Layout Optimization for Performance

High-current DC/DC regulators are often plagued by EMI issues due to high dv/dt and di/dt switching transients during MOSFET commutation. These transients lead to both conducted and radiated EMI, which can severely affect system performance, especially in industries such as automotive and communications, where EMI compliance is crucial. To address this, optimizing the PCB layout is one of the most effective ways to reduce EMI at no extra cost. By carefully designing the power stage layout, engineers can minimize the parasitic inductance of the switching loop, thus reducing voltage overshoot, ringing, and overall EMI emissions. For instance, placing input capacitors close to the MOSFETs, and using a vertically oriented power loop in a multilayer PCB structure can significantly reduce the parasitic loop area. This optimization results in improved EMI performance, lowering the overshoot by up to 4V compared to conventional designs. In this white paper from Texas Instruments, we dive deeper into how specific layout changes can help mitigate EMI for high-current regulators. By leveraging best practices, such as minimizing switching loop area and using high-frequency decoupling capacitors, engineers can enhance system stability and comply with stringent EMI standards more easily.

Cache-Friendly Structs Last week, we conducted a pool, and cache efficiency was one of the most requested topics. I’m really glad this one came up because cache-friendly data structures are one of the most critical factors in modern high-performance systems — and yet, many developers still underestimate how much performance depends on memory layout rather than algorithm complexity. Modern CPUs, such as those designed by Intel, operate at extremely high speeds, but memory access remains relatively slow. To bridge this gap, processors use multiple levels of cache (L1, L2, L3). These caches store small portions of memory closer to the CPU, allowing much faster access compared to main RAM. At first glance, a struct may appear to be just a simple grouping of fields. However, the way fields are ordered and accessed has a direct impact on performance. Struct layout determines how efficiently the CPU cache can load and reuse data. When structs are designed properly, the CPU can fetch useful data in fewer cache lines, reducing latency and improving throughput. To demonstrate why cache-friendly structs matter in practice, consider the core principles that influence performance: Spatial locality — accessing data that is physically close in memory Temporal locality — reusing data that was recently accessed Cache line utilization — maximizing useful data per cache fetch Predictable memory access patterns — enabling hardware prefetching Without these principles, CPUs spend more time waiting on memory than executing instructions. This leads to cache misses, pipeline stalls, and significant performance degradation — especially in systems that process millions of objects per second. In practice, cache-friendly struct design provides something extremely valuable: efficiency. The CPU can load fewer cache lines, reuse more data, and execute instructions continuously without waiting on memory. This is essential in performance-critical environments such as trading systems, real-time engines, and large-scale simulations. One of the biggest strengths of cache-friendly design is that it improves performance without changing algorithms. Simply reorganizing fields can reduce memory stalls and dramatically increase throughput. Below, we included a simple example showing how struct layout directly affects cache efficiency. Even in its minimal form, it illustrates how memory organization impacts performance. And here’s the key takeaway: Cache-friendly structs succeed because they prioritize memory locality, predictability, and efficient cache utilization over convenience. In high-performance systems, memory layout is often more important than algorithm complexity. Struct design provides the foundation that allows modern CPUs to operate at their full potential. Have you ever improved performance significantly just by reorganizing struct fields? #Cpp #LowLatency #CacheFriendly #MemoryManagement #AlgorithmicTrading #EngineeringExcellence #SoftwareArchitecture

🚀 Cache Locality in C++: The Invisible Performance Killer In low-latency requirements, the CPU cache is your true data center. Main memory is hundreds of cycles away — a single cache miss can destroy your latency budget. 💡 A common layout (Array of Structs - AoS): struct Trade { double price; int quantity; char side; // 'B' or 'S' }; std::vector<Trade> trades; This is convenient, but not always cache-efficient. When you access price, the CPU fetches the entire cache line containing that field. If the struct is large or misaligned, unnecessary data (quantity, side) may get pulled in, and fewer useful price values fit in the cache line. ⚡ A cache-friendlier layout (Structure of Arrays - SoA): struct Trades { std::vector<double> prices; std::vector<int> quantities; std::vector<char> sides; }; Here, if your algorithm only touches prices, the cache lines are filled with exactly the data you need — no wasted bandwidth. 🔑 Takeaway: • AoS is convenient, but can waste cache capacity • SoA improves utilization when access patterns are predictable • In HFT, this translates directly into nanoseconds saved per iteration 👉 Next time you design a performance-critical loop, ask yourself: Am I feeding the CPU cache what it needs, or wasting bandwidth? 💭 I’m curious — what’s your favorite technique to get the most out of CPU caches in performance-critical systems? #Cplusplus #Performance #LowLatency #HighFrequencyTrading #SystemDesign

