Various processors and pieces of code are often compared to brains, but neuromorphic chips work to much more directly mimic neurological systems through the use of computational “neurons” that communicate with one another. Intel’s first-generation Loihi chip, introduced in 2017, has around 128,000 of those digital neurons. Over the ensuing four years, Loihi has been packed into increasingly large systems, learned to touch and even been taught to smell.Intel Unveils Loihi 2, Its Second-Generation Neuromorphic Chip, HPCWire
Now, it’s getting a new family member: Loihi 2. In its press release, Intel said that years of testing with the first-generation Loihi chip helped them to design a second generation with up to ten times the processing speed; up to 15 times greater resource density; and up to a million computational neurons per chip – more than seven times those in the first generation. Intel reports that early tests have shown that Loihi 2 required more than 60 times fewer ops per inference when running deep neural networks as compared to Loihi 1 (without a loss in accuracy).
What is CMAKE?
CMake is an open-source, cross-platform family of tools designed to build, test and package software. CMake is used to control the software compilation process using simple platform and compiler independent configuration files, and generate native makefiles and workspaces that can be used in the compiler environment of your choice.
You can download the latest cmake from https://cmake.org/download/
Prerequisites that I use
Step 1: You can use the bootstrap which will default the cmake to default location ie /usr/local/. If you are using bootstrap,
# tar -zxvf cmake-3.21.3.tar.gz # cd cmake-3.21.3 # ./bootstrap # make # make install
CMake 3.21.3, Copyright 2000-2021 Kitware, Inc. and Contributors Found GNU toolchain C compiler on this system is: gcc C++ compiler on this system is: g++ -std=gnu++1y Makefile processor on this system is: gmake g++ has setenv g++ has unsetenv g++ does not have environ in stdlib.h g++ has stl wstring g++ has <ext/stdio_filebuf.h> --------------------------------------------- gmake: Warning: File `Makefile' has modification time 0.15 s in the future gmake: `cmake' is up to date. gmake: warning: Clock skew detected. Your build may be incomplete. loading initial cache file /myhome/melvin/Downloads/cmake-3.21.3/Bootstrap.cmk/InitialCacheFlags.cmake CMake Error at CMakeLists.txt:107 (message): The C++ compiler does not support C++11 (e.g. std::unique_ptr). -- Configuring incomplete, errors occurred! See also "/myhome/melvin/Downloads/cmake-3.21.3/CMakeFiles/CMakeOutput.log". See also "/myhome/melvin/Downloads/cmake-3.21.3/CMakeFiles/CMakeError.log".
Step 1: You may want to export this before compiling
Step 2: You might want to move to an unmounted directory like /root and try compiling again with root access.
Alternatively, instead of using ./boostrap, you can use the traditional configure command
#./configure --prefix=/usr/local/cmake-3.21.3 # make # make install
The Detailed Information can be found at Displaying MPI Debug Information
The I_MPI_DEBUG environment variable provides a convenient way to get detailed information about an MPI application at runtime. You can set the variable value from 0 (the default value) to 1000. The higher the value, the more debug information you get.
High values of I_MPI_DEBUG can output a lot of information and significantly reduce performance of your application. A value of I_MPI_DEBUG=5 is generally a good starting point, which provides sufficient information to find common errors.Displaying MPI Debug Information
To redirect the debug information output from stdout to stderr or a text file, use the I_MPI_DEBUG_OUTPUT environment variable
$ mpirun -genv I_MPI_DEBUG=5 -genv I_MPI_DEBUG_OUTPUT=debug_output.txt -n 32 ./mpi_program
|<level>||Indicate the level of debug information provided.|
|0||Output no debugging information. This is the default value.|
|1||Output libfabric* version and provider.|
|2||Output information about the tuning file used.|
|3||Output effective MPI rank, pid and node mapping table.|
|4||Output process pinning information.|
|5||Output environment variables specific to the Intel® MPI Library.|
|> 5||Add extra levels of debug information.|
|<flags>||Comma-separated list of debug flags|
|pid||Show process id for each debug message.|
|tid||Show thread id for each debug message for multithreaded library.|
|time||Show time for each debug message.|
|datetime||Show time and date for each debug message.|
|host||Show host name for each debug message.|
|level||Show level for each debug message.|
|scope||Show scope for each debug message.|
|line||Show source line number for each debug message.|
|file||Show source file name for each debug message.|
|nofunc||Do not show routine name.|
|norank||Do not show rank.|
|nousrwarn||Suppress warnings for improper use case (for example, incompatible combination of controls).|
|flock||Synchronize debug output from different process or threads.|
|nobuf||Do not use buffered I/O for debug output.|
What is included in the Intel oneAPI AI Analytics Toolkit? For more information, do take a look at Intel OneAPI Al Analytics Toolkit
- Intel® Distribution for Python*
- Intel® Distribution of Modin* (via Anaconda distribution of the toolkit using the Conda package manager)
- Intel® Low Precision Optimization Tool
- Intel® Optimization for PyTorch*
- Intel® Optimization for TensorFlow*
- Model Zoo for Intel® Architecture
- Download size: 2.18 GB
- Date: August 2, 2021
- Version: 2021.3
Command Line Installation
wget https://registrationcenter-download.intel.com/akdlm/irc_nas/18040/l_AIKit_p_2021.3.0.1370_offline.sh sudo bash l_AIKit_p_2021.3.0.1370_offline.sh
Step 1: From the console, locate the downloaded install file.
Step 2: Use $ sudo sh ./<installer>.sh to launch the GUI Installer as the root.
