We overview recent changes in the ROOT I/O system, increasing performance and enhancing it and improving its interaction with other data analysis ecosystems. Both the newly introduced compression algorithms, the much faster Bulk I/O data path, and a few additional techniques have the potential to significantly to improve experiments' software performance. The need for efficient lossless data compression has grown significantly as the amount of HEP data collected, transmitted, and stored has dramatically increased during the LHC era. While compression reduces storage space and, potentially, I/O bandwidth usage, it should not be applied blindly: there are significant trade-offs between the increased CPU cost for reading and writing files...
The compute capacity growth in high performance computing (HPC) systems is outperforming improvement...
ROOT is an object-oriented C++ framework conceived in the high-energy physics (HEP) community, des...
Architectural and technological trends of systems used for scientific computing call for a significa...
We overview recent changes in the ROOT I/O system, enhancing it by improving its performance and int...
ROOT provides an flexible format used throughout the HEP community. The number of use cases - from a...
When processing large amounts of data, the rate at which reading and writing can take place is a cri...
HPC systems are becoming ever more important as a data processing resource for the LHC experiments. ...
The ROOT TTree data format encodes hundreds of petabytes of High Energy and Nuclear Physics events. ...
Nowadays, the large majority of research insights are gained by using compute-aided analyses. Before...
The analysis of High Energy Physics (HEP) data sets often takes place outside the realm of experimen...
The major SSC experiments are expected to produce up to 1 Petabyte of data per year each. Once the p...
[EN] Data analysis workflows in High Energy Physics (HEP) read data written in the ROOT columnar for...
In the coming years, HEP data processing will need to exploit parallelism on present and future hard...
In the coming years, HEP data processing will need to exploit parallelism on present and future hard...
Two key changes are driving an immediate need for deeper understanding of I/O workloads in high-perf...
The compute capacity growth in high performance computing (HPC) systems is outperforming improvement...
ROOT is an object-oriented C++ framework conceived in the high-energy physics (HEP) community, des...
Architectural and technological trends of systems used for scientific computing call for a significa...
We overview recent changes in the ROOT I/O system, enhancing it by improving its performance and int...
ROOT provides an flexible format used throughout the HEP community. The number of use cases - from a...
When processing large amounts of data, the rate at which reading and writing can take place is a cri...
HPC systems are becoming ever more important as a data processing resource for the LHC experiments. ...
The ROOT TTree data format encodes hundreds of petabytes of High Energy and Nuclear Physics events. ...
Nowadays, the large majority of research insights are gained by using compute-aided analyses. Before...
The analysis of High Energy Physics (HEP) data sets often takes place outside the realm of experimen...
The major SSC experiments are expected to produce up to 1 Petabyte of data per year each. Once the p...
[EN] Data analysis workflows in High Energy Physics (HEP) read data written in the ROOT columnar for...
In the coming years, HEP data processing will need to exploit parallelism on present and future hard...
In the coming years, HEP data processing will need to exploit parallelism on present and future hard...
Two key changes are driving an immediate need for deeper understanding of I/O workloads in high-perf...
The compute capacity growth in high performance computing (HPC) systems is outperforming improvement...
ROOT is an object-oriented C++ framework conceived in the high-energy physics (HEP) community, des...
Architectural and technological trends of systems used for scientific computing call for a significa...