High Current PCB Design: Practical Layout Tips 📍 Designing high-current circuits is not just about increasing trace width. In real projects, current capability depends on layout strategy, copper distribution, and thermal design, therefore PCB layout becomes critical for reliability. Here are some practical approaches: 🟠 Parallel MOSFETs for Higher Current Using multiple MOSFETs in parallel can significantly improve current capacity in half-bridge designs. This allows current sharing and reduces stress on a single device. 🟠 Multi-Layer Copper Distribution For high-current paths: • place MOSFETs on the top layer • use copper pours + vias to connect multiple layers • replicate power copper on inner layers This creates parallel current paths across layers, greatly improving current capacity and reducing resistance. 🟠 Minimize Distance in Half-Bridge Layout In half-bridge design: • place high-side and low-side MOSFETs as close as possible • reduce loop area This improves: ◽ current efficiency ◽ switching performance ◽ EMI behavior 🟠 Use the Right Power Plane Strategy When routing high current: • use power planes (e.g. VM) instead of GND planes for main current paths • maximize copper area connected to the power source The goal is to provide a low-resistance path to the supply 🟠 Increase Copper Thickness Copper thickness directly affects current capability. Typical values: • 1 oz ≈ 35 μm • 2 oz ≈ 70 μm For very high current (e.g. 100A): • use 4 oz copper • increase trace width (e.g. ≥15 mm) • use multi-layer routing + thermal design 🟠 Consider Busbars for Extreme Current For very high current applications: PCB traces may not be enough. In industrial designs (e.g. power systems, servers): • copper busbars are often used • or thick copper / plated structures 🟠 Don't Ignore Return Path Design Current always flows in loops. • low-frequency current → prefers low resistance path • high-frequency current → follows closest return path (minimum inductance) Poor return path design can lead to: ◽ EMI ◽ unstable switching ◽ signal integrity issues 📌 DFM notes High current PCB design is not only about electrical capability. From a manufacturing perspective: • copper balance • via reliability • thermal distribution all affect long-term stability. Small layout differences can lead to significant temperature variation in production. High current design is not just make it wider. It's about: current path + copper distribution + thermal + layout working together #PCBDesign #PowerElectronics #HardwareEngineering #DFM #HighCurrent #ElectronicsEngineering #KnownPCB

You’ve optimized your React app, split your bundles, and minimized your CSS… but it still lags? Here’s one trick you probably haven’t used: CSS contain — the unsung hero of layout and paint performance. I stumbled upon this gem while debugging a performance issue on a list of expandable cards. Turns out, each toggle was causing a layout reflow across the entire page. The fix? One line: .contained-card { contain: layout paint; } Boom. Now the browser knows: “Changes here don’t affect the rest of the layout.” Why contain is magical: Isolates layout, style, and paint calculations. Reduces scope of expensive reflows. Makes complex UIs (like dashboards or lists) much more performant. Types of containment: layout → Prevents layout recalculations outside the element. style → Isolates style inheritance. paint → Confines paint operations. size → Prevents size from affecting outside elements. Bonus: Pair contain with will-change for buttery-smooth UI transitions. We talk a lot about code splitting and lazy loading, but rendering scope control is equally powerful—and criminally underused. Want your app to feel faster? Give the browser boundaries. #frontendperformance #csscontain #webdev #react #renderingpipeline #css #cleanui #lighthouse

