Intel unveil Second-Generation Neuromorphic Chip

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 systemslearned to touch and even been taught to smell.

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).

Intel Unveils Loihi 2, Its Second-Generation Neuromorphic Chip, HPCWire

The C++ compiler does not support C++11 during bootstrap for cmake

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

Prerequisites that I use

  • gnu-6.5
  • m4-1.4.18
  • gmp-6.1.0
  • mpfr-3.1.4
  • mpc-1.0.3
  • isl-0.18
  • gsl-2.1

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

Errors encountered

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

export CXXFLAGS="-O3"

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


  1. The C++ compiler does not support C++11 (e.g. std::unique_ptr). building OpenWRT
  2. c++11 std::unique_ptr error cmake 3.11.3 bootstrap

Displaying Intel-MPI Debug Information

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

I_MPI_DEBUG Arguments

<level>Indicate the level of debug information provided.
0Output no debugging information. This is the default value.
1Output libfabric* version and provider.
2Output information about the tuning file used.
3Output effective MPI rank, pid and node mapping table.
4Output process pinning information.
5Output environment variables specific to the Intel® MPI Library.
> 5Add extra levels of debug information.
<flags>Comma-separated list of debug flags
pidShow process id for each debug message.
tidShow thread id for each debug message for multithreaded library.
timeShow time for each debug message.
datetimeShow time and date for each debug message.
hostShow host name for each debug message.
levelShow level for each debug message.
scopeShow scope for each debug message.
lineShow source line number for each debug message.
fileShow source file name for each debug message.
nofuncDo not show routine name.
norankDo not show rank.
nousrwarnSuppress warnings for improper use case (for example, incompatible combination of controls).
flockSynchronize debug output from different process or threads.
nobufDo not use buffered I/O for debug output.


  1. Displaying MPI Debug Information
  2. Developer Reference: I_MPI_DEBUG

Installing Intel® oneAPI AI Analytics Toolkit

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


sudo bash

Installation Instruction

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.


  1. Intel OneAPI Al Analytics Toolkit

Installing Intel OneAPI HPC Toolkit for Linux

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

sudo bash

Installation Instruction:

  • 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.


Error: Too many elements extracted from the MEAM Library on LAMMPS

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


  1. Compiling LAMMPS-15Jun20 with GNU 6 and OpenMPI 3

One Hundred Year Study on Artificial Intelligence, or AI100

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 opinionalgorithmic 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 Ponte Vecchio playing Catch Up with AMD and Nvidia

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 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.

Intel Lays Down The Gauntlet For AMD And Nvidia GPUs by Frobes

No MEAM parameter file in pair coefficients Errors in LAMMPS

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

 export LAMMPS_POTENTIALS=/path/to/lammps/potentials

For more information, do read LAMMPS Documentation

Supporting Science with HPC

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.  

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. 

Article is taken from Supporting Science with HPC from Scientific-Computing