ReadMeFeed / Community knowledge
Real questions. Useful conversations. Find the people who know your stack.
To diagnose performance issues in Blender, follow these steps: Enable the built‑in statistics overlay ( View → Show Overlays → Statistics ) to see frame times and memory usage. Record a short animation or render a single frame with the Timecode panel open; note the total render time. Use an external profiler such as Linux perf or Windows ETW to capture CPU‑l
To diagnose Bazel performance, follow these steps: Enable profiling with bazel build //... --profile=profile.out to capture timing data. Examine the generated profile using bazel analyze-profile profile.out or view it in the Bazel UI. Identify long‑running actions (e.g., compilation, linking) and check their input sizes and dependencies. Look for unnecessary
When working with large CMake projects, the configuration and generation phases can become slow. What systematic approaches (e.g., using CMake's --trace, --trace-expand, time commands, or external profiling tools) can be employed to measure where time is spent, and how can the results be interpreted to identify and mitigate bottlenecks?
When working with CLion, users may observe slowdowns during indexing or builds. What systematic approach can be taken to measure and identify bottlenecks, such as using built‑in profilers, external tools like perf or Valgrind, or CLion’s own performance tests? Please outline the steps, what metrics to collect, how to verify findings, and how to roll back cha
Explain the steps involved in using measurement tools (such as timers, profilers, or hardware counters) to identify performance issues in C code. Include considerations for setting up experiments, collecting data, interpreting results, and verifying improvements, while noting any assumptions about the environment or toolchain.
blender
bazel
clion