Methods and systems relating to the field of parallel computing are disclosed herein. The methods and systems disclosed include approaches for sparsity uniformity enforcement for a set of computational nodes which are used to execute a complex computation. A disclosed method includes determining a sparsity distribution in a set of operand data, and generating, using a compiler, a set of instructions for executing, using the set of operand data and a set of processing cores, a complex computation. Alternatively, the method includes altering the operand data. The method also includes distributing the set of operand data to the set of processing cores for use in executing the complex computation in accordance with the set of instructions. Either the altering is conducted to, or the compiler is programmed to, balance the sparsity distribution among the set of processing cores.

A method, a computer program product, and a system for allocating a variable into storage class memory during compilation of a program. The method includes selecting a variable recorded in a symbol table during compilation and computing a variable size of the variable by analyzing attributes related to the variable. The method further includes computing additional attributes relating to the variable. The method also includes computing a control flow graph and analyzing the control flow graph and the additional attributes to determine an allocation location for the variable. The method further includes allocating the variable into a storage class memory based on the analysis performed.

A compiler device, for generating an instruction sequence to be executed by an arithmetic processing device, includes at least one memory and at least one processor. The at least one processor is configured to receive a first instruction sequence for a first process and a second instruction sequence for a second process to be executed after the first process; generate third instructions, each third instruction being generated by merging a first instruction included in the first instruction sequence and a second instruction included in the second instruction sequence; and generate a third instruction sequence by concatenating the third instructions, instructions included in the first instruction sequence that are not merged into the third instructions, and instructions other than the second instruction among the plurality of instructions included in the second instruction sequence that are not merged into the one or more third instructions.

Systems and methods method for mapping between function calls and entities of the computer program. The method includes executing a computer program in a first computing environment; determining a first entity of the computer program to track; assigning an identifier to the first entity; determining the first entity has been accessed by at least one function call; and mapping the at least one function call with the identifier of the first entity; generating a cluster including the at least one function, wherein the cluster may be executed independently from the rest of the computer program.

Disclosed are a graph instruction processing method and apparatus, which relates to the field of computer technologies One example method includes: detecting whether a first graph instruction has a conditional instruction element; and when the first graph instruction has the conditional instruction element, determining that the first graph instruction is a conditional execution instruction, and processing the first graph instruction when both data flow information and control flow information of the first graph instruction are in a ready state; or when the first graph instruction does not have a conditional instruction element, determining that the first graph instruction is a non-conditional execution instruction, and processing the first graph instruction when data flow information of the first graph instruction is in a ready state.

Methods and systems relating to the field of parallel computing are disclosed herein. The methods and systems disclosed include approaches for sparsity uniformity enforcement for a set of computational nodes which are used to execute a complex computation. A disclosed method includes determining a sparsity distribution in a set of operand data, and generating, using a compiler, a set of instructions for executing, using the set of operand data and a set of processing cores, a complex computation. Alternatively, the method includes altering the operand data. The method also includes distributing the set of operand data to the set of processing cores for use in executing the complex computation in accordance with the set of instructions. Either the altering is conducted to, or the compiler is programmed to, balance the sparsity distribution among the set of processing cores.

Among other things, a machine-based method comprises receiving an application specification comprising one or more algorithms. Each algorithm is not necessarily suitable for concurrent execution on multiple nodes in parallel. One or more different object classes are grouped into one or more groups, each being appropriate for executing the one or more algorithms of the application specification. The executing involves data that is available in objects of the object classes. A user is enabled to code an algorithm of the one or more algorithms for one group in a single threaded environment without regard to concurrent execution of the algorithm on multiple nodes in parallel. An copy of the coded algorithm is distributed to each of the multiple nodes, without needing additional coding. The coded algorithm is caused to be executed on each node in association with at least one instance of a group independently of and in parallel to executing the other copies of the coded algorithm on the other nodes.

A computer generates a parallel program, based on an analysis of a single program that includes a plurality of tasks written for a single-core microcomputer, by parallelizing parallelizable tasks for a multi-core processor having multiple cores. The computer includes a macro task (MT) group extractor that analyzes, or finds, a commonly-accessed resource commonly accessed by the plurality of tasks, and extracts a plurality of MTs showing access to such commonly-accessed resource. Then, the computer uses an allocation restriction determiner to allocate the extracted plural MTs to the same core in the multi-core processor. By devising a parallelization method described above, an overhead in an execution time of the parallel program by the multi-core processor is reduced, and an in-vehicle device is enabled to execute each of the MTs in the program optimally.

A computer obtains invalidation information that shows ignorable data dependency relationships from among a plurality of data dependency relationships, and extracts a synchronous-dependent relationship from among the ignorable data dependency relationships that are shown as a write-write to the same data by the invalidation information. Then, the computer generates a parallel program for maximizing the number of parallelized macro tasks by ignoring other data dependency relationships other than the extracted synchronous-dependent relationship while preventing simultaneous write to the same data by two macro tasks having the synchronous-dependent relationship.

Methods for analyzing and improving a target computer application and corresponding systems and computer-readable mediums. A method includes receiving the target application. The method includes generating a parallel control flow graph (ParCFG) corresponding to the target application. The method includes analyzing the ParCFG by the computer system. The method includes generating and storing the modified ParCFG for the target application.