Virtual Supercomputer

• US 20110004566A1
• Filed: 06/28/2010
• Published: 01/06/2011
• Est. Priority Date: 04/09/2003
• Status: Active Grant

• Current Assignee: Veriscape, Inc.
• Original Assignee: Veriscape, Inc.
• Inventors: Berkowitz, Gary Charles, Wurtz, Charles Christopher
• Granted Patent: US 8,271,259 B2
• Time in Patent Office: Days
• US Class Current: 705/36.R
• CPC Class Codes: G06F 16/22 Indexing; Data structures therefor; Storage structures
• CPC Class Codes: G06F 9/3004 to perform operations on memory
• CPC Class Codes: G06F 9/5077 Logical partitioning of resources; Management or configuration of virtualized resources specific details on emulation or internal functioning of virtual machines G06F9/455
• CPC Class Codes: G06Q 40/06 Asset management; Financial planning or analysis

Abstract

The virtual supercomputer is an apparatus, system and method for generating information processing solutions to complex and/or high-demand/high-performance computing problems, without the need for costly, dedicated hardware supercomputers, and in a manner far more efficient than simple grid or multiprocessor network approaches. The virtual supercomputer consists of a reconfigurable virtual hardware processor, an associated operating system, and a set of operations and procedures that allow the architecture of the system to be easily tailored and adapted to specific problems or classes of problems in a way that such tailored solutions will perform on a variety of hardware architectures, while retaining the benefits of a tailored solution that is designed to exploit the specific and often changing information processing features and demands of the problem at hand.

Claims

1-3. -3. (canceled)

4. A system to analyze a plurality of different scenarios for an object for various conditions to determine a state of said object, wherein said scenarios are represented by one or more nodes, said system comprising:

a computer system to dynamically configure and emulate a hardware architecture of a processing system to process said one or more nodes to determine said state for said object, wherein at least one of said nodes includes data for said object and said computer system includes;

an instantiation manager to create and delete said nodes and to configure said nodes in a topology representing said scenarios;

a population manager to determine and store said data for said object within said nodes;

a navigation manager to traverse said topology in response to a query from a user and process said nodes with a first degree of precision to identify nodes of said scenarios for further analysis to determine said state of said object; and

an interpolation manager to analyze said identified nodes with a second greater degree of precision to determine said state of said object.

5. The system of claim 4, wherein said computer system includes at least one of local and distributed networks of processing systems.

6. The system of claim 4, wherein each node includes one or more of numeric tags, character tags, boolean flags, numeric values, character values, object identifications, database-record identifications, simple arrays, variable-density multidimensional arrays, symbolic functions, mathematical functions, connection pointers to other nodes, function pointers, lookup-table list pointers, linked-lists, and pointers to other topologies, data representations, procedures, or other emulated hardware architectures.

7. The system of claim 4, wherein said topology includes at least one of independent point-clouds, ordered sets of points, acyclic graphs, cyclic graphs, balanced trees, recombining graphs, meshes, and lattices.

8. The system of claim 4, wherein said computer system includes:

a process unit to manage processing of said nodes; and

a toolbox unit to enable performance of frequently-used tasks.

9. The system of claim 4, wherein said computer system includes daemons that operate concurrently to perform tasks including at least one of collecting garbage, pruning trees, condensing redundancies, and processing edit-queues.

10. The system of claim 4, wherein said computer system further includes:

a node unit to produce new nodes based on at least one of interpolating with finer granularity around said identified nodes, and extrapolating said data of said identified nodes.

11. The system of claim 4, wherein said computer system further includes:

an elaboration unit to elaborate said data of said identified nodes.

12. The system of claim 4, wherein said hardware architecture includes a non-Von Neumann architecture.

13. The system of claim 4, wherein said object includes a portfolio of financial instruments, and each of at least one of said nodes includes data for a corresponding type of financial instrument.

14. The system of claim 13, wherein said data for a corresponding type of financial instrument includes a price.

15. The system of claim 13, wherein said state of said portfolio indicates portfolio risk.

16. The system of claim 13, wherein said types of said financial instruments are reduced based on statistical sampling.

17. The system of claim 4, wherein said computer system further includes:

a computation module to pre-compute said data within said nodes to enable determination of said state of said object in near real-time.

18. The system of claim 17, wherein said data within each of said at least one node is defined by corresponding parameters, and said computation module pre-computes said data based on a reduced set of said corresponding parameters to generate a parametric hypercube for said pre-computed data that is accessed during processing of said nodes.

19. The system of claim 4, wherein said state of said object is determined based on the data of the set of nodes for a given scenario.

20. A program product apparatus including a computer useable memory with computer program logic stored therein to enable a computer system to analyze a plurality of different scenarios for an object for various conditions to determine a state of said object, wherein said scenarios are represented by one or more nodes, said program product apparatus comprising:

a virtual machine unit to dynamically configure and emulate a hardware architecture of a processing system to process said one or more nodes to determine said state for said object, wherein at least one of said nodes includes data for said object;

an instantiation manager to create and delete said nodes and to configure said nodes in a topology representing said scenarios;

a population manager to determine and store said data for said object within said nodes;

a navigation manager to traverse said topology in response to a query from a user and process said nodes with a first degree of precision to identify nodes of said scenarios for further analysis to determine said state of said object; and

an interpolation manager to analyze said identified nodes with a second greater degree of precision to determine said state of said object.