Optionally, use $ sh ./<installer>.sh to launch the GUI Installer as the current user.
Step 3: Follow the instructions in the installer.
Step 4: Explore the Get Started Guide.
What is included in the OneAPI Installer? For more information, do take a look at Get the Intel® oneAPI HPC Toolkit
- Intel® oneAPI DPC++/C++ Compiler
- Intel® oneAPI Fortran Compiler
- Intel® C++ Compiler Classic
- Intel® Cluster Checker
- Intel® Inspector
- Intel® MPI Library
- Intel® Trace Analyzer and Collector
- Download size: 1.25 GB
- Version: 2021.3
- Date: June 21, 2021
wget https://registrationcenter-download.intel.com/akdlm/irc_nas/17912/l_HPCKit_p_2021.3.0.3230_offline.sh sudo bash l_HPCKit_p_2021.3.0.3230_offline.sh
- Step 1: From the console, locate the downloaded install file.
- Step 2: Use $ sudo sh ./<installer>.sh to launch the GUI Installer as root.
Optionally, use $ sh ./<installer>.sh to launch the GUI Installer as current user.
- Step 3: Follow the instructions in the installer.
- Step 4: Explore the Get Started Guide.
If you encounter an errors similar
ERROR: Too many elements extracted from MEAM library (current limit:5 ). Increase 'maxelt' in meam.h and recompile. Last command: pair_coeff * * library.alloy2.meam .............................
Move to /usr/local/lammps-29Oct20/src/USER-MEAMC/meam.h and /usr/local/lammps-29Oct20/src/meam.h. Edit line 22. The default value is #define maxelt 5
#definte maxelt 6
Recompile the lammps. Go to /usr/local/lammps-29Oct20/src
% make clean-all % make g++_openmpi -j 16
A newly published report on the state of artificial intelligence says the field has reached a turning point where attention must be paid to the everyday applications and even abuses of AI technology
“In the past five years, AI has made the leap from something that mostly happens in research labs or other highly controlled settings to something that’s out in society affecting people’s lives,” Brown University computer scientist Michael Littman, who chaired the report panel, said in a news release.
“That’s really exciting, because this technology is doing some amazing things that we could only dream about five or ten years ago,” Littman added. “But at the same time the field is coming to grips with the societal impact of this technology, and I think the next frontier is thinking about ways we can get the benefits from AI while minimizing the risks.”
Those risks include deep-fake images and videos that are used to spread misinformation or harm people’s reputations; online bots that are used to manipulate public opinion; algorithmic bias that infects AI with all-too-human prejudices; and pattern recognition systems that can invade personal privacy by piecing together data from multiple sources.
The report says computer scientists must work more closely with experts in the social sciences, the legal system and law enforcement to reduce those risks.
Intel recently announced details on their forthcoming data center GPU, the Xe HPC, code named Ponte Vecchio (PVC). Intel daringly implied that the peak performance of the PVC GPU would be roughly twice that of today’s fastest GPU, the Nvidia A100. PVC and Sapphire Rapids (the multi-tile next-gen Xeon) are being used to build Aurora, the Argonne National Lab’s Exascale supercomputer, in 2022, so this technology should finally be just around the corner.Intel Lays Down The Gauntlet For AMD And Nvidia GPUs by Frobes
Intel is betting on this first-generation datacenter GPU for HPC to finally catch up with Nvidia and AMD, both for HPC (64-bit floating point) and AI (8 and 16-bit integer and 16-bit floating point). The Xe HPC device is a multi-tiled, multi-process-node package with new GPU cores, HBM2e memory, a new Xe Link interconnect, and PCIe Gen 5 implemented with over 100-billion transistors. That is nearly twice the size of the 54-billion Nvidia A100 chip. At that size, power consumption could be an issue at high frequencies. Nonetheless, the Xe design clearly demonstrates that Intel gets it; packaging smaller dies helps reduce development and manufacturing costs, and can improve time to market.
If you are encountering errors like, you may want to check
ERROR: No MEAM parameter file in pair coefficients (../pair_meamc.cpp:243)
When a pair_coeff command using a potential file is specified, LAMMPS looks for the potential file in 2 places. First it looks in the location specified. E.g. if the file is specified as “niu3.eam”, it is looked for in the current working directory. If it is specified as “../potentials/niu3.eam”, then it is looked for in the potentials directory, assuming it is a sister directory of the current working directory. If the file is not found, it is then looked for in one of the directories specified by the
LAMMPS_POTENTIALS environment variable. Thus if this is set to the potentials directory in the LAMMPS distribution, then you can use those files from anywhere on your system, without copying them into your working directory. Environment variables are set in different ways for different shells. Here are example settings for
For more information, do read LAMMPS Documentation https://docs.lammps.org/stable/pair_coeff.html
Article is taken from Supporting Science with HPC from Scientific-Computing
HPC integrators can help scientists and HPC research centres through the provisioning and management of HPC clusters. As the number of applications and potential user groups for HPC continues to expand supporting domain expert scientists use and access of HPC resources is increasingly important.Article is taken from Supporting Science with HPC from Scientific-Computing
While just ten years ago a cluster would have been used by just a few departments at a University, now there is a huge pool of potential users from non-traditional HPC applications. This also includes Artificial intelligence (AI) and machine learning (ML) as well as big data or applying advanced analytics to data sets from research areas that would previously not be interested in the use of HPC systems.
This culminates in a growing need to support and facilitate the use of HPC resources in academia or research and development. These organisations can either choose to employ the staff to support this infrastructure or try to outsource some or all of these processes to companies experienced in the management and support of HPC systems.