Performance Analysis Tools

Debuggers show what was rendered. Profilers show how long it took. This is how I profile GPUs: GPU debuggers help me to see how rendering works in detail - used shaders, meshes, and targets. GPU profilers provide timings and performance insights. They are not the same. A minimal profiler, at the very least, provides some timing statistics about each draw call. Now, think about the tools you're using to improve the GPU performance in your game. Do they at least provide timings of each draw call? For me, those tools are debuggers, NOT profilers. I don't use them for GPU optimization: ❌ Frame Debugger in Unity - no timings at all. ❌ Unity Profiler - no timings for GPU draw calls. ❌ RenderDoc - very limited timings (if any), hard to interpret When I need to improve the rendering performance, I use the GPU profilers provided by the GPU vendors. Those tools show me detailed timings and performance counters: ✔️ Nvidia Nsight Graphics (my favourite) ✔️ AMD Radeon GPU Profiler ✔️ Intel GPA ✔️ PIX - profiler for DirectX 12 My optimization routine usually goes like this: 1. I find a place in the game that struggles and prepare the project to have a consistent reproduction of the same scenario. 2. I analyze the rendering using the debugger, usually Nsight Frame Debugger, which can also show some basic timings. 3. I analyze the performance using the profiler, usually Nsight GPU Trace Profiler. 4. I plan the optimization work based on the gathered data (no guessing here). 5. After optimization is done, I test in the same scenario to avoid testing bias. If you're interested in GPU profiling, I've created an article that explains the basics of GPU profiling using Nvidia Nsight Graphics. You can find the links in the comments. 

I'm pleased to make available my upcoming DATE 2025 paper, the result of a project led by my PhD student Nicholas Wendt together with Mahesh Ketkar from Intel and myself. Nicholas prepared a crystal-clear video presentation, which makes the paper's complex concepts easy to understand. The paper is titled: "SPIRE: Inferring Hardware Bottlenecks from Performance Counter Data". The paper introduces SPIRE (Statistical Piecewise Linear Roofline Ensemble), a novel performance modeling approach that combines the accessibility of roofline models with the detailed insights of hardware performance counters. Unlike existing performance analysis tools like VTune or Perfmon, SPIRE generates an ensemble of piecewise linear roofline models trained on performance counter data to estimate a processor’s maximum throughput and identify bottlenecks. It uses the models to automate the interpretation of the performance counter measurements, quickly zeroing in on microarchitectural bottlenecks such as front-end stalls, memory latency, and core execution inefficiencies. Unlike traditional analysis tools that require architecture-specific tuning, SPIRE automatically learns processor characteristics, making it applicable across different architectures with minimal deployment effort. This automated and generalized approach provides accurate performance insights, aiding both software optimizations and hardware design improvements. You can see Nicholas' short presentation here: https://lnkd.in/gcEBcub7 And you can read the full paper here: https://lnkd.in/gW5xhpi4 #computerarchitecture #performanceanalysis #research

Chapter 3 of "Microsoft Power BI Performance Best Practices" delves into the tools and techniques essential for performance tuning in Power BI. The chapter begins by explaining the two primary engines within the Analysis Services process: - Formula Engine: Handles the logical processing of queries. - Storage Engine: Manages data retrieval and storage operations. Understanding the roles and interactions of these engines is crucial for diagnosing and enhancing the performance of semantic models. The chapter introduces the Performance Analyzer, a built-in tool in Power BI that assists in evaluating the performance of report visuals. This tool breaks down processing into querying, visualizing, and other components, providing durations and metrics that help identify performance bottlenecks. It also allows users to copy queries for further analysis in external tools. Additionally, the chapter discusses the examination of log files to assess the durations of various actions. Techniques for transforming exported data are presented, including the use of hierarchies and methods to leverage this data with the Performance Analyzer. The chapter concludes by exploring external tools that can aid in performance tuning: - DAX Studio: Enables in-depth analysis of DAX queries, providing insights into their performance within the formula and storage engines. - Query Diagnostics in Power Query: Helps analyze extraction code to determine timing and efficiency. - Tabular Editor: Facilitates modifications to metadata, allowing for streamlined management and optimization of data models. By leveraging these tools and techniques, Power BI professionals can effectively identify, analyze, and address performance issues, leading to more efficient and responsive reports.