Last week, I was debugging a sluggish web application when I discovered the culprit: layout thrashing. If you're a web developer, you've probably encountered this performance issue without even knowing it. What is Layout Thrashing? Think of it as "Forced Office Reorganization" Imagine you're working in an office. Every time someone reads the position of their desk (getting layout information), the office manager has to reorganize the entire floor plan (reflow). Now imagine multiple people checking their desk positions one after another, forcing the manager to reorganize everything repeatedly. Exhausting, right? That's layout thrashing in your browser. Here's a common scenario I encountered in a recent project. We were building a dynamic list where we needed to measure and update element heights: // 🚫 Bad Practice - Causes Layout Thrashing const elements = document.querySelectorAll('.item'); elements.forEach(element => {   const height = element.offsetHeight; // Read   element.style.height = `${height + 10}px`; // Write   // The next read forces a reflow because of the previous write }); This code reads and writes in a loop, forcing the browser to recalculate styles multiple times. On a list of just 100 items, this could trigger 100 expensive reflows! In our project, this pattern caused: - 500ms delay on initial render - Janky scrolling animations - High CPU usage - Battery drain on mobile devices How to Fix It: The "Read-Write-Read" Pattern Here's how we fixed the issue: // ✅ Good Practice - Batch reads and writes const elements = document.querySelectorAll('.item'); // Read phase - Gather all measurements const heights = Array.from(elements).map(element =>    element.offsetHeight ); // Write phase - Apply all updates elements.forEach((element, i) => {   element.style.height = `${heights[i] + 10}px`; }); #WebDevelopment #Performance #JavaScript #WebOptimization #FrontEnd

If you're in the AI performance space, you'll see countless blogs about custom GPU kernels that are 10, 30, or even 100% faster than NVIDIA's cuBLAS. Interestingly, the perf gains aren't the crazy part; it's how simple they are to achieve. Performance comes from optimizations, and optimizations fall into two buckets: features and tuning. Feature implementation is what the engineers at Modular (yours truly), NVIDIA, and Hazy Research do. It's coming up with a fusion, memory loading pattern, scheduling technique, etc., and making it accessible. Tuning is what comes after. It's picking the right combination of optimizations and dispatching them based on your workload. This is where we have an edge over cuBLAS. You see, cuBLAS is a generic library; algorithms like matmul need to be performant across a huge range of shapes. As Performance Engineers, we're not trying to support the generic case. We have a specific model in mind (Kimi, Deepseek, …), which uses fixed shapes (head_dim, weight matrix dimensions, …), and we know its dominant workloads (prefill, decode). So we can use this info to make a kernel tailored to our needs. And here's the cool part: in tons of cases, the tailored kernel can be made with tuning alone. No custom GPU code required. All you need is a strong kernel library (Mojo Kernels, Cutlass, ThunderKittens) and the knowledge of when to apply an optimization. To give you a head start, here are a couple of common scenarios and the appropriate optimization: Matmul, Decode: Try SwapAB. The batch size sits in the M dimension, but tensor cores have a fixed M size; this wastes compute. With SwapAB, the A and B matrices switch positions, moving the batch size to the N dimension, where you have finer granularity. The tradeoff is that C needs to be transposed. Prefill: Try a persistent kernel with a CLC scheduler. A large matmul launches lots of blocks across multiple waves; this puts heavy pressure on the block scheduler. Persistent kernels remove that pressure entirely. Each SM persists for the full kernel duration, and the CLC scheduler handles the assignment. My next article will be an extensive list of optimizations and when to use them, so keep your eyes peeled for that. In the meantime, check out these great resources 👇 Vishal Padia's excellent blog on Flash Attention: https://lnkd.in/g9rzN939 Mojo Kernels Matmul(FP4/8/16) config: https://lnkd.in/gPHDV76J

