queries, using connection pooling, and optimizing indexing. - **Compression and Buffering:** Manage data flow efficiently by using compression to reduce data size and buffering. - **Update Dependencies:** Ensure optimal performance and security by regularly updating and pruning dependencies. By implementing these strategies, you can significantly enhance the performance of your Node.js applications, making them more responsive and scalable for high-traffic environments.

Capacity Optimization (Optimization Part-5) Efficient PRB (Physical Resource Block) usage is crucial for improving DL user throughput. High PRB utilization can lead to network congestion and degraded performance, especially in areas with high traffic demand. Here's a breakdown: High Utilization Challenges (example): Carrier 1 - 800 MHz: •13% of samples show PRB utilization > 70%, resulting in DL user throughput < 4 Mbps. Carrier 2 - 1800 MHz: •7% of samples show PRB utilization > 90%, with DL user throughput < 4 Mbps. Ways to Cater to High Utilization: 1. Channel Optimization: Optimize channel allocation and resource scheduling to improve PRB efficiency. 2. Add New Sectors in Sites / Load Balance: New sectors can help distribute traffic evenly across the network, reducing congestion and improving throughput. 3. Enhance Antenna Technology: Leverage advanced antenna tech (e.g., MIMO) for better signal distribution and capacity handling. 4. Add New Sites / Carrier / Spectrum Refarming: Deploy additional sites to expand coverage and capacity. Implement spectrum refarming to repurpose underutilized frequency bands for more efficient resource use. Key Takeaways: • High PRB utilization is directly linked to poor DL throughput, especially in congested areas. • Capacity optimization strategies, including channel optimization, sector addition, and spectrum management, are key to enhancing network performance and user experience. By applying these strategies, operators can reduce congestion, improve DL throughput, and better cater to high utilization areas, ensuring optimal network performance. To learn more, refer to the course on RAN Engineering - https://lnkd.in/e9TpSHzF