oneAPI Deep Neural Network Library (oneDNN)
Performance library for Deep Learning
2.2.0
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Profiling oneDNN Performance

oneDNN uses JIT (just-in-time) code generation based on the primitive parameters and instruction set supported by the system. In order to correctly attribute performance event information, profilers must 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) Description
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 Description x64 AArch64
DNNL_JIT_PROFILE 1 Enables VTune Amplifier integration x(default) N/A
2 Enables basic Linux perf integration x x(default)
6 Enables Linux perf integration with JIT dump output x x
14 Enables Linux perf integration with JIT dump output and TSC timestamps x N/A

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

Features for VTune Amplifier

ITT Tagging for Primitive Execution

oneDNN supports ITT tagging at primitive execution in order to provide performance information on the level of a oneDNN primitive. This feature is supported on both CPU and GPU.

ITT tagging in oneDNN during primitive execution provides more information from VTune Amplifier for the items below.

  1. Get the primitives timeline chart from VTune Amplifier, and identify potential performance issues.
  2. Get platform information such as an L1/L2 cache miss or level of FP vectorization on the primitive level.
  3. Map primitive with related computation kernels.
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) Description
DNNL_ENABLE_ITT_TASKS ON, OFF Enables ITT tagging for primitive execution
Run-Time Controls

When the feature is enabled at build-time, the DNNL_ITT_TASK_LEVEL environment variable can be used to enable different level of ITT tagging.

Environment variable Value Description
DNNL_ITT_TASK_LEVEL 0 no ITT event will be triggered
1 ITT events are only triggered in master thread
2 ITT events are triggered in all OMP/TBB threads

Example: Profiling with VTune Amplifier

For this section, it is assumed that the performance profiling environment is already set up.

profiling for hotspots

Collect profiling data:

$ amplxe-cl -collect hotspots -q -no-summary -knob sampling-mode=hw -r dnnl-vtune ./benchdnn --mode=P --conv --batch=inputs/conv/shapes_alexnet
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%,%impl%,%name%,%prb%,%Gops%,%Gfreq%,%-time%,%-Gflops%,%0time%,%0Gflops%
perf,cpu,jit:avx512_common,"alexnet:conv1",--conv g1mb256ic3ih227oc96oh55kh11sh4ph0n"alexnet:conv1",53.9726,0,17.4285,3096.81,22.5851,2389.74
perf,cpu,jit:avx512_common,"alexnet:conv2",--conv g2mb256ic96ih27oc256oh27kh5ph2n"alexnet:conv2",104.696,0,20.2195,5177.98,21.9233,4775.56
perf,cpu,jit:avx512_common,"alexnet:conv3",--conv mb256ic256ih13oc384oh13kh3ph1n"alexnet:conv3",68.904,0,15.5134,4441.57,18.1391,3798.64
perf,cpu,jit:avx512_common,"alexnet:conv4",--conv g2mb256ic384ih13oc384oh13kh3ph1n"alexnet:conv4",51.678,0,11.7397,4401.97,12.4623,4146.76
perf,cpu,jit:avx512_common,"alexnet:conv5",--conv g2mb256ic384ih13oc256oh13kh3ph1n"alexnet:conv5",34.452,0,7.77148,4433.13,8.50435,4051.11
tests:5 passed:5 skipped:0 mistrusted:0 unimplemented:0 failed:0 listed:0
total perf: min(ms):72.6726 avg(ms):83.6142
Note
Here, it is not necessary to set the DNNL_JIT_PROFILE environment variable.

Below are the top 10 function hotspots using the 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 [Dynamic code] _jit_avx512_common_conv_fwd_kernel 300.128503
benchdnn [Dynamic code] _jit_avx512_common_conv_fwd_kernel 293.946143
benchdnn [Dynamic code] _jit_avx512_common_conv_fwd_kernel 285.549830
benchdnn [Dynamic code] _jit_avx512_common_conv_fwd_kernel 268.868599
benchdnn [Dynamic code] _jit_avx512_common_conv_fwd_kernel 256.715527
benchdnn libgomp.so.1.0.0 func@0x194f0 186.604226
benchdnn libgomp.so.1.0.0 func@0x19370 82.609694
benchdnn libdnnl.so.1.8 dnnl::impl::cpu::x64::jit_avx512_co.. 35.682241
benchdnn vmlinux [vmlinux] 10.763433

The JIT-ed function _jit_avx512_common_conv_fwd_kernel is shown as belonging to the [Dynamic code] module.

