Abstract: Systems and methods for dynamic granularity control of parallelized work in a heterogeneous multi-processor portable computing device (PCD) are provided. During operation a first parallelized portion of an application executing on the PCD is identified. The first parallelized portion comprising a plurality of threads for parallel execution on the PCD. Performance information is obtained about a plurality of processors of the PCD, each of the plurality of processors corresponding to one of the plurality of threads. A number M of workload partition granularities for the plurality of threads is determined, and a total execution cost for each of the M workload partition granularities is determined. An optimal granularity comprising a one of the M workload partition granularities with a lowest total execution cost is determined, and the first parallelized portion is partitioned into a plurality of workloads having the optimal granularity.

Abstract: Systems and methods for dynamic granularity control of parallelized work in a heterogeneous multi-processor portable computing device (PCD) are provided. During operation a first parallelized portion of an application executing on the PCD is identified. The first parallelized portion comprising a plurality of threads for parallel execution on the PCD. Performance information is obtained about a plurality of processors of the PCD, each of the plurality of processors corresponding to one of the plurality of threads. A number M of workload partition granularities for the plurality of threads is determined, and a total execution cost for each of the M workload partition granularities is determined An optimal granularity comprising a one of the M workload partition granularities with a lowest total execution cost is determined, and the first parallelized portion is partitioned into a plurality of workloads having the optimal granularity.

Abstract: Systems, methods, and computer programs are disclosed for performing runtime auto-parallelization of application code. One embodiment of such a method comprises receiving application code to be executed in a multi-processor system. The application code comprises an injected code cost computation expression for at least one loop in the application code defining a serial workload for processing the loop. A runtime profitability check of the loop is performed based on the injected code cost computation expression to determine whether the serial workload can be profitably parallelized. If the serial workload can be profitably parallelized, the loop is executed in parallel using two or more processors in the multi-processor system.

Abstract: Generating parallelized executable code from input code includes statically analyzing the input code to determine aspects of data flow and control flow of the input code; dynamically analyzing the input code to determine additional aspects of data flow and control flow of the input code; generating an intermediate representation of the input code based at least in part on the aspects of data flow and control flow of the input code identified by the static analysis and the additional aspects of data and control flow of the input code identified by the dynamic analysis; and processing the intermediate representation to determine portions of the intermediate representation that are eligible for parallel execution; and generating parallelized executable code from the processed intermediate representation.

Abstract: Generating parallelized executable code from input code includes statically analyzing the input code to determine aspects of data flow and control flow of the input code; dynamically analyzing the input code to determine additional aspects of data flow and control flow of the input code; generating an intermediate representation of the input code based at least in part on the aspects of data flow and control flow of the input code identified by the static analysis and the additional aspects of data and control flow of the input code identified by the dynamic analysis; and processing the intermediate representation to determine portions of the intermediate representation that are eligible for parallel execution; and generating parallelized executable code from the processed intermediate representation