Abstract:
A multi-core system and a method for processing data in parallel in the multi-core system are provided. In the multi-core system, partitioning and allocating of data may be dynamically controlled based on local memory information. Thus, it is possible to increase an availability of a Central Processing Unit (CPU) and a local memory, and is possible to improve a performance of data parallel processing.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims the benefit of Korean Patent Application No. 10-2011-0008411, filed on Jan. 27, 2011, in the Korean Intellectual Property Office, the disclosure of which is incorporated herein by reference. 
     BACKGROUND 
     1. Field 
     One or more example embodiments of the following description relate to a multi-core system, and a method for processing data in parallel in the multi-core system. 
     2. Description of the Related Art 
     A conventional single-core system using a single processor has been replaced with a multi-core system using multiple processors, due to limitations in improvements of clock and power problems in the single-core system. 
     Additionally, such a change due to limitations in hardware inevitably leads to a change in software. Since conventional software is written based on only a single core, it is impossible to expect an improvement in performance when the conventional software is operated in multiple cores. This is because the single-core system is completely different in structure from the multi-core system. Accordingly, to solve the problem, researches have been continuously conducted on an Operating System (OS) for multi-core, a parallel programming model enabling parallel processing, a dynamic execution environment, and the like. 
     SUMMARY 
     The foregoing and/or other aspects are achieved by providing a method for processing data in parallel in a multi-core system, including acquiring local memory information regarding a plurality of local memories, the plurality of local memoires respectively corresponding to a plurality of cores, and partitioning the data into a plurality of pieces of subdata, based on the acquired local memory information. 
     The foregoing and/or other aspects are also achieved by providing a method for processing data in parallel in a multi-core system, including acquiring local memory information regarding a local memory, the local memory corresponding to a core used to process subdata of the data among a plurality of cores, computing a size of a part of the subdata, based on the acquired local memory information, and allocating the part of the subdata to the core used to process the subdata. 
     The foregoing and/or other aspects are also achieved by providing a method for processing data in parallel in a multi-core system, including acquiring local memory information regarding a local memory during a runtime for processing the data in parallel, the local memory corresponding to a core, and performing multi-buffering on the data when an available capacity of the local memory is increased, and performing repartitioning on the data when the available capacity is reduced. 
     The foregoing and/or other aspects are achieved by providing a multi-core system for processing data in parallel, including a plurality of local memories, a plurality of cores respectively including the plurality of local memories, a shared memory shared by the plurality of cores, and a controller to control the plurality of cores to process the data in parallel, wherein the controller acquires local memory information regarding the plurality of local memories, and partitions the data into a plurality of pieces of subdata, based on the acquired local memory information. 
     The foregoing and/or other aspects are also achieved by providing a multi-core system for processing data in parallel, including a plurality of local memories, a plurality of cores respectively including the plurality of local memories, a shared memory shared by the plurality of cores, and a controller to control the plurality of cores to process the data in parallel, wherein the controller acquires local memory information regarding a local memory corresponding to a core used to process subdata of the data among the plurality of cores, computes a size of a part of the subdata based on the acquired local memory information, and allocates the part of the subdata to the core used to process the subdata. 
     The foregoing and/or other aspects are also achieved by providing a multi-core system for processing data in parallel, including a plurality of local memories, a plurality of cores respectively including the plurality of local memories, a shared memory shared by the plurality of cores, and a controller to control the plurality of cores to process the data in parallel, wherein the controller acquires local memory information regarding the plurality of local memories during a runtime for processing the data in parallel, performs multi-buffering on the data when an available capacity of a local memory among the plurality of local memories is increased, and performs repartitioning on the data when the available capacity is reduced. 
     The foregoing and/or other aspects are achieved by providing a method for processing data in parallel in a multi-core system comprising a plurality of cores and a plurality of corresponding local memories. The method includes acquiring local memory information regarding the plurality of local memories, and computing a size of subdata to be allocated to a core of the plurality of cores based on the local memory information. 