Below are the top 10 primitive type hotspots using the command-line interface:

$ amplxe-cl -report hotspots -q -r dnnl-vtune -format csv -csv-delimiter ';' -group-by task -column 'CPU Time:Self' | head -n 10 | column -t -s';'
Column filter is ON.
Task Type CPU Time
convolution 1451.459338
[Outside any task] 280.489764
reorder 10.434821 10.763433

Profiling for Micro-architecture Information

Collect profiling data:

$ amplxe-cl -collect uarch-exploration -knob sampling-interval=1 -data-limit=2000 -q -no-summary -r dnnl-vtune-ue ./benchdnn --mode=P --conv --batch=inputs/conv/shapes_alexnet
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%,%impl%,%name%,%prb%,%Gops%,%Gfreq%,%-time%,%-Gflops%,%0time%,%0Gflops%
perf,cpu,jit:avx512_common,"alexnet:conv1",--conv g1mb256ic3ih227oc96oh55kh11sh4ph0n"alexnet:conv1",53.9726,0,17.2344,3131.68,24.1246,2237.24
perf,cpu,jit:avx512_common,"alexnet:conv2",--conv g2mb256ic96ih27oc256oh27kh5ph2n"alexnet:conv2",104.696,0,20.2988,5157.74,22.6731,4617.63
perf,cpu,jit:avx512_common,"alexnet:conv3",--conv mb256ic256ih13oc384oh13kh3ph1n"alexnet:conv3",68.904,0,15.5369,4434.87,17.1371,4020.75
perf,cpu,jit:avx512_common,"alexnet:conv4",--conv g2mb256ic384ih13oc384oh13kh3ph1n"alexnet:conv4",51.678,0,11.428,4522.06,12.7986,4037.79
perf,cpu,jit:avx512_common,"alexnet:conv5",--conv g2mb256ic384ih13oc256oh13kh3ph1n"alexnet:conv5",34.452,0,7.64233,4508.05,8.99841,3828.68
tests:5 passed:5 skipped:0 mistrusted:0 unimplemented:0 failed:0 listed:0
total perf: min(ms):72.1404 avg(ms):85.7318

Below are L1 Data Cache issues among primitive types using the command-line interface:

$ amplxe-cl -report hotspots -q -r dnnl-vtune-ue -format csv -csv-delimiter ';' -group-by task -column 'L1 Bound' | head -n 10 | column -t -s';'
Column filter is ON.
Task Type Back-End Bound:Memory Bound:L1 Bound(%) Back-End Bound:Memory Bound:L1 Bound:DTLB Overhead(%) Back-End Bound:Memory Bound:L1 Bound:Loads Blocked by Store Forwarding(%) Back-End Bound:Memory Bound:L1 Bound:Lock Latency(%) Back-End Bound:Memory Bound:L1 Bound:Split Loads(%) Back-End Bound:Memory Bound:L1 Bound:4K Aliasing(%) Back-End Bound:Memory Bound:L1 Bound:FB Full(%)
convolution 8.2 0.0 0.0 0.0 0.0 0.2 26.6
[Outside any task] 4.4 0.0 0.0 0.0 0.0 0.0 2.6
reorder 16.0 0.0 0.0 0.0 0.0 0.1

Below are issues with Instruction Cache misses among primitive types using the command-line interface:

$ amplxe-cl -report hotspots -q -r dnnl-vtune-ue -format csv -csv-delimiter ';' -group-by task -column 'ICache Misses' | head -n 10 | column -t -s';'
Column filter is ON.
Task Type Front-End Bound:Front-End Latency:ICache Misses(%)
convolution 0.2
[Outside any task] 0.1
reorder 0.3

Here are some column names within micro-architecture profiling results. You could replace 'ICache Misses' with another column name.

*"ICache Misses","ITLB Overhead","Bad Speculation","L1 Bound","L2 Bound","L3 Bound","DRAM Bound","Average CPU Frequency","Task Time","Task Count", etc*

For getting all column names within your profiling results, you could use the command below to get more detailed information.

$ amplxe-cl -report hotspots -q -r dnnl-vtune-ue -format csv -csv-delimiter ';' -group-by task -column=?
Available values for '-column' option are:
CPU Time:Self
Clockticks:Self
Instructions Retired:Self
CPI Rate:Self
Retiring:Self
Retiring:General Retirement:Self
Retiring:General Retirement:FP Arithmetic:Self
Retiring:General Retirement:FP Arithmetic:FP x87:Self
Retiring:General Retirement:FP Arithmetic:FP Scalar:Self
Retiring:General Retirement:FP Arithmetic:FP Vector:Self
Retiring:General Retirement:Other:Self
Retiring:Microcode Sequencer:Self
Retiring:Microcode Sequencer:Assists:Self
Front-End Bound:Self
Front-End Bound:Front-End Latency:Self
Front-End Bound:Front-End Latency:ICache Misses:Self

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 the .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 --conv --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 perf.data (23102 samples) ]

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

$ perf inject -j -i perf.data -o perf.data.j

The following command displays the top hotspots:

$ perf report -i perf.data.j --stdio | head -n20
# To display the perf.data 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 libgomp.so.1.0.0 [.] 0x000000000001d8ba
29.41% benchdnn jitted-31475-0.so [.] jit_avx2_conv_fwd_kernel_f32
20.49% benchdnn libgomp.so.1.0.0 [.] 0x000000000001d712
3.47% benchdnn libdnnl.so.1.1 [.] 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 libgomp.so.1.0.0 [.] 0x000000000001d8be
0.93% benchdnn libgomp.so.1.0.0 [.] 0x000000000001d716
0.75% benchdnn libgomp.so.1.0.0 [.] 0x000000000001d8c5
0.55% benchdnn libgomp.so.1.0.0 [.] 0x000000000001d8c3
0.46% benchdnn libgomp.so.1.0.0 [.] 0x000000000001d71d
Note
Not every kernel/distribution supports 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 Brendan Gregg's excellent perf examples page