oneAPI Deep Neural Network Library (oneDNN)
Performance library for Deep Learning
Profiling oneDNN Performance

oneDNN uses JIT (just-in-time) code generation based on primitive parameters and instruction set supported by the system. In order to correctly attribute performance event information, profilers need to be notified about address ranges containing JIT-ed code. oneDNN supports two profilers: VTune(TM) Amplifier and Linux perf.

Build-time Controls

At build-time, support for this feature is controlled via cmake option DNNL_ENABLE_JIT_PROFILING.

CMake Option Supported values (defaults in bold) Desc
DNNL_ENABLE_JIT_PROFILING ON, OFF Enables performance profilers integration

Run-time Controls

When the feature is enabled at build-time, the DNNL_JIT_PROFILE environment variable can be used to manage integration with performance profilers.

Environment variable Value Desc
DNNL_JIT_PROFILE 1 Enables VTune Amplifier integration (default)
2 Enables basic Linux perf integration
6 Enables Linux perf integration with JIT dump output
14 Enables Linux perf integration with JIT dump output and TSC timestamps

Other valid values for DNNL_JIT_PROFILE include integer values representing a combination of flags accepted by dnnl_set_jit_profiling_flags function.

The default setting of the profiling flags is to enable integration with VTune Amplifier; therefore it does not require any additional setup and works out of the box. Code integrating oneDNN may override this behavior.

This feature can also be managed at run-time with the following functions:

Function settings take precedence over environment variables.

Example: Profiling with VTune Amplifier

Assuming that environment is set up already.

Collect profiling data:

$ amplxe-cl -collect hotspots -q -no-summary -knob sampling-mode=hw -r dnnl-vtune ./benchdnn --mode=P mb1ic32ih14oc32oh14kh3ph1n"resnet_50:res4a_branch2b*6"
amplxe: Warning: To enable hardware event-base sampling, VTune Amplifier has disabled the NMI watchdog timer. The watchdog timer will be re-enabled after collection completes.
Output template: perf,%engine%,%name%,%desc%,%Gops%,%Gfreq%,%-time%,%-Gflops%,%0time%,%0Gflops%
perf,cpu,resnet_50:res4a_branch2b*6,--conv mb1ic32ih14oc32oh14kh3ph1nresnet_50:res4a_branch2b*6,0.0032768,0,2.13525,1.53462,4.32546,0.757561
tests:1 passed:0 skipped:0 mistrusted:0 unimplemented:0 failed:0 listed:0
total perf: min(ms):2.13525 avg(ms):4.32546
You don't need to set DNNL_JIT_PROFILE environment variable.

Display top 10 hotspots using command-line interface:

$ amplxe-cl -report hotspots -q -r dnnl-vtune -format csv -csv-delimiter ';' -group-by process,module,function -column 'CPU Time:Self' | head -n 10 | column -t -s';'
Column filter is ON.
Process Module Function CPU Time
benchdnn do_spin 54.796608
benchdnn do_spin 52.075321
benchdnn cpu_relax 3.979194
benchdnn cpu_relax 3.838870
benchdnn [Dynamic code] jit_avx2_conv_fwd_kernel_f32 2.355442
benchdnn vmlinux __lock_acquire 0.801853
benchdnn vmlinux do_raw_spin_lock 0.290672
benchdnn dnnl::impl::cpu::jit_avx2_convolution_fwd_t::execute_forward(dnnl::impl::exec_ctx_t const&) const::{lambda(intint)#1}::operator() 0.260602
benchdnn vmlinux plist_check_prev_next 0.115266

The JIT-ed function jit_avx2_conv_fwd_kernel_f32 is shown as belonging to the [Dynamic code] module.

See more examples in the VTune Amplifier User Guide

Example: Profiling with Linux perf

The following command instructs oneDNN to enable both jitdump and perfmap profiling modes and write jitdump files into .debug directory in the current directory by setting environment variable JITDUMPDIR to point to the current directory.

$ JITDUMPDIR=. DNNL_JIT_PROFILE=6 perf record -k1 ./tests/benchdnn/benchdnn --mode=P mb1ic32ih14oc32oh14kh3ph1n"resnet_50:res4a_branch2b*6"
Output template: perf,%engine%,%name%,%desc%,%Gops%,%Gfreq%,%-time%,%-Gflops%,%0time%,%0Gflops%
perf,cpu,resnet_50:res4a_branch2b*6,--conv mb1ic32ih14oc32oh14kh3ph1nresnet_50:res4a_branch2b*6,0.0032768,0,0.0131836,248.551,0.0262988,124.599
tests:1 passed:0 skipped:0 mistrusted:0 unimplemented:0 failed:0 listed:0
total perf: min(ms):0.0131836 avg(ms):0.0262988
[ perf record: Woken up 1 times to write data ]
[ perf record: Captured and wrote 0.884 MB (23102 samples) ]

The following command injects the information from the jitdump files into the performance data:

$ perf inject -j -i -o

The following command displays the top hotspots:

$ perf report -i --stdio | head -n20
# To display the header info, please use --header/--header-only options.
# Total Lost Samples: 0
# Samples: 23K of event 'cpu-clock:uhH'
# Event count (approx.): 5775500000
# Overhead Command Shared Object Symbol
39.33% benchdnn [.] 0x000000000001d8ba
29.41% benchdnn [.] jit_avx2_conv_fwd_kernel_f32
20.49% benchdnn [.] 0x000000000001d712
3.47% benchdnn [.] dnnl::impl::cpu::jit_avx2_convolution_fwd_t::execute_forward(dnnl::impl::exec_ctx_t const&) const::{lambda(int, int)#1}::operator()
1.52% benchdnn [.] 0x000000000001d8be
0.93% benchdnn [.] 0x000000000001d716
0.75% benchdnn [.] 0x000000000001d8c5
0.55% benchdnn [.] 0x000000000001d8c3
0.46% benchdnn [.] 0x000000000001d71d
Not every kernel / distribution support displaying detailed profiling information. Symbol resolution (usually) works as long as the perfmap mode is enabled, but annotating a JIT-ed functions disassembly, which requires jitdump, seems to often fail on kernels before 5.x.

See more on the Brendan Gregg's excellent perf examples page