     Additional aspects, features, and/or advantages of example embodiments will be set forth in part in the description which follows and, in part, will be apparent from the description, or may be learned by practice of the disclosure. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       These and/or other aspects and advantages will become apparent and more readily appreciated from the following description of the example embodiments, taken in conjunction with the accompanying drawings of which: 
         FIG. 1  illustrates a diagram of a multi-core system according to example embodiments; 
         FIG. 2  illustrates a diagram of a multi-buffering operation performed by the multi-core system of  FIG. 1 ; 
         FIG. 3  illustrates a diagram of a repartitioning operation performed by the multi-core system of  FIG. 1 ; and 
         FIGS. 4 through 6  illustrate flowcharts of methods for processing data in parallel in a multi-core system according to example embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     Reference will now be made in detail to example embodiments, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the like elements throughout. Example embodiments are described below to explain the present disclosure by referring to the figures. 
       FIG. 1  illustrates a diagram of a multi-core system  100  according to example embodiments. 
     Referring to  FIG. 1 , the multi-core system  100  may include, for example, a plurality of local memories  121 ,  122 ,  123 , and  124 , a plurality of cores  111 ,  112 ,  113 , and  114 , a shared memory  130 , and a controller  140 . Here, the plurality of cores  111 ,  112 ,  113 , and  114  may respectively include the plurality of local memories  121 ,  122 ,  123 , and  124 . The shared memory  130  may be shared by the plurality of cores  111 ,  112 ,  113 , and  114 , and the controller  140  may control the plurality of cores  111 ,  112 ,  113 , and  114  to process data in parallel. 
     According to an aspect, the controller  140  may acquire local memory information regarding the plurality of local memories  121 ,  122 ,  123 , and  124 . 
     The local memory information may include, for example, information regarding a total size, a currently used size, an available size, a frequency of use, an access cost, and the like, with respect to each of the plurality of local memories  121 ,  122 ,  123 , and  124 . 
     The controller  140  may partition data into a plurality of pieces of subdata, based on the acquired local memory information. More specifically, the controller  140  may determine a partition ratio of the plurality of pieces of subdata, based on the local memory information, and may partition the data into the plurality of pieces of subdata based on the determined partition ratio. 
     According to an aspect, the controller  140  may partition data into a plurality of pieces of subdata based on a ratio of sizes of the plurality of local memories  121 ,  122 ,  123 , and  124 . For example, when the ratio of sizes of the four local memories  121 ,  122 ,  123 , and  124  is set to “3:1:2:1”, the controller  140  may partition data into four pieces of subdata at a size ratio of “3:1:2:1.” 
     Depending on example embodiments, the controller  140  may partition data into a plurality of sets of subdata, based on the local memory information. Here, each of the plurality of sets may include at least one piece of subdata. 
     According to an aspect, the controller  140  may acquire local memory information regarding a local memory corresponding to a subdata processing core among the plurality of cores  111 ,  112 ,  113 , and  114 . Here, the subdata processing core may be used to process subdata of the data. 
     Additionally, the controller  140  may compute a size of a part of subdata to be allocated to the subdata processing core, based on the acquired local memory information. 
     Moreover, the controller  140  may allocate the part of the subdata to the subdata processing core. 
     For example, when the core  112  processes subdata, the controller  140  may acquire local memory information regarding the local memory  122  corresponding to the core  112 . Additionally, the controller  140  may compute a size of a part of subdata to be allocated to the core  112 , based on the local memory information regarding the local memory  122 . Furthermore, the controller  140  may allocate the part of the subdata to the core  112 . 
     According to an aspect, the controller  140  may acquire local memory information regarding the plurality of local memories  121 ,  122 ,  123 , and  124 , during a runtime for processing data in parallel. 
     Additionally, when an available capacity of a local memory among the plurality of local memories  121 ,  122 ,  123 , and  124  is increased, the controller  140  may perform multi-buffering on the data. Conversely, when an available capacity of a local memory among the plurality of local memories  121 ,  122 ,  123 , and  124  is reduced, the controller  140  may perform repartitioning on the data. 
     According to an aspect, when the available capacity of the local memory is reduced again after the multi-buffering, the controller  140  may return a size of the local memory that corresponds to the reduced available capacity. 
     Hereinafter, multi-buffering and repartitioning performed by the controller  140  of the multi-core system  100  of  FIG. 1  will be further described with reference to  FIGS. 2 and 3 . 
       FIG. 2  illustrates a diagram of a multi-buffering operation performed by the multi-core system  100  of  FIG. 1 . 
