dcm2niix is designed to convert neuroimaging data from the DICOM format to the NIfTI format. This web page hosts the developmental source code – a compiled version for Linux, MacOS, and Windows of the most recent stable release is included with MRIcroGL. A full manual for this software is available in the form of a NITRC wiki.
% git clone https://github.com/rordenlab/dcm2niix.git
% cd dcm2niix
% cmake -DUSE_OPENJPEG=ON -DCMAKE_CXX_FLAGS=-g -DUSE_STATIC_RUNTIME:BOOL=OFF -DCMAKE_INSTALL_PREFIX=/usr/local/dcm2niix . && make
% make install
If you turn USE_STATIC_RUNTIME:BOOL=ON, Some Centos/Redhat may report “/usr/bin/ld: cannot find -lstdc++”. This can be resolved by installing static versions of libstdc++: yum install libstdc++-static.
IBM Spectrum Scale Container Native Storage Access (CNSA) allows the deployment of Spectrum Scale in a Red Hat OpenShift cluster. Using a remote mount attached file system, CNSA provides a persistent data store to be accessed by the applications via the IBM Spectrum Scale Container Storage Interface (CSI) driver using Persistent Volumes (PVs).
Intel started a brand new architecture, built for scalability and designed to take advantage of the most advanced silicon technologies: Xe HPC. With incredible hardware like Ponte Vecchio and an open, standards-based software stack in oneAPI, Intel is already seeing leadership performance in AI workloads like ResNet-50.
A data fabric is an architectural pattern that dynamically orchestrates disparate sources across a hybrid and multicloud landscape to provide business-ready data that supports applications, analytics and business process automation.
0:00 – Intro 0:38 – Unstructured data 1:12 – Structured data 2:03 – Natural Language Understanding (NLU) & Natural Language Generation (NLG) 2:36 – Machine Translation use case 3:40 – Virtual Assistance / Chat Bots use case 4:14 – Sentiment Analysis use case 4:44 – Spam Detection use case 5:44 – Tokenization 6:18 – Stemming & Lemmatization 7:42 – Part of Speech Tagging 8:22 – Named Entity Recognition (NER) 9:08 – Summary
../compute_voronoi_atom.h:24:21: fatal error: voro++.hh: No such file or directory
This is due to one of the a header file not found at /usr/local/lammps-29Oct20/src/compute_voronoi_atom.cpp. To resolve the issue, do take a look at Line 23 or 24 and edit to the path where you place voro++.hh
The best way for now to think of AMX is that it’s a matrix math overlay for the AVX-512 vector math units, as shown below. We can think of it like a “TensorCore” type unit for the CPU. The details about what this is were only a short snippet of the overall event, but it at least gives us an idea of how much space Intel is granting to training and inference specifically.
Data comes directly into the tiles while at the same time, the host hops ahead and dispatches the loads for the toles. TMUL operates on data the moment it’s ready. At the end of each multiplication round, the tiles move to cache and SIMD post-processing and storing. The goal on the software side is to make sure both the host and AMX unit are running simultaneously.
The prioritization for AMX toward real-world AI workloads also meant a reckoning for how users were considering training versus inference. While the latency and programmability benefits of having training stay local are critical, and could well be a selling point for scalable training workloads on the CPU, inference has been the sweet spot for Intel thus far and AMX caters to that realization.
From The Next Platform “With AMX, Intel Adds AI/ML Sparkle to Sapphire Rapids”
The 9th Annual MVAPICH User Group (MUG) conference will be held virtually with free registration from August 23-25, 2021. An exciting program has been put together with the following highlights:
Two Keynote Talks by Luiz DeRose from Oracle and Gilad Shainer from NVIDIA
Seven Tutorials/Demos (AWS, NVIDIA, Oracle, Rockport Networks, X-ScaleSolutions, and The Ohio State University)
16 Invited Talks from many organizations (LLNL, INL, Broadcom, Rockport Networks, Microsoft Azure, AWS, Paratools and University of Oregon, CWRU, SDSC, TACC, KISTI, Konkuk University, UTK, Redhat, NSF, X-ScaleSolutions, and OSC)
12 Short Presentations from the MVAPICH2 project members
A talk on the Future Roadmap of the MVAPICH2 Project
The system has been designed to be both cost-effective and scalable.
To maximise value, Pawsey has invested in Ceph, software for building storage systems out of generic hardware, and has built the online storage infrastructure around Ceph in-house. As more servers are added, the online object storage becomes more stable, resilient, and even faster.
“That’s how we were able to build a 60 PB system on this budget,” explains Gray.
“An important part of this long-term storage upgrade was to demonstrate how it can be done in a financially scalable way. In a world of mega-science projects like the Square Kilometre Array, we need to develop more cost-effective ways of providing massive storage.”