21. The apparatus of claim 20, wherein said computer system includes at least one of local and distributed networks of processing systems.

22. The apparatus of claim 20, wherein each node includes one or more of numeric tags, character tags, boolean flags, numeric values, character values, object identifications, database-record identifications, simple arrays, variable-density multidimensional arrays, symbolic functions, mathematical functions, connection pointers to other nodes, function pointers, lookup-table list pointers, linked-lists, and pointers to other topologies, data representations, procedures, or other emulated hardware architectures.

23. The apparatus of claim 20, wherein said topology includes at least one of independent point-clouds, ordered sets of points, acyclic graphs, cyclic graphs, balanced trees, recombining graphs, meshes, and lattices.

24. The apparatus of claim 20, further including:

a process unit to manage processing of said nodes; and

a toolbox unit to enable performance of frequently-used tasks.

25. The apparatus of claim 20, further including daemons that operate concurrently to perform tasks including at least one of collecting garbage, pruning trees, condensing redundancies, and processing edit-queues.

26. The apparatus of claim 20, further including:

a node unit to produce new nodes based on at least one of interpolating with finer granularity around said identified nodes, and extrapolating said data of said identified nodes.

27. The apparatus of claim 20, further including:

an elaboration unit to elaborate said data of said identified nodes.

28. The apparatus of claim 20, wherein said hardware architecture includes a non-Von Neumann architecture.

29. The apparatus of claim 20, wherein said object includes a portfolio of financial instruments, and each of at least one of said nodes includes data for a corresponding type of financial instrument.

30. The apparatus of claim 29, wherein said data for a corresponding type of financial instrument includes a price.

31. The apparatus of claim 29, wherein said state of said portfolio indicates portfolio risk.

32. The apparatus of claim 29, wherein said types of said financial instruments are reduced based on statistical sampling.

33. The apparatus of claim 20, further including:

a computation module to pre-compute said data within said nodes to enable determination of said state of said object in near real-time.

34. The apparatus of claim 33, wherein said data within each of said at least one node is defined by corresponding parameters, and said computation module pre-computes said data based on a reduced set of said corresponding parameters to generate a parametric hypercube for said pre-computed data that is accessed during processing of said nodes.

35. The apparatus of claim 20, wherein said state of said object is determined based on the data of the set of nodes for a given scenario.

36. A method of analyzing a plurality of different scenarios for an object, via a computer system, for various conditions to determine a state of said object, wherein said scenarios are represented by one or more nodes, said method comprising:

(a) dynamically configuring and emulating a hardware architecture of a processing system, via said computer system, to process said one or more nodes to determine said state for said object, wherein at least one of said nodes includes data for said object, and step (a) further includes;

creating and deleting said nodes and configuring said nodes in a topology representing said scenarios;

determining and storing said data for said object within said nodes;

traversing said topology in response to a query from a user and processing said nodes with a first degree of precision to identify nodes of said scenarios for further analysis to determine said state of said object; and

analyzing said identified nodes with a second greater degree of precision to determine said state of said object.

37. The method of claim 36, wherein said computer system includes at least one of local and distributed networks of processing systems.

38. The method of claim 36, wherein each node includes one or more of numeric tags, character tags, boolean flags, numeric values, character values, object identifications, database-record identifications, simple arrays, variable-density multidimensional arrays, symbolic functions, mathematical functions, connection pointers to other nodes, function pointers, lookup-table list pointers, linked-lists, and pointers to other topologies, data representations, procedures, or other emulated hardware architectures.

39. The method of claim 36, wherein said topology includes at least one of independent point-clouds, ordered sets of points, acyclic graphs, cyclic graphs, balanced trees, recombining graphs, meshes, and lattices.

40. The method of claim 36, wherein step (a) further includes:

managing processing of said nodes; and

enabling performance of frequently-used tasks.

41. The method of claim 36, wherein daemons of said computer system operate concurrently to perform tasks including at least one of collecting garbage, pruning trees, condensing redundancies, and processing edit-queues.

42. The method of claim 36, wherein step (a) further includes:

producing new nodes based on at least one of interpolating with finer granularity around said identified nodes, and extrapolating said data of said identified nodes.

43. The method of claim 36, wherein step (a) further includes:

elaborating said data of said identified nodes.

44. The method of claim 36, wherein said hardware architecture includes a non-Von Neumann architecture.

45. The method of claim 36, wherein said object includes a portfolio of financial instruments, and each of at least one of said nodes includes data for a corresponding type of financial instrument.

46. The method of claim 45, wherein said data for a corresponding type of financial instrument includes a price.

47. The method of claim 45, wherein said state of said portfolio indicates portfolio risk.

48. The method of claim 45, wherein said types of said financial instruments are reduced based on statistical sampling.

49. The method of claim 36, wherein step (a) further includes:

pre-computing said data within said nodes to enable determination of said state of said object in near real-time.

50. The method of claim 49, wherein said data within each of said at least one node is defined by corresponding parameters, and said data is pre-computed based on a reduced set of said corresponding parameters to generate a parametric hypercube for said pre-computed data that is accessed during processing of said nodes.

51. The method of claim 36, wherein said state of said object is determined based on the data of the set of nodes for a given scenario.