Visualize linux scheduling data in Trace Event format - cisco-open/perf-trace-viewer

A miscellaneous collection of in-development and unsupported performance analysis tools for Linux ftrace and perf_events (aka the "perf" command). Both ftrace and perf are core Linux tracing tools, included in the kernel source.

December 18, 2018 - # perf trace -e sched:*switch/nr=2/,block:*_plug/nr=4/,block:*_unplug/nr=1/,net:*dev_queue/nr=3,max-stack=16/

Profiling and tracing information for Python using viztracer and perf, the GIL exposed. - maartenbreddels/per4m

November 18, 2019 - Simple tool for viewing Linux perf traces in IDA Pro.

An intel PT trace converter from `perf.data` to Fuchsia trace format. - michoecho/perf2perfetto

do_perf_trace_##call(void *__data, proto) \ { \ struct trace_event_call *event_call = __data; \ struct trace_event_data_offsets_##call __maybe_unused __data_offsets;\ struct trace_event_raw_##call *entry; \ struct pt_regs *__regs; \ u64 __count = 1; \ struct task_struct *__task = NULL; \ struct hlist_head *head; \ int __entry_size; \ int __data_size; \ int rctx; \ \ __data_size = trace_event_get_offsets_##call(&__data_offsets, args); \ \ head = this_cpu_ptr(event_call->perf_events); \ if (!bpf_prog_array_valid(ev

Typically, the kernel does not do particularly CPU intensive operations, making it possible to trace for longer periods. Tracing the kernel-only can be useful for analyzing latencies. perf tools support the ability to make snapshots of Intel PT trace. A snapshot can be made at any time during ...

Intel processor trace tools for analyzing performance of function - mysqlperformance/pt_perf

perf can also dynamically create tracepoints using the kprobes and uprobes frameworks, for kernel and userspace dynamic tracing.

Tracking framework performance and usage at scale. Contribute to GoogleChromeLabs/perf-track development by creating an account on GitHub.

It can do both sampling or tracing, it is a very flexible tracer. In this lab, you will obtain a kernel trace that can then be analyzed by the visualization tools. Note on perf: The perf binary trace is not readable by Trace Compass.

'/scripts/python/Perf-Trace-Util/lib/Perf/Trace') · from Core import * from perf_trace_context import * · unhandled = autodict() · def trace_begin(): print("trace_begin") pass · · def trace_end(): print_unhandled() ·

... # Trace new processes, until Ctrl-C: perf record -e sched:sched_process_exec -a # Trace all context-switches with stack traces, for 1 second: perf record -e context-switches –ag -- sleep 1 # Trace CPU migrations, for 10 seconds: perf record ...

Found 1 probe_trace_events. Opening /sys/kernel/debug/tracing/uprobe_events write=1 Added new event: Writing event: p:probe_systemd/source_dispatch /usr/lib/systemd/systemd:0x2d340 description=+0(+16(%ax)):string probe_systemd:source_dispatch (on source_dispatch in /usr/lib/systemd/systemd with description=s->description:string) You can now use it in all perf tools, such as: perf record -e probe_systemd:source_dispatch -aR sleep 1

perf record -- node --perf-basic-prof --print-code --redirect-code-traces /tmp/fib.js perf report --objdump=$HOME/src/chr/v8/tools/objdump-v8

To identify block I/O operation sources, we are going to record the block:block_rq_insert kernel tracepoint, including user-space call stacks, and correlate them to our application's behavior. To do so, run the following command (while ./logger is still executing): # perf record -p $(pidof logger) -e block:block_rq_insert -g -- sleep 10

PerfMark is a High Performance Tracing Library. Contribute to perfmark/perfmark development by creating an account on GitHub.

# as they happen (uses trace_pipe), context switching and consuming CPU to do · # so. If needed, you can try the "-d secs" option, which buffers events · # instead, reducing overhead. If you think the buffer option is losing events, # try increasing the buffer size (buffer_size_kb). # # From perf-tools: https://github.com/brendangregg/perf-tools ·

By leveraging kernel trace points in the scheduler, the overhead is pretty manageable and we only pay a price, when the process is actually getting switched out. Most notably we do not pay a price when e.g. a mutex lock operation can be handled directly in user-space. To do off-CPU analysis with Hotspot, you need to record the data with a very specific command: perf record \ -e cycles \ # on-CPU profiling -e sched:sched_switch --switch-events \ # off-CPU profiling --sample-cpu \ # track on which core code is executed -m 8M \ # reduce chance of event loss --aio -z \ # reduce disk-I/O overhead and data size --call-graph dwarf \ # we definitely want backtraces <your application>