The Tool Desk
Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Joel Fernandes’s 2023 Linux Foundation webinar, “Linux Kernel Debugging Tricks of the Trade,” is a practical tour of kernel-specific investigation—not an introduction to debugging. It shows how to prepare a debuggable kernel, capture useful evidence from crashes and hangs, and choose among QEMU/GDB, stack traces, tracing, lockup detectors, and KASAN. The central lesson is that kernel failures rarely yield to one recipe: as the slide deck puts it, “Usually no magic formula, requires creative detective work.”
What the webinar is—and who it is for
The session was presented by Joel Agnel Fernandes and recorded by the Linux Foundation on September 12, 2023. Fernandes is identified as a Google Staff Software Engineer and a Linux kernel and RCU subsystem maintainer. The event is aimed at seasoned kernel developers and people beginning Linux kernel development, but the slides deliberately skip general software-debugging instruction. You should already be comfortable programming and working in Linux before treating this as hands-on guidance.
The material is best understood as a troubleshooting toolkit. It covers debug information, address-space layout randomization (ASLR), panic and oops handling, deliberate panic testing, RCU stall timeouts, stack inspection, QEMU and GDB, KGDB/KDB, ftrace, lockup detection, frame pointers, and KASAN.
Start by classifying the failure
Tool choice follows the evidence you need and the environment in which the failure occurs. A reproducible fault in a virtual machine invites live inspection; an unreproducible crash may require a dump; a hang needs per-CPU and lockup evidence; suspected memory corruption benefits from a detector.
#1 Best Overall
| Failure or question | Useful approach | Evidence produced | Main prerequisites or costs |
|---|---|---|---|
| Reproducible crash or code path | QEMU with GDB; KGDB/KDB or another remote-debugging setup | Live registers, source location, call frames, data structures and assembly | Debuginfo, a controllable target and a debugger connection; ASLR can complicate address mapping |
| Crash when a live session is unavailable | Crash dump analysis | Post-failure kernel state and backtraces | Dump capture and a kernel build whose symbols match the failed image |
| Unclear call path | Frame pointers and stack traces | More reliable call-chain information | Kernel configuration and the runtime overhead associated with the chosen build options |
| Warning, oops or panic with useful history needed | ftrace configured to retain and dump trace data | Events leading up to the failure | Trace configuration, buffer planning and storage or console output |
| System hang or interrupt storm | Per-CPU backtraces and lockup detectors | Which CPU is stuck and what each CPU is doing | Detector configuration; reports can add diagnostic activity |
| Suspected use-after-free or out-of-bounds access | KASAN | Memory-access reports identifying corruption patterns | Instrumentation and a stated performance cost, so it is generally used in diagnostic builds |
Prepare a kernel that can explain itself
Build with debug information
Without line information, a debugger may show addresses and symbols without the source line that matters. A build containing kernel debug information lets GDB relate execution to C code, inspect structures, and follow the path into the failing function. Keep the exact unstripped image and matching modules for analysis; a symbol mismatch can make an otherwise convincing backtrace misleading.
Account for ASLR
Address-space layout randomization changes where code and data appear in memory. That is useful hardening, but it can make it harder to map a runtime address back to the expected source location. When examining a crash or a live target, treat address relocation as part of the setup rather than assuming a fixed address from a previous run.
Improve stack reliability
The slides highlight CONFIG_FRAME_POINTERS as a way to improve stack traces. Better frame information makes it easier to turn a vague fault into a concrete call path, especially when the failure occurs deep in kernel code or while several CPUs are active.
Configuration names and boot parameters change across kernel releases and architectures. The webinar’s examples date from 2023, so verify the current kernel documentation for the release you are building before copying a setting into a production system.
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Rank #2
Use a live debugger when execution can be reproduced
QEMU paired with GDB provides a controlled environment in which you can stop the guest, inspect the current instruction, examine registers and data structures, disassemble code, and move through stack frames. This is particularly useful when you can trigger the same bug repeatedly and need to see what the kernel was doing immediately before the failure.
What live debugging can reveal
- The source-level location associated with the current instruction, when matching debug information is available.
- Register values, memory and kernel data structures at the stop point.
- The call path and nearby assembly when C-level behavior is not enough.
- Whether a suspected hang is actually progressing on one CPU while another CPU is blocked.
When GDB is the wrong first move
The deck identifies three practical limits: the bug may not reproduce, you may not yet know what to inspect, or GDB may not be usable in the target environment. In those cases, a crash dump, trace buffer, lockup report or detector-generated report can provide a better starting point. GDB is not limited to live sessions, however; it can also be used while examining a crash dump when the required symbols and dump data are available.
