A Reconfigurable Memory for Indexing Mass of Data
| General | |
| Overview | |
| Project Summary | |
| Publications | |
| Intranet | |
| People | |
| Architecture | |
| System | |
| RMEM board | |
| Programming | |
| Framework | |
| Operator Synthesis | |
| Applications | |
| Genomics | |
| Images | |
| Text | |
| Support | |
| Overview | |
| ACI ReMIX | |
| ARC INRIA | |
| West Genopole | |
Project Summary
|
|
Context Indexing is a well-known technique that accelerates searches within large volumes of data, such as the ones needed by applications related to genomics, to content-based image or text retrieval. Very large index (larger than the main memory capacity) are generally stored on magnetic disks. In that case, the design of indexes is fully disk-oriented, since minimizing disk I/Os is the key point to reduce response times. Therefore, disk-oriented design indirectly impacts the search algorithms that navigate within the index since they have to favor sequential patterns , avoiding as much as possible any random access to data. ReMIX Idea The ReMIX project proposes the design of a dedicated and very large index memory (several hundreds of Giga bytes), big enough to entirely store huge indexes. The use of an almost unlimited memory raises completely new issues when designing indexes. Furthemore, it allows to entirely revisit the principles that are at the root of almost all existing indexing strategies. Here, within this scheme, direct access to data, massive parallel processing, huge data redundancy, pre-computed structures, etc., can be advantageously promoted to speed-up the search. |
Dominique Lavenier
lavenier@irisa.fr
http://www.irisa.fr/remix
 
  |