We are almost wrapped on a full blown post purchase test that hopefully will power how we push our program forward next year: Key Results: Layout C (simple + strongest savings offer) won with 65% lift over baseline Layout B (detailed Cannabis Trio page) delivered 42% lift over baseline Layout A (basic out-of-box page) served as control Testing Framework: Start with offer strength (pricing validation) Optimize page architecture (design/aesthetic) Expand product mix (LTV growth) Next Test Hypothesis: Combining Layout B's detailed design with Layout C's aggressive offer could drive an additional ~25% lift We ran a compelling test in preparation for Black Friday Cyber Monday where we validated a new promotional offer structure. Whether we were testing one-off promotions or more evergreen unique offers, each required building out the complete post-purchase workflow—something I had been deeply involved in since we first launched this capability. We used Aftersell by Rokt for this testing, which proved to be a solid platform to build with. Here's what we learned through their tool, and potentially something worth considering for your brand's infrastructure. We tested three different layout variations. Layout A used their simple out-of-the-box landing page. Layout B featured a much more detailed landing page showcasing our Cannabis Trio. Layout C kept the simplicity but introduced our most generous "savings" positioning. The results were clear: Layout B outperformed our baseline (Layout A) by 42%, which validated that more detailed pages tended to outperform simpler ones. However, Layout C delivered even stronger results, outperforming the original by 65%. This confirmed that offer strength could trump page complexity. The key insight was that while detailed pages improved performance, the most powerful driver was offer strength itself. Our hypothesis for the next iteration was that combining a custom, detailed landing page (similar to Layout B's approach) with the aggressive offer positioning (Layout C's strength) could potentially deliver an additional ~25% lift on top of what we had already achieved. This revealed a testing sequence framework worth following: first, validate that you had a strong enough offer from a pricing perspective. Second, optimize your page architecture and aesthetic once the offer was proven. Third, introduce additional products to expand lifetime value. This framework provided a structured approach for building out campaigns heading into the holiday season. - Aftersell put together an incredible document on other tests that might be worth checking out if you’re just setting up your upsell program OR needing to revamp an existing one: https://lnkd.in/gqgj-X64 #aftersellpartner

🚨 It’s not just your Spark engine. It’s also your data layout. There’s a race to speed up Apache Spark with Rust backends, SIMD tricks, or vectorized execution via Gluten and Comet. These are great technical efforts, much needed. But here’s the catch. If your tables are: 🧩 Too many tiny files → massive task scheduling overhead 🧭 Poor clustering → wide scans on every query 🧱 No compaction or sorting → inefficient for change-based access 🕵️ No indexing → slow lookups, unnecessary scans Then no engine—no matter how “modern”—will save you. We’ve seen 50–70% cost reductions on workloads without touching the engine—just by: ✅ Compacting based on access patterns ✅ Clustering on the correct primary & seconday keys ✅ Pruning files faster and eliminating over-partitioning Most “performance gains” in proprietary warehouses come from automated storage optimization—which you don’t get on open lake formats unless you build it yourself. So before you switch runtimes again, it’s wise to ask: Is your engine slow—or is your table layout broken? #ApacheSpark #DataEngineering #DataLakes #QueryOptimization #OpenTableFormats #Lakehouse #ApacheHudi #ApacheIceberg #DeltaLake

Introducing Insights in Chrome DevTools Performance panel! Many web developers know the power of the Chrome DevTools Performance panel, but navigating its wealth of data to pinpoint issues can be daunting. While tools like Lighthouse provide great summaries, they often lack the context of when and where issues occur within a full performance trace. On the Chrome team we're bridging this gap with the new "Insights sidebar" directly within the Performance panel. Read all about it: https://lnkd.in/gGd3bkPw This exciting feature integrates Lighthouse-style analysis right into your workflow. After recording a performance trace, the Insights sidebar appears, offering actionable recommendations. Crucially, it doesn't just list potential problems but highlights relevant events and overlays explanations directly on the performance timeline. Hover over an insight like "LCP by phase," "Render blocking requests" or "Layout shift culprits" to visually connect the suggestion to the specific moments in your trace. The sidebar covers key areas like Largest Contentful Paint (LCP) optimization (including phase breakdowns and request discovery), Interaction to Next Paint (INP) analysis (like DOM size impact and forced reflows), Cumulative Layout Shift (CLS) culprits, and general page load issues such as third-party impact and image optimization. It's designed to make performance debugging more intuitive by linking high-level insights to the granular data, helping you improve Core Web Vitals and overall user experience more effectively. Check out the Insights sidebar in the latest Chrome versions (it's been evolving since Chrome 131!). It’s a fantastic step towards making complex performance analysis more accessible. Give it a try on your next performance audit! #softwareengineering #programming #ai