For hardware or deployment targets that cannot be paused conveniently, the presentation discusses KGDB/KDB and remote-debugging alternatives. The exact transport and configuration depend on the target, so treat the webinar as a conceptual map and consult documentation for the kernel version and platform in use.
Read oopses, panics and stacks correctly
Oops versus panic
An oops reports a serious kernel fault but may leave the kernel running. A panic means the kernel cannot recover and must halt or reboot. That distinction changes what evidence is still available: after an oops you may be able to collect additional state, while a panic path should be configured to preserve and emit the information needed for post-failure analysis.
Dump traces at the failure boundary
The webinar recommends arranging for trace data to be dumped to the console around warnings, oopses or panics. A trace that ends at the failure can show the events that led there, rather than only the final crashing instruction. Plan where that output will go and how it will be retained; a console dump is useful only if the environment actually records it.
Deliberately trigger a panic
Testing the panic path in a disposable development or virtualized environment verifies that your console, reboot behavior, trace buffers and dump collection work before a real incident. Do not perform such a test on a system whose availability or data is not protected.
Turn a hang into a call path
For a system that appears frozen, switch among CPU threads in the debugger when possible and inspect each CPU’s backtrace. Comparing those stacks can distinguish a CPU spinning in a loop, a task blocked on a lock, and a system-wide condition in which interrupts or scheduling are no longer making progress.
Detect lockups and interrupt storms
Lockup detectors provide automated warnings when a CPU or task stops making expected progress. The slides specifically connect them with diagnosing interrupt storms. Enable them deliberately in a diagnostic configuration, then interpret the report alongside per-CPU stacks; a detector tells you that progress stopped, while the stacks help explain where.
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Investigate RCU stalls
RCU stall timeouts are another signal that a read-copy-update grace period is not completing as expected. Treat the timeout as a clue rather than a complete diagnosis: combine its report with call stacks, CPU activity and trace history to identify the code path preventing progress.
Use KASAN for memory corruption
KASAN is an in-kernel detector for memory errors such as use-after-free and out-of-bounds accesses. Its value is the detailed report around the invalid access, often including the allocation and access context needed to find the bug rather than merely observing a later crash.
The trade-off is performance. The presentation explicitly calls out a KASAN cost, so use an instrumented diagnostic build or a controlled test workload rather than assuming it is suitable for normal production operation. A clean KASAN run also does not prove that all memory bugs are absent; it means no violation was observed under the exercised workload.
A practical investigation sequence
- Make the failure safe to reproduce. Prefer QEMU or another disposable target when testing panic paths, tracing and instrumented kernels.
- Preserve matching artifacts. Keep the exact kernel image, modules and debug information associated with the run or dump.
- Describe the symptom precisely. Record whether you have an oops, panic, hang, warning, RCU stall or suspected memory corruption.
- Choose the least intrusive evidence source. Start with existing logs and stacks; add tracing, lockup detection or KASAN when the symptom demands it.
- Capture context before changing the system. Save console output, trace data and per-CPU backtraces before rebooting or reproducing the fault again.
- Escalate to live inspection. Use QEMU/GDB or a remote KGDB/KDB arrangement when the fault is reproducible and source-level state is the missing evidence.
- Compare multiple signals. A detector report, stack and trace that point to the same path are stronger than any one artifact in isolation.
- Retest after the smallest plausible fix. Re-run the reproducer and the diagnostic that exposed the original failure, then remove expensive instrumentation before production deployment.
What to take away from the 2023 session
- There is no universal kernel-debugging recipe; investigation is iterative and evidence-driven.
- Symbols, frame information and kernel configuration determine how much meaning you can extract from an address or stack.
- Live debugging is powerful for reproducible faults, while dumps and traces remain essential when pausing the target is impossible.
- Hangs require attention to CPUs, interrupts, locks and RCU progress—not just the last printed line.
- Instrumentation improves visibility but brings setup requirements and, in KASAN’s case, a significant performance trade-off.
The Linux Foundation event page provides the recorded session, Joel Fernandes’s slide deck and a repository containing demo kernel code. Those materials are useful for following the examples, but their 2023 configuration should be reconciled with the documentation for the kernel release and architecture you are debugging.
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Frequently Asked Questions
Does this webinar teach Linux or general software debugging from the beginning?
No. It assumes programming and Linux familiarity and concentrates on kernel-specific tools and failure modes.
Can GDB be used if the kernel is no longer running?
Yes. The presentation notes that GDB can analyze a crash dump, provided the dump and matching debug information are available.
Is KASAN suitable for a production kernel?
The webinar highlights a performance cost, so KASAN is generally better suited to controlled diagnostic builds and test workloads than ordinary production operation.
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