Launch Script Usage Guide

Overview

As introduced in the Performance Tuning Guide, there are several factors that influence performance. Setting configuration options properly contributes to a performance boost. However, there is no unified configuration that is optimal to all topologies. Users need to try different combinations by themselves. A launch script is provided to automate these configuration settings to free users from this complicated work. This guide helps you to learn some common usage examples that cover many optimized configuration cases.

The configurations are mainly around the following perspectives.

1. OpenMP library: [Intel OpenMP library (default) | GNU OpenMP library]

2. Memory allocator: [PyTorch default memory allocator | Jemalloc | TCMalloc (default)]

3. Number of instances: [Single instance (default) | Multiple instances]

Usage of launch script

The launch script is provided as a module of intel_extension_for_pytorch. You can take advantage of it with the following command:

ipexrun [knobs] <your_pytorch_script> [args]

Available option settings (knobs) are listed below:

knob	type	default value	help
-h, --help	-	-	show this help message and exit
-m, --module	-	False	Changes each process to interpret the launch script as a python module, executing with the same behavior as 'python -m'.
--no-python	-	False	Avoid applying python to execute program.
--silent	-	False	Suppress printings to stdio.
--log-dir	str	''	The log file directory. Setting it to empty ('') disables logging to files.
--log-file-prefix	str	'run'	log file name prefix

Launcher Common Arguments:

knob	type	default value	help
--ncores-per-instance	int	0	Number of cores per instance
--nodes-list	str	''	Specify nodes list for multiple instances to run on, in format of list of single node ids "node_id,node_id,..." or list of node ranges "node_id-node_id,...". By default all nodes will be used.
--use-e-cores	-	False	Use Efficient-Cores on the workloads or not. By default, only Performance-Cores are used.
--memory-allocator	str	'auto'	Choose which memory allocator to run the workloads with. Supported choices are ['auto', 'default', 'tcmalloc', 'jemalloc'].
--omp-runtime	str	'auto'	Choose which OpenMP runtime to run the workloads with. Supported choices are ['auto', 'default', 'intel'].

Multi-instance Arguments:

knob	type	default value	help
--ninstances	int	0	Number of instances
--instance-idx	int	-1	Inside the multi instance list, execute a specific instance at index. If it is set to -1, run all of them.
--use-logical-cores	-	False	Use logical cores on the workloads or not. By default, only physical cores are used.
--skip-cross-node-cores	-	False	Allow instances to be executed on cores across NUMA nodes.
--multi-task-manager	str	'auto'	Choose which multi task manager to run the workloads with. Supported choices are ['auto', 'none', 'numactl', 'taskset'].
--latency-mode	-	False	Use 4 cores per instance over all physical cores.
--throughput-mode	-	False	Run one instance per node with all physical cores.
--cores-list	str	''	Specify cores list for multiple instances to run on, in format of list of single core ids "core_id,core_id,..." or list of core ranges "core_id-core_id,...". By default all cores will be used.
--benchmark	-	False	Enable benchmark config. JeMalloc's MALLOC_CONF has been tuned for low latency. Recommend to use this for benchmarking purpose; for other use cases, this MALLOC_CONF may cause Out-of-Memory crash.

Distributed Training Arguments With oneCCL backend:

knob	type	default value	help
--nnodes	int	0	Number of machines/devices to use for distributed training
--nprocs-per-node	int	0	Number of processes run on each machine/device. It is by default the number of available nodes when set to 0. Argument --nodes-list affects this default value.
--ccl-worker-count	int	4	Number of cores per rank for ccl communication
--logical-cores-for-ccl	-	False	Use logical cores for the ccl worker.
--master-addr	str	127.0.0.1	Address of master node (rank 0), should be either IP address or hostname of node 0. For single node multi-proc training, the --master-addr can simply be 127.0.0.1.
--master-port	int	29500	Port on master node (rank 0) for communication during distributed training.
--hostfile	str	'hostfile'	Set the hostfile for multi-node multi-proc training. The hostfile includes a node address list containing either IP addresses or hostnames of computation nodes.
--extra-mpi-params	str	''	Extra parameters for mpiexec.hydra except for -np -ppn -hostfile and -genv I_MPI_PIN_DOMAIN

