What to do when Nested Parallelism Runs Amuck? Getting Started with...

Introduction and Description of Product

Intel® Threading Building Blocks (Intel® TBB) is a portable, open-source parallel programming library from the parallelism experts at Intel. A Python module for Intel® TBB is included in the Intel® Distribution for Python and provides an out-of-the-box scheduling replacement to address common problems arising from nested parallelism. It handles coordination of both intra- and inter-process concurrency. This article will show you how to launch Python programs using the Python module for Intel® TBB to parallelize math from popular Python modules like NumPy* and SciPy* by way of Intel® Math Kernel Library (Intel® MKL) thread scheduling. Please note that Intel® MKL also comes bundled free with the Intel® Distribution for Python. Intel® TBB is the native threading library for Intel® Data Analytics Acceleration Library (Intel® DAAL), which is a high-performance analytics package with a fully functional Python API. Furthermore, If working with the full Intel® Distribution for Python package, it is also the native threading underneath Numba*, OpenCV*, and select Scikit-learn* algorithms (which have been accelerated with Intel® DAAL).

How to Get Intel® TBB

To install full Intel® Distribution for Python package, which includes Intel® TBB, click below for installation guides:

Anaconda* Package
YUM Repository
APT Repository
Docker* Images

To install from Anaconda cloud:

conda install –c https://software.repos.intel.com/python/conda/ tbb

(It will change to ‘tbb4py’ in Q1 of 2018. Article will be updated accordingly)

Drop-in Use with Interpreter Call (no other code changes)

Simply drop in Intel® TBB and determine if it is the right solution for your problem statement!

Performance degradation due to over-subscription can be caused by nested parallel calls, many times unbeknownst to the user. These sort of “mistakes” are easy to make in a scripting environment. Intel® TBB can be turned on easily for out-of-the-box thread scheduling with no code changes. In the faith of the scripting culture of the Python community, this allows for quick checking of Intel® TBB’s performance recovery. If you already have math code written, you can easily launch with the “-m tbb ” interpreter flag, followed by script name and any required args for your script. It’s as easy as this:

python -m tbb script.py args*

NOTE: See the Interpreter Flag Reference Section for full list of available flags.

Interpreter Flag Reference

Command Line Usage

python -m tbb [-h] [--ipc] [-a] [--allocator-huge-pages] [-p P] [-b] [-v] [-m] script.py args*

Get Help from Command Line

python -m tbb –-help
pydoc tbb

List of the currently available interpreter flags

Interpreter Flag	Description of Instruction
-h, --help	show this help message and exit
-m	Executes following as a module (default: False)
-a, --allocator	Enable TBB scalable allocator as a replacement for standard memory allocator (default: False)
--allocator-huge-pages	Enable huge pages for TBB allocator (implies: -a) (default: False)
-p P, --max-num-threads P	Initialize TBB with P max number of threads per process (default: number of available logical processors on system)
-b, --benchmark	Block TBB initialization until all the threads are created before continue the script. This is necessary for performance benchmarks that want to exclude TBB initialization from the measurements (default: False)
-v, --verbose	Request verbose and version information (default: False)
--ipc	Enable inter-process (IPC) coordination between TBB schedulers (default: False)