🧠 Interview Insight: Performance in CDS Views Recently, I was asked an interesting question during an interview: “How do you handle performance in a CDS view, and have you used any tools to support this?” 💡 My approach: • I emphasized code pushdown and view modeling best practices—like avoiding nested views and unnecessary joins. • I discussed using ABAP Trace (SAT) and SQL Trace (ST05) to analyze query execution and identify bottlenecks. • I also mentioned leveraging PlanViz in HANA Studio to visualize execution plans and optimize data access paths. 🔍 Tools like ST05 and PlanViz have been invaluable in pinpointing performance issues and validating improvements. 📈 Performance tuning in CDS isn’t just about speed—it’s about designing scalable, maintainable views that align with Clean Core principles. Curious to hear how others approach this— What tools or strategies have helped you optimize CDS view performance? #SAP #CDSViews #PerformanceTuning #ABAP #HANA #CleanCore #SAPRAP #TechInterview #SAPUI5 #SAPDevelopment

New in Chrome DevTools! Debug an app's full performance trace with Gemini! The DevTools Performance panel just got a major upgrade with a deeper integration of Gemini! Now you can analyze performance issues faster and more holistically than ever before. After recording a trace, you can now chat with Gemini about the entire trace, related Performance insights, and even connected field data - all without needing to select specific context beforehand! Get a full-picture analysis of your page's performance and identify potential bottlenecks: Let Gemini highlight areas of concern before you manually dive into the details. Once Gemini helps you spot a potential problem, the workflow is smooth: 1. Refine your focus: Easily select a more specific context item - like a single trace event, a specific Flame Chart block, or a Performance insight. 2. Continue the same chat: Keep the conversation going! Gemini will adjust its advice and analysis based on the new, narrow focus, helping you get to the root cause faster. The power of Gemini is now available for all insights in the Performance > Insights tab. If you see an insight, you can instantly ask Gemini about it for deeper context, explanations, and potential fixes. This new workflow is designed to help you move from a broad performance overview to a targeted deep dive without ever breaking your flow. Give it a try on your next performance recording! #ai #programming #softwareengineering

Performance professionals must comprehend the distinctions between Thread Dump Analysis, Heap Dump Analysis, and GC Logs Analysis to effectively troubleshoot and optimize system performance. This is a must for us to know to get into the world of Performance engineering. 🎄 Thread Dump Analysis reveals insights into thread-related issues like deadlocks and high CPU usage. 🎄Heap Dump Analysis is crucial for identifying memory leaks and understanding memory consumption through visual representations of object relationships. 🎄 On the other hand, GC Logs Analysis focuses on Garbage Collection behavior, helping professionals optimize memory management by analyzing pause times and frequency. A comprehensive understanding of these analyses enables Performance professionals to pinpoint specific performance issues, whether they stem from thread complexities, memory leaks, or inefficient garbage collection. Proficiency in tools like Jstack, VisualVM, and GCViewer is vital for accurate and efficient analysis. Mastering these techniques empowers performance engineers to proactively address performance challenges, ensuring optimal system functionality and responsiveness. Continuous monitoring and strategic application of these analyses contribute to robust performance-tuning strategies in dynamic computing environments. #PerformanceEngineering #ThreadDump #HeapDump #GCLogs #Optimization #Softwaretesters #Performancetesters

Monitoring and visualizing application performance is critical, especially in distributed systems where multiple components interact. Recently, I worked on a project that showcased the power of AWS X-Ray for tracing and analyzing application requests. Here’s a detailed breakdown of what I learned and how X-Ray can make a significant difference in application monitoring. What is AWS X-Ray? AWS X-Ray provides tools to monitor, trace, and debug applications running in production or development environments. By capturing and analyzing application traces, X-Ray enables us to identify bottlenecks, understand dependencies, and ensure the overall health of the system. 1️⃣ Configured X-Ray in the Application Layer • Enabled the X-Ray SDK in the application code to capture traces. • Instrumented the application to capture SQL queries and HTTP requests for better visibility into performance. 2️⃣ Set Up X-Ray in the Web Layer • Integrated the X-Ray recorder with the web-tier application to track client-side interactions and their impact on the backend systems. 3️⃣ Deployed the X-Ray Daemon • Installed and configured the X-Ray daemon on the EC2 instances to process and send trace data to the X-Ray service. 4️⃣ Monitored the Trace Map • Generated a service map to visualize the flow of requests across the architecture, including the load balancers, EC2 instances, and Aurora database. • Used CloudWatch to complement X-Ray by analyzing metrics, response times, and any potential issues in real time. Key Features Explored: • Trace Map: A graphical representation of the application’s architecture, showing the interactions between various components. • Trace Details: Dive deep into individual requests to see how they flow through the system, from the client to the backend. • Raw Data Insights: Accessed JSON trace data for advanced debugging and detailed performance analysis. Why is X-Ray Important? • Provides end-to-end visibility into application performance. • Simplifies debugging in distributed systems by breaking down requests into segments and subsegments. • Highlights latency issues, slow queries, or misconfigurations in real time, enabling faster resolution. • Facilitates optimization by identifying dependencies and usage patterns. AWS X-Ray is an essential tool for any cloud-based architecture where observability and operational insights are critical. I created the architecture diagram using Cloudairy I would love to mention some amazing individuals who have inspired me and who I learn from and collaborate with: Neal K. Davis Steven Moran Eric Huerta Prasad Rao Azeez Salu Mike Hammond Teegan A. Bartos Kumail Rizvi Benjamin Muschko #AWS #CloudComputing #AWSXRay #Observability #ApplicationMonitoring #CloudArchitecture #CloudWatch #Metrics #diagrams