Codeless Optimization feature related option settings (knobs) are listed below:

knob	type	default value	help
--auto-ipex	-	False	Auto enabled the ipex optimization feature
--dtype	string	False	data type, can choose from ['float32', 'bfloat16']
--auto-ipex-verbose	-	False	This flag is only used for debug and UT of auto ipex.
--disable-ipex-graph-mode	-	False	Enable the Graph Mode for ipex.optimize() function

Note: --latency-mode and --throughput-mode are exclusive knobs to --ninstances, --ncores-per-instance and --use-logical-cores. I.e., setting either of --latency-mode or --throughput-mode overwrites settings of --ninstances, --ncores-per-instance and --use-logical-cores if they are explicitly set in command line. --latency-mode and --throughput-mode are mutually exclusive.

The launch script respects existing environment variables when it get launched, except for LD_PRELOAD. If you have your favorite values for certain environment variables, you can set them before running the launch script. Intel OpenMP library uses an environment variable KMP_AFFINITY to control its behavior. Different settings result in different performance numbers. By default, if you enable Intel OpenMP library, the launch script will set KMP_AFFINITY to granularity=fine,compact,1,0. If you want to try with other values, you can use export command on Linux to set KMP_AFFINITY before you run the launch script. In this case, the script will not set the default value but take the existing value of KMP_AFFINITY, and print a message to stdout.

Execution via the launch script can dump logs into files under a designated log directory so you can do some investigations afterward. By default, it is disabled to avoid undesired log files. You can enable logging by setting knob --log-dir to be:

• directory to store log files. It can be an absolute path or relative path.

• types of log files to generate. One file (<prefix>_timestamp_instances.log) contains command and information when the script was launched. Another type of file (<prefix>_timestamp_instance_#_core#-core#....log) contain stdout print of each instance.

For example:

run_20210712212258_instances.log
run_20210712212258_instance_0_cores_0-43.log

Usage Examples

Example script resnet50.py will be used in this guide.

• Single instance for inference

  • I. Use all physical cores

  • II. Use all cores including logical cores

  • III. Use physical cores on 1 node

  • IV. Use your designated number of cores

• Multiple instances for inference

  • V. Throughput mode (i.e. number of numa node instances, each instance runs on 1 numa node)

  • VI. Latency mode (Use 4 cores for each instance)

  • VII. Your designated number of instances

  • VIII. Your designated number of instances and instance index

• Usage of Jemalloc/TCMalloc/Default memory allocator

  • Jemalloc

  • TCMalloc

  • Default memory allocator

• Usage of GNU OpenMP library

  • Intel OpenMP library

  • GNU OpenMP library

Note: GIF files below illustrate CPU usage ONLY. Do NOT infer performance numbers.

Single instance for inference

I. Use all physical cores

ipexrun --log-dir ./logs resnet50.py

CPU usage is shown as below. 1 main worker thread was launched, then it launched physical core number of threads on all physical cores.

If you check your log directory, you will find directory structure as below.

.
├── resnet50.py
└── logs
    ├── run_20210712212258_instance_0_cores_0-43.log
    └── run_20210712212258_instances.log

The run_20210712212258_instances.log contains information and command that were used for this execution launch.

$ cat logs/run_20210712212258_instances.log
2021-07-12 21:22:58,764 - __main__ - WARNING - Both TCMalloc and JeMalloc are not found in $CONDA_PREFIX/lib or $VIRTUAL_ENV/lib or /.local/lib/ or /usr/local/lib/ or /usr/local/lib64/ or /usr/lib or /usr/lib64 or /home/<user>/.local/lib/ so the LD_PRELOAD environment variable will not be set. This may drop the performance
2021-07-12 21:22:58,764 - __main__ - INFO - OMP_NUM_THREADS=44
2021-07-12 21:22:58,764 - __main__ - INFO - Using Intel OpenMP
2021-07-12 21:22:58,764 - __main__ - INFO - KMP_AFFINITY=granularity=fine,compact,1,0
2021-07-12 21:22:58,764 - __main__ - INFO - KMP_BLOCKTIME=1
2021-07-12 21:22:58,764 - __main__ - INFO - LD_PRELOAD=<VIRTUAL_ENV>/lib/libiomp5.so
2021-07-12 21:22:58,764 - __main__ - WARNING - Numa Aware: cores:['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43'] on different NUMA nodes
2021-07-12 21:22:58,764 - __main__ - INFO - numactl -C 0-43 <VIRTUAL_ENV>/bin/python resnet50.py 2>&1 | tee ./logs/run_20210712212258_instance_0_cores_0-43.log

II. Use all cores including logical cores

ipexrun --use-logical-core --log-dir ./logs resnet50.py

CPU usage is shown as below. 1 main worker thread was launched, then it launched threads on all cores, including logical cores.

