Abstract: A machine-implemented method of generating trapping-set information for use in LDPC-decoding processing of read signals generated, e.g., by sensing a storage medium, such as a magnetic platter. In one embodiment, the method can be implemented as an add-on to any other trapping-set search method in which the discovered trapping sets are evaluated to determine their influence on the overall bit-error rate and/or error-floor characteristics of the LDPC decoder. The method can advantageously reuse at least some of the computational results obtained during this evaluation, thereby requiring a relatively small amount of additional computations, while providing a significant benefit of discovering many more trapping sets in addition to the ones that are being evaluated.

Abstract: In one embodiment, a machine-implemented method programs a heterogeneous multi-processor computer system to run a plurality of program modules, wherein each program module is to be run on one of the processors The system includes a plurality of processors of two or more different processor types. According to the recited method, machine-implemented offline processing is performed using a plurality of SIET tools of a scheduling information extracting toolkit (SIET) and a plurality of SBT tools of a schedule building toolkit (SBT). A program module applicability analyzer (PMAA) determines whether a first processor of a first processor type is capable of running a first program module without compiling the first program module. Machine-implemented online processing is performed using realtime data to test the scheduling software and the selected schedule solution.

Abstract: A machine-implemented method of generating trapping-set information for use in LDPC-decoding processing of read signals generated, e.g., by sensing a storage medium, such as a magnetic platter. In one embodiment, the method can be implemented as an add-on to any other trapping-set search method in which the discovered trapping sets are evaluated to determine their influence on the overall bit-error rate and/or error-floor characteristics of the LDPC decoder. The method can advantageously reuse at least some of the computational results obtained during this evaluation, thereby requiring a relatively small amount of additional computations, while providing a significant benefit of discovering many more trapping sets in addition to the ones that are being evaluated.

Abstract: In one embodiment, a machine-implemented method programs a heterogeneous multi-processor computer system to run a plurality of program modules, wherein each program module is to be run on one of the processors The system includes a plurality of processors of two or more different processor types. According to the recited method, machine-implemented offline processing is performed using a plurality of SIET tools of a scheduling information extracting toolkit (SIET) and a plurality of SBT tools of a schedule building toolkit (SBT). A program module applicability analyzer (PMAA) determines whether a first processor of a first processor type is capable of running a first program module without compiling the first program module. Machine-implemented online processing is performed using realtime data to test the scheduling software and the selected schedule solution.

Abstract: In one embodiment, a heterogeneous multi-processor computer system includes (i) a plurality of dedicated processors (DPs), each DP configured to implement one or more program modules during runtime operations; (ii) two or more control processors (CPs), each CP configured to run scheduling software for controlling the runtime operations by a corresponding subset of DPs; and (iii) one or more buses interconnecting the DPs and CPs. Each CP is configured to vary timing of implementation of the program modules for the corresponding subset of DPs based on resource availability, and each CP is configured to vary timing of data transfers by the corresponding subset of DPs based on resource availability.

Abstract: In one embodiment, a machine-implemented method programs a heterogeneous multi-processor computer system to run a plurality of program modules, wherein each program module is to be run on one of the processors. The system comprising a plurality of processors of two or more different processor types. Machine-implemented offline processing is performed using a plurality of SIET tools of a scheduling information extracting toolkit (SIET) and a plurality of SBT tools of a schedule building toolkit (SBT). Machine-implemented online processing is performed using realtime data to test the scheduling software and the selected schedule solution. A Source Code Generator (SCG) integrates scheduling information for the selected schedule solution into the scheduling software for a first processor such that the scheduling information is compiled with the scheduling software.