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Lustre is a type of parallel , generally used for large-scale cluster computing. The name Lustre is a portmanteau word derived from Linux and cluster. Lustre file system software is available under the GNU General Public License (version 2 only) and provides high performance file systems for computer clusters ranging in size from small workgroup clusters to large-scale, multi-site systems. Since June 2005, Lustre has consistently been used by at least half of the top ten, and more than 60 of the top 100 fastest supercomputers in the world, including the world's No. 1 ranked TOP500 supercomputer in November 2022, Frontier, as well as previous top supercomputers such as Fugaku, Titan and Sequoia. Lustre file systems are scalable and can be part of multiple computer clusters with tens of thousands of client nodes, hundreds of petabytes (PB) of storage on hundreds of servers, and tens of terabytes per second (TB/s) of aggregate I/O throughput. This makes Lustre file systems a popular choice for businesses with large data centers, including those in industries such as meteorology, simulation, artificial intelligence and machine learning, oil and gas, life science, rich media, and finance. The I/O performance of Lustre has widespread impact on these applications and has attracted broad attention. The Lustre file system architecture was started as a research project in 1999 by Peter J. Braam, who was a staff of Carnegie Mellon University (CMU) at the time. Braam went on to found his own company Cluster File Systems in 2001, starting from work on the in the at CMU. Lustre was developed under the Accelerated Strategic Computing Initiative Path Forward project funded by the United States Department of Energy, which included Hewlett-Packard and Intel. In September 2007, Sun Microsystems acquired the assets of Cluster File Systems Inc. including its ”intellectual property“. Sun included Lustre with its high-performance computing hardware offerings, with the intent to bring Lustre technologies to Sun's ZFS and the Solaris operating system.
[2]Paolo Prandoni, [3]LCAV, EPFL
This notebook shows a simple way to process audio files (in WAV format)
in a block-by-block fashion; this is useful in two cases
1. when you want to process a data stream in real time but you can
accept a small processing delay; in this case a buffered approach
can yield computational gains
2. when you need to process a large file that does not fit entirely
into