If you check your log directory, you will find directory structure as below.

.
├── resnet50.py
└── logs
    ├── run_20210712223308_instances.log
    └── run_20210712223308_instance_0_cores_0-87.log

The run_20210712223308_instances.log contains information and command that were used for this execution launch.

$ cat logs/run_20210712223308_instances.log
2021-07-12 22:33:08,117 - __main__ - WARNING - Both TCMalloc and JeMalloc are not found in $CONDA_PREFIX/lib or $VIRTUAL_ENV/lib or /.local/lib/ or /usr/local/lib/ or /usr/local/lib64/ or /usr/lib or /usr/lib64 or /home/<user>/.local/lib/ so the LD_PRELOAD environment variable will not be set. This may drop the performance
2021-07-12 22:33:08,117 - __main__ - INFO - OMP_NUM_THREADS=88
2021-07-12 22:33:08,117 - __main__ - INFO - Using Intel OpenMP
2021-07-12 22:33:08,118 - __main__ - INFO - KMP_AFFINITY=granularity=fine,compact,1,0
2021-07-12 22:33:08,118 - __main__ - INFO - KMP_BLOCKTIME=1
2021-07-12 22:33:08,118 - __main__ - INFO - LD_PRELOAD=<VIRTUAL_ENV>/lib/libiomp5.so
2021-07-12 22:33:08,118 - __main__ - WARNING - Numa Aware: cores:['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '44', '45', '46', '47', '48', '49', '50', '51', '52', '53', '54', '55', '56', '57', '58', '59', '60', '61', '62', '63', '64', '65', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43', '66', '67', '68', '69', '70', '71', '72', '73', '74', '75', '76', '77', '78', '79', '80', '81', '82', '83', '84', '85', '86', '87'] on different NUMA nodes
2021-07-12 22:33:08,118 - __main__ - INFO - numactl -C 0-87 <VIRTUAL_ENV>/bin/python resnet50.py 2>&1 | tee ./logs/run_20210712223308_instance_0_cores_0-87.log

III. Use physical cores on designated nodes

ipexrun --nodes-list 1 --log-dir ./logs resnet50.py

CPU usage is shown as below. 1 main worker thread was launched, then it launched threads on all other cores on the same numa node.

If you check your log directory, you will find directory structure as below.

.
├── resnet50.py
└── logs
    ├── run_20210712214504_instances.log
    └── run_20210712214504_instance_0_cores_22-43.log

The run_20210712214504_instances.log contains information and command that were used for this execution launch.

$ cat logs/run_20210712214504_instances.log
2021-07-12 21:45:04,512 - __main__ - WARNING - Both TCMalloc and JeMalloc are not found in $CONDA_PREFIX/lib or $VIRTUAL_ENV/lib or /.local/lib/ or /usr/local/lib/ or /usr/local/lib64/ or /usr/lib or /usr/lib64 or /home/<user>/.local/lib/ so the LD_PRELOAD environment variable will not be set. This may drop the performance
2021-07-12 21:45:04,513 - __main__ - INFO - OMP_NUM_THREADS=22
2021-07-12 21:45:04,513 - __main__ - INFO - Using Intel OpenMP
2021-07-12 21:45:04,513 - __main__ - INFO - KMP_AFFINITY=granularity=fine,compact,1,0
2021-07-12 21:45:04,513 - __main__ - INFO - KMP_BLOCKTIME=1
2021-07-12 21:45:04,513 - __main__ - INFO - LD_PRELOAD=<VIRTUAL_ENV>/lib/libiomp5.so
2021-07-12 21:45:04,513 - __main__ - INFO - numactl -C 22-43 -m 1 <VIRTUAL_ENV>/bin/python resnet50.py 2>&1 | tee ./logs/run_20210712214504_instance_0_cores_22-43.log

IV. Use your designated number of cores

ipexrun --ninstances 1 --ncores-per-instance 10 --log-dir ./logs resnet50.py

CPU usage is shown as below. 1 main worker thread was launched, then it launched threads on other 9 physical cores.

If you check your log directory, you will find directory structure as below.

.
├── resnet50.py
└── logs
    ├── run_20210712220928_instances.log
    └── run_20210712220928_instance_0_cores_0-9.log

The run_20210712220928_instances.log contains information and command that were used for this execution launch.

$ cat logs/run_20210712220928_instances.log
2021-07-12 22:09:28,355 - __main__ - WARNING - Both TCMalloc and JeMalloc are not found in $CONDA_PREFIX/lib or $VIRTUAL_ENV/lib or /.local/lib/ or /usr/local/lib/ or /usr/local/lib64/ or /usr/lib or /usr/lib64 or /home/<user>/.local/lib/ so the LD_PRELOAD environment variable will not be set. This may drop the performance
2021-07-12 22:09:28,355 - __main__ - INFO - OMP_NUM_THREADS=10
2021-07-12 22:09:28,355 - __main__ - INFO - Using Intel OpenMP
2021-07-12 22:09:28,355 - __main__ - INFO - KMP_AFFINITY=granularity=fine,compact,1,0
2021-07-12 22:09:28,356 - __main__ - INFO - KMP_BLOCKTIME=1
2021-07-12 22:09:28,356 - __main__ - INFO - LD_PRELOAD=<VIRTUAL_ENV>/lib/libiomp5.so
2021-07-12 22:09:28,356 - __main__ - INFO - numactl -C 0-9 -m 0 <VIRTUAL_ENV>/bin/python resnet50.py 2>&1 | tee ./logs/run_20210712220928_instance_0_cores_0-9.log

You can also specify the cores to be utilized using --cores-list argument. For example, if core id 11-20 are desired instead of the first 10 cores, the launch command would be as below.

ipexrun --ncores-per-instance 10 --cores-list "11-20" --log-dir ./logs resnet50.py

Please notice that when specifying --cores-list, a correspondant --ncores-per-instance argument is required for instance number deduction.

In this case the log directory should be like

.
├── resnet50.py
└── logs
    ├── run_20210712221615_instances.log
    └── run_20210712221615_instance_0_cores_11-20.log

The run_20210712221615_instances.log contains information and command that were used for this execution launch.

$ cat logs/run_20210712221615_instances.log
2021-07-12 22:16:15,591 - __main__ - WARNING - Both TCMalloc and JeMalloc are not found in $CONDA_PREFIX/lib or $VIRTUAL_ENV/lib or /.local/lib/ or /usr/local/lib/ or /usr/local/lib64/ or /usr/lib or /usr/lib64 or /home/<user>/.local/lib/ so the LD_PRELOAD environment variable will not be set. This may drop the performance
2021-07-12 22:16:15,591 - __main__ - INFO - OMP_NUM_THREADS=10
2021-07-12 22:16:15,591 - __main__ - INFO - Using Intel OpenMP
2021-07-12 22:16:15,591 - __main__ - INFO - KMP_AFFINITY=granularity=fine,compact,1,0
2021-07-12 22:16:15,591 - __main__ - INFO - KMP_BLOCKTIME=1
2021-07-12 22:16:15,591 - __main__ - INFO - LD_PRELOAD=<VIRTUAL_ENV>/lib/libiomp5.so
2021-07-12 22:16:15,591 - __main__ - INFO - numactl -C 11-20 -m 0 <VIRTUAL_ENV>/bin/python resnet50.py 2>&1 | tee ./logs/run_20210712221615_instance_0_cores_11-20.log

Multiple instances for inference

V. Throughput mode

ipexrun --throughput-mode --log-dir ./logs resnet50.py

CPU usage is shown as below. 2 main worker threads were launched on 2 numa nodes respectively, then they launched threads on other physical cores.

If you check your log directory, you will find directory structure as below.

.
├── resnet50.py
└── logs
    ├── run_20210712221150_instances.log
    ├── run_20210712221150_instance_0_cores_0-21.log
    └── run_20210712221150_instance_1_cores_22-43.log

The run_20210712221150_instances.log contains information and command that were used for this execution launch.

