Abstract

Unlike conventional or otherwise known decomposition methods like standard functional decomposition, time-affecting linear pathway (TALP) decomposition, or output-affecting linear pathways (OALPs) from the decomposition of TALPs, advanced output-affecting linear pathway (A-OALP) decomposition from algorithms separate the output variables of each execution pathway such that there is only one output variable per A-OALP, converting an algorithm into a set of process groups. A-OALPs extend parallelization by combining task-like parallelism with dynamic loop parallelism. The lightweight nature of A-OALPs allows for persistent thread and code management.

IPC Classes  ?

• G06F 8/40 - Transformation of program code
• G06F 8/41 - Compilation

Abstract

Unlike conventional or otherwise known decomposition methods like standard functional decomposition, time-affecting linear pathway (TALP) decomposition, or output-affecting linear pathways (OALPs) from the decomposition of TALPs, advanced output-affecting linear pathway (A-OALP) decomposition from algorithms separate the output variables of each execution pathway such that there is only one output variable per A-OALP, converting an algorithm into a set of process groups. A-OALPs extend parallelization by combining task-like parallelism with dynamic loop parallelism. The lightweight nature of A-OALPs allows for persistent thread and code management.

IPC Classes  ?

• G06F 8/40 - Transformation of program code
• G06F 8/41 - Compilation

Abstract

Computer processing and outcome prediction systems and methods used to generate algorithm time prediction polynomials, inverse algorithm time prediction polynomials, determine race conditions, determine when a non-linear algorithm can be treated as if it were linear, as well as automatically generate parallel and quantum solutions from classical software or from the relationship between monotonic attribute values.

IPC Classes  ?

• G06F 5/01 - Methods or arrangements for data conversion without changing the order or content of the data handled for shifting, e.g. justifying, scaling, normalising
• G06F 8/41 - Compilation
• G06F 16/2455 - Query execution
• G06F 17/15 - Correlation function computation

Abstract

Computer processing and outcome prediction systems and methods used to generate algorithm time prediction polynomials, inverse algorithm time prediction polynomials, determine race conditions, determine when a non-linear algorithm can be treated as if it were linear, as well as automatically generate parallel and quantum solutions from classical software or from the relationship between monotonic attribute values.

IPC Classes  ?

• G06F 8/40 - Transformation of program code
• G06F 5/01 - Methods or arrangements for data conversion without changing the order or content of the data handled for shifting, e.g. justifying, scaling, normalising
• G06F 8/41 - Compilation
• G06F 16/2455 - Query execution
• G06F 17/15 - Correlation function computation

Abstract

Concepts of time-affecting linear pathways (TALPs) decomposed from existing application source code, algorithms, processes, software modules, and functions, are extended. For instance, T-polynomials can be expanded to define when the interaction of high-order polynomials can be treated as if they were linear functions using a new type of T-polynomial. The number of inherent analytics that are extractable from TALPs of an algorithm or source code can be expanded to include the prediction polynomials of advanced time complexity, advanced space complexity, resource complexity, and output complexity along with their inverses. An overlay to the TALP execution pathway is defined, allowing for input variable sensitivity analysis. Further, automatic detection and quantification of context variables are provided for more accurate sensor analysis.

IPC Classes  ?

• G06F 17/11 - Complex mathematical operations for solving equations

Abstract

Computer processing and outcome prediction systems and methods used to generate algorithm time prediction polynomials, inverse algorithm time prediction polynomials, determine race conditions, determine when a non-linear algorithm can be treated as if it were linear, as well as automatically generate parallel and quantum solutions from classical software or from the relationship between monotonic attribute values.

IPC Classes  ?

• G06F 8/40 - Transformation of program code
• G06F 5/01 - Methods or arrangements for data conversion without changing the order or content of the data handled for shifting, e.g. justifying, scaling, normalising
• G06F 8/41 - Compilation
• G06F 16/2455 - Query execution
• G06F 17/15 - Correlation function computation

Abstract

System, methods, and computer programs for executing time-affecting linear pathways (TALPs) using distributed mobile applications as platforms. The systems, methods, and computer programs can use currently unused mobile device compute cycles for commercial-grade computation based on TALPs. TALPs can take the place of software functions, modules, and software applications that are provided for the analysis of customer data. TALPs can be used to perform strong parallel processing.

IPC Classes  ?

• H04L 67/00 - Network arrangements or protocols for supporting network services or applications
• H04L 67/10 - Protocols in which an application is distributed across nodes in the network
• H04L 67/568 - Storing data temporarily at an intermediate stage, e.g. caching
• H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks

Abstract

Systems, methods, and computer program products are provided for automatically finding, testing, and attaching code directly to software requirements. The requirements can be used to define, find, test and attach files and input/output interface displays to create new software applications.

IPC Classes  ?

• G06F 9/44 - Arrangements for executing specific programs
• G06F 8/10 - Requirements analysisSpecification techniques
• G06F 8/36 - Software reuse
• G06F 8/40 - Transformation of program code

Abstract

Computer processing and outcome prediction systems and methods used to generate algorithm time prediction polynomials, inverse algorithm time prediction polynomials, determine race conditions, determine when a non-linear algorithm can be treated as if it were linear, as well as automatically generate parallel and quantum solutions from classical software or from the relationship between monotonic attribute values.

IPC Classes  ?

• G06F 8/41 - Compilation
• G06F 5/01 - Methods or arrangements for data conversion without changing the order or content of the data handled for shifting, e.g. justifying, scaling, normalising
• G06F 16/2455 - Query execution
• G06F 17/15 - Correlation function computation

Abstract

Systems, methods, and computer program products are provided for automatically finding, testing, and attaching code directly to software requirements. The requirements can be used to define, find, test and attach files and input/output interface displays to create new software applications.

IPC Classes  ?

• G06F 9/44 - Arrangements for executing specific programs
• G06F 8/10 - Requirements analysisSpecification techniques
• G06F 8/36 - Software reuse
• G06F 8/40 - Transformation of program code

Abstract

Systems, methods, and computer program products are provided for automatically finding, testing, and attaching code directly to software requirements. The requirements can be used to define, find, test and attach files and input/output interface displays to create new software applications.

IPC Classes  ?

• G06F 9/44 - Arrangements for executing specific programs
• G06F 9/45 - Compilation or interpretation of high level programme languages

Abstract

Systems, methods, and computer program products are provided for space-time complexity speedup for computer algorithms in terms of processing time as a function of dataset size and type, showing serial, sub-linear, linear and superlinear speedup. Serial and parallel effects can be processed and calculated from a decomposed algorithm's sequential functional components, allowing the identification and use of the parallelizable components.

IPC Classes  ?

• G06N 5/02 - Knowledge representationSymbolic representation
• G06F 11/34 - Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation