Name
Abstract | ||
|---|---|---|
When processing large amounts of data, the rate at which reading and writing can take place is a critical factor. High energy physics data processing relying on ROOT is no exception. The recent parallelisation of LHC experimentsu0027 software frameworks and the analysis of the ever increasing amount of collision data collected by experiments further emphasized this issue underlying the need of increasing the implicit parallelism expressed within the ROOT I/O. In this contribution we highlight the improvements of the ROOT I/O subsystem which targeted a satisfactory scaling behaviour in a multithreaded context. The effect of parallelism on the individual steps which are chained by ROOT to read and write data, namely (de)compression, (de)serialisation, access to storage backend, are discussed. Performance measurements are discussed through real life examples coming from CMS production workflows on traditional server platforms and highly parallel architectures such as Intel Xeon Phi. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Distributed, Parallel, and Cluster Computing | Large Hadron Collider,Data processing,Implicit parallelism,Computer science,Xeon Phi,Parallel computing,Input/output,Collision,Workflow,Software framework,Distributed computing |
DocType | Volume | Citations |
Journal | abs/1804.03326 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guilherme Amadio | 1 | 0 | 0.68 |
| Brian Bockelman | 2 | 0 | 2.03 |
| Philippe Canal | 3 | 0 | 0.34 |
| Danilo Piparo | 4 | 4 | 1.73 |
| Enric Tejedor | 5 | 86 | 8.65 |
| Zhe Zhang | 6 | 18 | 11.32 |