$ cat logs/run_20210712221150_instances.log
2021-07-12 22:11:50,233 - __main__ - WARNING - Both TCMalloc and JeMalloc are not found in $CONDA_PREFIX/lib or $VIRTUAL_ENV/lib or /.local/lib/ or /usr/local/lib/ or /usr/local/lib64/ or /usr/lib or /usr/lib64 or /home/<user>/.local/lib/ so the LD_PRELOAD environment variable will not be set. This may drop the performance
2021-07-12 22:11:50,233 - __main__ - INFO - OMP_NUM_THREADS=22
2021-07-12 22:11:50,233 - __main__ - INFO - Using Intel OpenMP
2021-07-12 22:11:50,233 - __main__ - INFO - KMP_AFFINITY=granularity=fine,compact,1,0
2021-07-12 22:11:50,233 - __main__ - INFO - KMP_BLOCKTIME=1
2021-07-12 22:11:50,233 - __main__ - INFO - LD_PRELOAD=<VIRTUAL_ENV>/lib/libiomp5.so
2021-07-12 22:11:50,233 - __main__ - INFO - numactl -C 0-21 -m 0 <VIRTUAL_ENV>/bin/python resnet50.py 2>&1 | tee ./logs/run_20210712221150_instance_0_cores_0-21.log
2021-07-12 22:11:50,236 - __main__ - INFO - numactl -C 22-43 -m 1 <VIRTUAL_ENV>/bin/python resnet50.py 2>&1 | tee ./logs/run_20210712221150_instance_1_cores_22-43.log

VI. Latency mode

ipexrun --latency-mode --log-dir ./logs resnet50.py

CPU usage is shown as below. 4 cores are used for each instance.

If you check your log directory, you will find directory structure as below.

.
├── resnet50.py
└── logs
    ├── run_20210712221415_instances.log
    ├── run_20210712221415_instance_0_cores_0-3.log
    ├── run_20210712221415_instance_1_cores_4-7.log
    ├── run_20210712221415_instance_2_cores_8-11.log
    ├── run_20210712221415_instance_3_cores_12-15.log
    ├── run_20210712221415_instance_4_cores_16-19.log
    ├── run_20210712221415_instance_5_cores_20-23.log
    ├── run_20210712221415_instance_6_cores_24-27.log
    ├── run_20210712221415_instance_7_cores_28-31.log
    ├── run_20210712221415_instance_8_cores_32-35.log
    ├── run_20210712221415_instance_9_cores_36-39.log
    └── run_20210712221415_instance_10_cores_40-43.log

The run_20210712221415_instances.log contains information and command that were used for this execution launch.

