1. A parallel computer system comprising:
an in-memory database in the memory of a plurality of fully functional compute nodes;
a database loader for pre-loading the in-memory database to optimize database efficiency by clustering database attributes into the in-memory database, wherein clustering database attributes comprises placing data corresponding to a first attribute for a plurality of records together on a first compute node of the computer system and placing data corresponding to a second attribute for the plurality of records together on a second compute node of the computer system; and
wherein the database loader clusters the database attributes based on a flag in an SQL statement that initiates placing the record in the in-memory database.
2. The parallel computer system of claim 1, wherein the parallel computer system is a massively parallel computer system.
3. The parallel computer system of claim 1 wherein the database loader determines to cluster the database attributes based on historical information for accessing the database.
4. The parallel computer system of claim 3, wherein the historical information includes information chosen from the following: node information, network information and query historical information.
5. The parallel computer system of claim 4, wherein the node information includes node identification, timestamp, current utilization, future utilization and availability.
6. The parallel computer system of claim 4, wherein the network information includes network identification, timestamp, current utilization future utilization and availability.
7. The parallel computer system of claim 4, wherein the query information includes query identification, network used, elapsed time, node list and priority.
8. A computer implemented method for pre-loading an in-memory database into memory of a plurality of compute nodes of a parallel computer system, the method comprising the steps of:
receiving a database structure to load into the in-memory database in the plurality of compute nodes;
determining an optimized data node mapping to cluster database attributes across multiple compute nodes, wherein clustering database attributes across multiple compute nodes comprises placing data corresponding to a first attribute for a plurality of records together on a first compute node of the computer system and placing data corresponding to a second attribute for the plurality of records together on a second compute node of the computer system, wherein the database loader clusters the database attributes based on a flag in an SQL statement that initiates placing the record in the in-memory database; and
loading the database structure into the in-memory database with the determined optimized data node mapping.
9. The computer implemented method of claim 8, further comprises the step of determining the data is accessed often.
10. The computer implemented method of claim 8, further comprising the step of determining if there is a force location for the database structure in the in-memory database as indicated by a system administrator input.
11. The computer implemented method of claim 8, wherein the database attributes are clustered based on a historical information for accessing the database.
12. A computer-readable article of manufacture comprising:
a database loader for pre-loading an in-memory database in memory of a plurality of compute nodes of a parallel computer system to optimize database efficiency by clustering database attributes, wherein clustering database attributes comprises placing data corresponding to a first attribute for a plurality of records together on a first compute node of the computer system and placing data corresponding to a second attribute for the plurality of records together on a second compute node of the computer system, wherein the database loader clusters the database attributes based on a flag in an SQL statement that initiates placing the record in the in-memory database; and
non-transitory computer recordable media bearing the database loader.
13. The article of manufacture of claim 12, wherein the database loader determines to cluster the database attributes based on a historical information for accessing the database.
14. The article of manufacture of claim 12, wherein the historical information includes information chosen from the following: node information, network information and query historical information.
15. The article of manufacture of claim 14, wherein the node information includes node identification, timestamp, current utilization future utilization and availability.
16. The article of manufacture of claim 14, wherein the network information includes network identification, timestamp, current utilization future utilization and availability.
17. The article of manufacture of claim 14, wherein the query information includes query identification, network used, elapsed time, node list and priority.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
1. A process for separating metaxylene with a purity of at least 99% by weight from a feedstock F of aromatic hydrocarbons having essentially 8 carbon atoms by direct separation into a single stage of adsorption in a simulated moving bed in a simulated moving bed (SMB) absorption device with at least one column that comprises a number of adsorbent beds that are separated by distributionextraction plates Pi, in which at least one feedstock F and one desorbent D are fed into this device, and at least one extract E that is high in paraxylene and at least one raffinate R are drawn off, whereby the supply and draw-off points are changed over time with a switching time T providing a number of operating zones of the SMB, the following primary operating zones of the SMB:
a zone 1 for desorption of the metaxylene that is located between the supply of the desorbent D and the draw-off of the extract E;
a zone 2 for desorption of the compounds of the raffinate, located between the draw-off of the extract E and the supply of the feedstock F;
a zone 3 for the adsorption of at least metaxylene, located between the supply of the feedstock and the draw-off of the raffinate R;
a zone 4 that is located between the draw-off of the raffinate R and the supply of the desorbent D,
and conducting the process according to a predetermined configuration of zones (a, b, c, d) with:
a=number of adsorbent beds operating in zone 1;
b=number of adsorbent beds operating in zone 2;
c=number of adsorbent beds operating in zone 3;
d=number of adsorbent beds operating in zone 4;
said predetermined configuration being one of the following:
An SMB of 12 adsorbent beds operating according to configuration (2, 5, 3, 2),
or an SMB of 13 adsorbent beds operating according to configuration (2, 5, 4, 2),
or an SMB of 15 adsorbent beds operating according to configuration (2, 6, 4, 3).
2. A process according to claim 1, conducted with an SMB of 12 adsorbent beds operating according to configuration (2, 5, 3, 2).
3. A process according to claim 1, conducted with an SMB of 13 adsorbent beds operating according to configuration (2, 5, 4, 2).
4. A process according to claim 1, conducted with an SMB of 15 adsorbent beds operating according to configuration (2, 6, 4, 3).
5. A process according to claim 1, in which the desorbent comprises toluene or tetralin.
6. A process according to claim 5, in which the desorbent is toluene.
7. A process according to claim 1, in which the operation is conducted with a sufficient feedstock flow rate coupled with a sufficient solvent flow rate to result in a metaxylene purity of 99.5% by weight.
8. A process according to claim 7, wherein the adsorbent is a NaY zeolite, the desorbent is toluene and process is conducted at between 20 and 250\xb0 C., a pressure between the bubble pressure of xylenes at the operating temperature and 2 MPa are a volumetric ratio of desorbent to feedstock of between 0.5 and 6.