1 views collected around this technical thread.
The article examines Java thread states—WAITING and TIMED_WAITING—through monitoring screenshots, explains their definitions, trigger conditions, and recovery mechanisms, analyzes real‑world code involving concurrent tasks, and presents flame‑graph based performance optimizations for backend services.
This article provides an in‑depth overview of Valgrind, explaining its architecture, the capabilities of its various tools such as Memcheck, Cachegrind, Callgrind, Helgrind and Massif, and offers step‑by‑step installation, configuration, and practical code examples for detecting memory errors, leaks, and performance bottlenecks in C/C++ programs.
The article teaches Go developers how to generate and analyze CPU, heap, allocation, and goroutine profiles with pprof and full‑runtime traces, interpret SVG flame‑graphs, top lists, and source views, and apply concrete optimizations—such as buffering channels and using sync.Pool—to dramatically speed up a Mandelbrot generator.
Conan is an automated profiling solution for Go microservices that embeds an SDK to continuously or adaptively sample CPU, memory and goroutine metrics, detects anomalies via user‑defined rules, uploads data to a Pyroscope server, and reports results through Feishu or Pyroscope, delivering sub‑5 % overhead and faster root‑cause analysis.
This article introduces core Python concepts—including dictionaries and sets as key‑value and unique collections, file and directory manipulation with the os module, regular expression handling, logging setup, unit testing with unittest, performance profiling via cProfile, and using popular third‑party libraries such as requests and BeautifulSoup.
This article explains how to evaluate device overheating using metrics such as CPU/GPU usage and temperature, collect and visualize Trace data with SmartPerf or DevEco Studio, analyze high‑load threads and frequency scaling, and apply targeted code changes—disabling sensors, releasing Lottie resources, and simplifying components—to reduce heat and restore frame‑rate stability.
This article introduces Linux perf, explains how to install it, demonstrates basic commands such as perf‑list, perf‑stat, perf‑top, perf‑record and perf‑report, and shows how to combine perf with flame‑graphs to locate CPU‑bound hotspots and other performance bottlenecks in applications and the kernel.
This comprehensive guide teaches Go developers how to generate CPU, memory, and goroutine profiles with pprof, interpret SVG, top, source, and peek visualizations, understand the runtime’s sampling and allocation internals, use the trace tool to analyze events, and apply these techniques to real‑world optimizations such as speeding up a Mandelbrot image generator.
The article explains how AArch64 stack unwinding works using both frame‑pointer based methods and DWARF .eh_frame/.debug_frame CFI data, detailing the calling convention, compiler effects, CIE/FDE structures, and their integration into Android’s simpleperf profiler for accurate back‑traces.
This article introduces three powerful PHP debugging and profiling tools—Xdebug, PHPDBG, and Blackfire—explaining their features, configuration steps, and providing concrete code examples to help developers efficiently locate and fix issues while improving performance.
During a load test of a Java 1.6 application on a CentOS server, memory usage climbed from 20% to 100% after an hour of 300‑concurrent requests, prompting a detailed investigation that identified off‑heap leaks caused by GZIP compression in a JimDB client and led to fixes such as upgrading the JDK and avoiding unnecessary compression.
This article explains how to use the open‑source Java diagnostic tool Arthas to attach to a running JVM, view real‑time metrics, and employ the trace command to pinpoint and analyze time‑consuming methods without inserting manual timing code.
This article explains what web page memory leaks are, identifies common sources such as timers, event listeners, closures, and DOM references, and demonstrates step‑by‑step how to locate and resolve them using Chrome DevTools' Performance and Memory panels.
This article introduces four practical techniques for measuring Python code performance, including the built‑in time module, Jupyter’s %%time magic, line_profiler for per‑line timing, and memory_profiler for detailed memory consumption, complete with example code and interpretation of results.
This article introduces four practical methods for monitoring Python code performance—using the built‑in time module, the %%time IPython magic, line_profiler for per‑line timing, and memory_profiler for detailed memory usage—complete with code examples and interpretation of results.
The article explains how a sudden Go service memory‑usage alert was traced with go tool pprof to a massive allocation in the quantile.newStream function, uncovered a Prometheus metric‑label explosion caused by the START_POINT label, and resolved the leak by disabling that label, while also reviewing typical Go memory‑leak patterns.
This guide explains how to install and use Perfetto, Android’s next‑generation tracing tool, covering trace capture via command line or Traceur, UI navigation, key trace elements, and practical SQL queries for analyzing slices, counters, scheduling, thread states, and lock contention to optimize performance.
This guide explains how to integrate the open‑source Pyroscope platform with Rainbond to continuously profile microservices, detect CPU bottlenecks and memory leaks, and visualize performance data through its UI and various view modes.
This comprehensive guide explains Linux kernel performance profiling—both on‑CPU and off‑CPU—by stressing the need to target the critical 3 % of code, covering throughput, latency and power metrics, scalability laws, flame‑graph visualizations, perf and eBPF tools, lock‑contention analysis, and further reading recommendations.
This article presents a comprehensive overview of inference optimization methods—including model pruning, quantization, knowledge distillation, caching, instruction‑set acceleration, and operator fusion—and details a GPU‑centric parallel acceleration methodology with CUDA basics, performance‑analysis tools, theoretical limits, and practical case studies, all illustrated with real‑world examples from Tencent's intelligent dialogue products.