$ cat logs/run_20210712221415_instances.log
2021-07-12 22:14:15,140 - __main__ - WARNING - Both TCMalloc and JeMalloc are not found in $CONDA_PREFIX/lib or $VIRTUAL_ENV/lib or /.local/lib/ or /usr/local/lib/ or /usr/local/lib64/ or /usr/lib or /usr/lib64 or /home/<user>/.local/lib/ so the LD_PRELOAD environment variable will not be set. This may drop the performance
2021-07-12 22:14:15,140 - __main__ - INFO - OMP_NUM_THREADS=4
2021-07-12 22:14:15,140 - __main__ - INFO - Using Intel OpenMP
2021-07-12 22:14:15,140 - __main__ - INFO - KMP_AFFINITY=granularity=fine,compact,1,0
2021-07-12 22:14:15,140 - __main__ - INFO - KMP_BLOCKTIME=1
2021-07-12 22:14:15,140 - __main__ - INFO - LD_PRELOAD=<VIRTUAL_ENV>/lib/libiomp5.so
2021-07-12 22:14:15,140 - __main__ - INFO - numactl -C 0-3 -m 0 <VIRTUAL_ENV>/bin/python resnet50.py 2>&1 | tee ./logs/run_20210712221415_instance_0_cores_0-3.log
2021-07-12 22:14:15,143 - __main__ - INFO - numactl -C 4-7 -m 0 <VIRTUAL_ENV>/bin/python resnet50.py 2>&1 | tee ./logs/run_20210712221415_instance_1_cores_4-7.log
2021-07-12 22:14:15,146 - __main__ - INFO - numactl -C 8-11 -m 0 <VIRTUAL_ENV>/bin/python resnet50.py 2>&1 | tee ./logs/run_20210712221415_instance_2_cores_8-11.log
2021-07-12 22:14:15,149 - __main__ - INFO - numactl -C 12-15 -m 0 <VIRTUAL_ENV>/bin/python resnet50.py 2>&1 | tee ./logs/run_20210712221415_instance_3_cores_12-15.log
2021-07-12 22:14:15,151 - __main__ - INFO - numactl -C 16-19 -m 0 <VIRTUAL_ENV>/bin/python resnet50.py 2>&1 | tee ./logs/run_20210712221415_instance_4_cores_16-19.log
2021-07-12 22:14:15,154 - __main__ - WARNING - Numa Aware: cores:['20', '21', '22', '23'] on different NUMA nodes
2021-07-12 22:14:15,154 - __main__ - INFO - numactl -C 20-23 <VIRTUAL_ENV>/bin/python resnet50.py 2>&1 | tee ./logs/run_20210712221415_instance_5_cores_20-23.log
2021-07-12 22:14:15,157 - __main__ - INFO - numactl -C 24-27 -m 1 <VIRTUAL_ENV>/bin/python resnet50.py 2>&1 | tee ./logs/run_20210712221415_instance_6_cores_24-27.log
2021-07-12 22:14:15,159 - __main__ - INFO - numactl -C 28-31 -m 1 <VIRTUAL_ENV>/bin/python resnet50.py 2>&1 | tee ./logs/run_20210712221415_instance_7_cores_28-31.log
2021-07-12 22:14:15,162 - __main__ - INFO - numactl -C 32-35 -m 1 <VIRTUAL_ENV>/bin/python resnet50.py 2>&1 | tee ./logs/run_20210712221415_instance_8_cores_32-35.log
2021-07-12 22:14:15,164 - __main__ - INFO - numactl -C 36-39 -m 1 <VIRTUAL_ENV>/bin/python resnet50.py 2>&1 | tee ./logs/run_20210712221415_instance_9_cores_36-39.log
2021-07-12 22:14:15,167 - __main__ - INFO - numactl -C 40-43 -m 1 <VIRTUAL_ENV>/bin/python resnet50.py 2>&1 | tee ./logs/run_20210712221415_instance_10_cores_40-43.log

VII. Your designated number of instances

ipexrun --ninstances 4 --log-dir ./logs resnet50.py

CPU usage is shown as below. 4 main worker thread were launched, then they launched threads on all other physical cores.

If you check your log directory, you will find directory structure as below.

.
├── resnet50.py
└── logs
    ├── run_20210712221305_instances.log
    ├── run_20210712221305_instance_0_cores_0-10.log
    ├── run_20210712221305_instance_1_cores_11-21.log
    ├── run_20210712221305_instance_2_cores_22-32.log
    └── run_20210712221305_instance_3_cores_33-43.log

The run_20210712221305_instances.log contains information and command that were used for this execution launch.

$ cat logs/run_20210712221305_instances.log
2021-07-12 22:13:05,470 - __main__ - WARNING - Both TCMalloc and JeMalloc are not found in $CONDA_PREFIX/lib or $VIRTUAL_ENV/lib or /.local/lib/ or /usr/local/lib/ or /usr/local/lib64/ or /usr/lib or /usr/lib64 or /home/<user>/.local/lib/ so the LD_PRELOAD environment variable will not be set. This may drop the performance
2021-07-12 22:13:05,470 - __main__ - INFO - OMP_NUM_THREADS=11
2021-07-12 22:13:05,470 - __main__ - INFO - Using Intel OpenMP
2021-07-12 22:13:05,470 - __main__ - INFO - KMP_AFFINITY=granularity=fine,compact,1,0
2021-07-12 22:13:05,470 - __main__ - INFO - KMP_BLOCKTIME=1
2021-07-12 22:13:05,470 - __main__ - INFO - LD_PRELOAD=<VIRTUAL_ENV>/lib/libiomp5.so
2021-07-12 22:13:05,471 - __main__ - INFO - numactl -C 0-10 -m 0 <VIRTUAL_ENV>/bin/python resnet50.py 2>&1 | tee ./logs/run_20210712221305_instance_0_cores_0-10.log
2021-07-12 22:13:05,473 - __main__ - INFO - numactl -C 11-21 -m 0 <VIRTUAL_ENV>/bin/python resnet50.py 2>&1 | tee ./logs/run_20210712221305_instance_1_cores_11-21.log
2021-07-12 22:13:05,476 - __main__ - INFO - numactl -C 22-32 -m 1 <VIRTUAL_ENV>/bin/python resnet50.py 2>&1 | tee ./logs/run_20210712221305_instance_2_cores_22-32.log
2021-07-12 22:13:05,479 - __main__ - INFO - numactl -C 33-43 -m 1 <VIRTUAL_ENV>/bin/python resnet50.py 2>&1 | tee ./logs/run_20210712221305_instance_3_cores_33-43.log