I work with a lot of technologies that measure and display traffic flow, performance, and help understand and plan network capacity. -Packet capture, archive, and analysis. -Netflow/sFlow/IPFIX, and the systems that collect and display Flow data. Etc!! But one of the most satisfying things I do is create and delivery trainings for consumers of these systems. Yesterday, I delivered another training on the system we use for *Flow and Traffic Analysis. It is fun and gratifying to show users new and different ways to use these tools. But I usually don't talk about just random features... Traffic analysis and performance analysis is about *answering questions*... How much and what type of traffic is flowing over our link from X to Y? Is there any time of day when a certain link is congested, and if yes, by what? Why is my Application slow? When do I need to upgrade this circuit? Where along a path from the User to the SaaS application is a slowdown occurring? Understanding the questions, and the appropriate tools to use is what I try to help people understand, and leverage the tool sets we have to obtain the answers... preferably even before the customer asks. Associate the Tools you need and use with the types of questions that most frequently come up in your environment. My preference is for Tools that derive Flow from actual packets because, while you may only use the Flow information for most problems, you can quickly drill down into packets if the need arises, and it frequently does. But these kinds of Tools are expensive... so think carefully about where they are truly needed. Another Tool that is a favorite of mine is one that simulates traffic, a synthetic transaction Tool that measures latency, packet loss, and much more on a hop-by-hop basis. These systems are extremely valuable in narrowing your search quickly to "is it us", "is it the Internet or a carrier", or "is it the App service provider". Great tool category for SaaS and Cloud-based Applications. Other tools that are extreme valuable, are Tools that ingest all of your configurations and allow you to Map your environment, as well as run what-if simulations. If the right tool is used, and done correctly, these tools can even be used as a replacement for a physical testing Lab in most cases. They can also be used to automate the process of finding poor configurations or configuration mistakes. Networking Tools, as a category, is one of the most important sub-specialties within the Network Engineering space. I love this work and have been dedicated to it for over a decade. I will be talking more about this in the future in follow up posts. I really believe Network Monitoring/Performance/Capacity Monitoring needs more emphasis and a good PR job!! It is an essential, critical category and deserves dedicated emphasis within the enterprise IT environment! That my 2 cents worth for this Friday! 😀

5 ways the PyTorch Profiler can help make your model faster: - Memory View: This feature helps identify bottlenecks in time and memory consumption. It shows which specific operators are consuming the most memory or taking the longest to execute, helping you optimize your model to prevent out-of-memory errors and slow performance. - Distributed Debugging: When doing distributed training, this view helps you observe the performance at the individual node level. It can highlight issues like workload imbalances or "straggler" workers, which could be slowing down your training. This allows for targeted optimization in your code. - GPU Utilization: This view tracks GPU utilization and helps identify when it is underutilized. For example, if your model is not using the GPU to its full potential, you can adjust parameters like the batch size. It shows clear signs of underperformance when the GPU utilization is low. - Trace View: The trace view displays GPU utilization in 10 millisecond buckets, helping pinpoint any sudden drops or irregularities. It allows you to zoom in on specific time frames to find out why the performance is dipping, offering more insight into the problem. - SM Efficiency: This provides even finer details of GPU kernel performance. It shows the efficiency of each kernel, helping you identify the root cause of GPU underutilization, like idle times or sparse computation, and provides data that can guide you toward optimizations for smoother execution. These features combined allow for a comprehensive analysis of your model's performance, helping to diagnose and optimize both memory usage and execution efficiency. Video: https://lnkd.in/gSYPi_FP Tutorial: https://lnkd.in/gtVaNXe5

Performance testing is a crucial aspect of software quality assurance, ensuring applications can handle high loads and perform optimally under stress. Apache JMeter is one of the most powerful tools for load testing, helping QA engineers, developers, and DevOps teams analyze and improve system performance. In my latest guide, I cover: ✅ JMeter Basics – Installation, test plan creation, and components ✅ Thread Groups & Samplers – Simulating user behavior and API testing ✅ Assertions & Listeners – Validating responses and analyzing results ✅ Parameterization & Scripting – Enhancing test efficiency with variables and scripts ✅ Distributed Testing – Scaling tests across multiple machines for real-world scenarios Whether you're new to JMeter or looking to refine your skills, this guide provides step-by-step instructions and best practices to optimize your testing workflow. Are you using JMeter for performance testing? Let���s discuss your challenges and tips in the comments! #PerformanceTesting #JMeter #SoftwareTesting #QA #LoadTesting