VIII. Your designated number of instances and instance index

Launcher by default runs all ninstances for multi-instance inference/training as shown above. You can specify instance_idx to independently run that instance only among ninstances

ipexrun --ninstances 4 --instance-idx 0 --log-dir ./logs resnet50.py

you can confirm usage in log file:

2022-01-06 13:01:51,175 - __main__ - INFO - OMP_NUM_THREADS=14
2022-01-06 13:01:51,176 - __main__ - INFO - Using Intel OpenMP
2022-01-06 13:01:51,177 - __main__ - INFO - KMP_AFFINITY=granularity=fine,compact,1,0
2022-01-06 13:01:51,177 - __main__ - INFO - KMP_BLOCKTIME=1
2022-01-06 13:01:51,177 - __main__ - INFO - LD_PRELOAD=<VIRTUAL_ENV>/lib/libiomp5.so
2022-01-06 13:01:51,177 - __main__ - INFO - numactl -C 0-10 -m 0 <VIRTUAL_ENV>/bin/python resnet50.py 2>&1 | tee ./logs/run_20220106130151_instance_0_cores_0-13.log

ipexrun --ninstances 4 --instance-idx 1 --log-dir ./logs resnet50.py

you can confirm usage in log file:

2022-01-06 13:01:51,175 - __main__ - INFO - OMP_NUM_THREADS=14
2022-01-06 13:01:51,176 - __main__ - INFO - Using Intel OpenMP
2022-01-06 13:01:51,177 - __main__ - INFO - KMP_AFFINITY=granularity=fine,compact,1,0
2022-01-06 13:01:51,177 - __main__ - INFO - KMP_BLOCKTIME=1
2022-01-06 13:01:51,177 - __main__ - INFO - LD_PRELOAD=<VIRTUAL_ENV>/lib/libiomp5.so
2022-01-06 13:01:51,177 - __main__ - INFO - numactl -C 11-21 -m 0 <VIRTUAL_ENV>/bin/python resnet50.py 2>&1 | tee ./logs/run_20220106130151_instance_0_cores_0-13.log

Usage of Jemalloc/TCMalloc/Default memory allocator

Memory allocator influences performance sometime. If users do not designate desired memory allocator, the launch script searches them in the order of TCMalloc > Jemalloc > PyTorch default memory allocator, and takes the first matched one.

Jemalloc

Note: You can set your favorite value to MALLOC_CONF before running the launch script if you do not want to use its default setting.

ipexrun --memory-allocator jemalloc --log-dir ./logs resnet50.py

you can confirm usage in log file:

2021-07-13 15:30:48,235 - __main__ - INFO - Use JeMallocl memory allocator
2021-07-13 15:30:48,235 - __main__ - INFO - MALLOC_CONF=oversize_threshold:1,background_thread:true,metadata_thp:auto,dirty_decay_ms:9000000000,muzzy_decay_ms:9000000000
2021-07-13 15:30:48,235 - __main__ - INFO - OMP_NUM_THREADS=44
2021-07-13 15:30:48,235 - __main__ - INFO - Using Intel OpenMP
2021-07-13 15:30:48,235 - __main__ - INFO - KMP_AFFINITY=granularity=fine,compact,1,0
2021-07-13 15:30:48,235 - __main__ - INFO - KMP_BLOCKTIME=1
2021-07-13 15:30:48,235 - __main__ - INFO - LD_PRELOAD=<VIRTUAL_ENV>/lib/libiomp5.so:<VIRTUAL_ENV>/lib/libjemalloc.so
2021-07-13 15:30:48,236 - __main__ - WARNING - Numa Aware: cores:['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43'] on different NUMA nodes
2021-07-13 15:30:48,236 - __main__ - INFO - numactl -C 0-43 <VIRTUAL_ENV>/bin/python resnet50.py 2>&1 | tee ./logs/run_20210713153048_instance_0_cores_0-43.log

TCMalloc

ipexrun --memory-allocator tcmalloc --log-dir ./logs resnet50.py

you can confirm usage in log file:

2021-07-13 15:33:33,654 - __main__ - INFO - Use TCMalloc memory allocator
2021-07-13 15:33:33,654 - __main__ - INFO - OMP_NUM_THREADS=44
2021-07-13 15:33:33,654 - __main__ - INFO - Using Intel OpenMP
2021-07-13 15:33:33,654 - __main__ - INFO - KMP_AFFINITY=granularity=fine,compact,1,0
2021-07-13 15:33:33,654 - __main__ - INFO - KMP_BLOCKTIME=1
2021-07-13 15:33:33,654 - __main__ - INFO - LD_PRELOAD=<VIRTUAL_ENV>/lib/libiomp5.so:<VIRTUAL_ENV>/lib/libtcmalloc.so
2021-07-13 15:33:33,654 - __main__ - WARNING - Numa Aware: cores:['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43'] on different NUMA nodes
2021-07-13 15:33:33,655 - __main__ - INFO - numactl -C 0-43 <VIRTUAL_ENV>/bin/python resnet50.py 2>&1 | tee ./logs/run_20210713153333_instance_0_cores_0-43.log

Default memory allocator

ipexrun --memory-allocator default --log-dir ./logs resnet50.py

you can confirm usage in log file:

2021-07-13 15:36:59,784 - __main__ - INFO - OMP_NUM_THREADS=44
2021-07-13 15:36:59,784 - __main__ - INFO - Using Intel OpenMP
2021-07-13 15:36:59,784 - __main__ - INFO - KMP_AFFINITY=granularity=fine,compact,1,0
2021-07-13 15:36:59,784 - __main__ - INFO - KMP_BLOCKTIME=1
2021-07-13 15:36:59,784 - __main__ - INFO - LD_PRELOAD=<VIRTUAL_ENV>/lib/libiomp5.so
2021-07-13 15:36:59,784 - __main__ - WARNING - Numa Aware: cores:['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43'] on different NUMA nodes
2021-07-13 15:36:59,784 - __main__ - INFO - numactl -C 0-43 <VIRTUAL_ENV>/bin/python resnet50.py 2>&1 | tee ./logs/run_20210713153659_instance_0_cores_0-43.log

Usage of OpenMP library

Intel OpenMP Library

Generally, Intel OpenMP library brings better performance. Thus, in the launch script, Intel OpenMP library is used by default, if it is available. Intel OpenMP library takes environment variables like KMP_AFFINITY and KMP_BLOCKTIME to control its behavior. You can set your favorite values to them before running the launch script if you do not want to use the default settings.

GNU OpenMP Library

It is, however, not always that Intel OpenMP library brings better performance comparing to GNU OpenMP library. In this case, you can use knob --omp-runtime default to switch active OpenMP library to the GNU one. GNU OpenMP specific environment variables, OMP_SCHEDULE and OMP_PROC_BIND, for setting CPU affinity are set automatically.

ipexrun --omp-runtime default --log-dir ./logs resnet50.py

you can confirm usage in log file:

2021-07-13 15:25:00,760 - __main__ - WARNING - Both TCMalloc and JeMalloc are not found in $CONDA_PREFIX/lib or $VIRTUAL_ENV/lib or /.local/lib/ or /usr/local/lib/ or /usr/local/lib64/ or /usr/lib or /usr/lib64 or /home/<user>/.local/lib/ so the LD_PRELOAD environment variable will not be set. This may drop the performance
2021-07-13 15:25:00,761 - __main__ - INFO - OMP_SCHEDULE=STATIC
2021-07-13 15:25:00,761 - __main__ - INFO - OMP_PROC_BIND=CLOSE
2021-07-13 15:25:00,761 - __main__ - INFO - OMP_NUM_THREADS=44
2021-07-13 15:25:00,761 - __main__ - WARNING - Numa Aware: cores:['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43'] on different NUMA nodes
2021-07-13 15:25:00,761 - __main__ - INFO - numactl -C 0-43 <VIRTUAL_ENV>/bin/python resnet50.py 2>&1 | tee ./logs/run_20210713152500_instance_0_cores_0-43.log