     Referring to  FIG. 2 , the multi-core system  100  may process data by allocating subdata A 0   221 , and subdata A 1   222  to a local memory  211 . Here, when the local memory  211  is changed to a local memory  212  by an increase in an available capacity of the local memory  211  during a runtime for processing the subdata A 0   221 , the multi-core system  100  may perform multi-buffering on the data. In other words, since the increase in the available capacity of the local memory  211  leads to a change from the local memory  211  to the larger local memory  212 , the multi-core system  100  may upload, to the local memory  212 , subdata  223  obtained by combining subdata A 0   221  with subdata A 1   222 . 
     According to an aspect, when the available capacity of the local memory  211  is reduced again after the change to the local memory  212 , the multi-core system  100  may return to a size of the local memory  211  that corresponds to the reduced available capacity. 
       FIG. 3  illustrates a diagram of a repartitioning operation performed by the multi-core system  100  of  FIG. 1 . 
     Referring to  FIG. 3 , the multi-core system  100  may process data by allocating subdata A 0   321  to a local memory  311 . Here, when the local memory  311  is changed to a local memory  312  by a reduction in an available capacity of the local memory  311  during a runtime for processing the subdata A 0   321 , the multi-core system  100  may perform repartitioning on the data. Since the reduction in the available capacity of the local memory  311  leads to a change from the local memory  311  to the smaller local memory  312 , the multi-core system  100  may repartition the subdata A 0   321  into subdata A 0 ′  322  and subdata A 0 ″  323 , and may upload the subdata A 0 ′  322  to the local memory  312 . 
       FIGS. 4 through 6  illustrate flowcharts of methods for processing data in parallel in a multi-core system according to example embodiments. 
     Referring to  FIG. 4 , in operation  410 , local memory information regarding a plurality of local memories may be acquired. Here, the plurality of local memories may respectively correspond to a plurality of cores. 
     The local memory information may include, for example, information regarding a total size, a currently used size, an available size, a frequency of use, an access cost, and the like, with respect to each of the plurality of local memories. 
     In operation  420 , the data may be partitioned into a plurality of pieces of subdata based on the acquired local memory information. 
     More specifically, a partition ratio of the plurality of pieces of subdata may be determined based on the local memory information, and the data may be partitioned into the plurality of pieces of subdata based on the determined partition ratio. 
     According to an aspect, the data may be partitioned into a plurality of pieces of subdata based on a ratio of sizes of the plurality of local memories. For example, when a ratio of sizes of four local memories is set to “3:1:2:1”, data may be partitioned into four pieces of subdata at a size ratio of “3:1:2:1.” 
     Depending on example embodiments, data may be partitioned into a plurality of sets of subdata, based on the local memory information. Here, each of the plurality of sets may include at least one piece of subdata. 
     Referring to  FIG. 5 , in operation  510 , local memory information regarding a local memory corresponding to a subdata processing core among a plurality of cores may be acquired. Here, the subdata processing core may be used to process subdata of the data. 
     In operation  520 , a size of a part of subdata to be allocated to the subdata processing core may be computed based on the acquired local memory information. 
     In operation  530 , whether the computed size of the part of the subdata is greater than “0” may be determined. 
     When the computed size is greater than “0”, the part of the subdata may be allocated to the subdata processing core in operation  540 . 
     When the computed size is equal to or less than “0”, another core may be selected from among cores other than the subdata processing core in operation  550 , so that the selected core may process subdata. 
     Referring to  FIG. 6 , in operation  610 , local memory information regarding a local memory corresponding to a core may be acquired during a runtime for processing data in parallel. 
     In operation  620 , whether an available capacity of at least one local memory among the plurality of local memories is changed during the runtime may be determined based on the acquired local memory information. 
     Specifically, when the available capacity of the at least one local memory is increased, multi-buffering may be performed on the data in operation  631 . In operation  640 , subdata on which the multi-buffering has been performed may be allocated. 
     According to an aspect, when the available capacity of the at least one local memory is reduced again after the multi-buffering, a size of the at least one local memory corresponding to the reduced available capacity may be restored. 
     When the available capacity of the at least one local memory is reduced, repartitioning may be performed on the data in operation  632 . In operation  640 , subdata where the repartitioning is performed may be allocated. 
     The above-described example embodiments may be recorded in non-transitory computer-readable media or processor-readable media including program instructions to implement various operations embodied by a computer. The media may also include, alone or in combination with the program instructions, data files, data structures, and the like. The program instructions recorded on the media may be those specially designed and constructed for the purposes of the example embodiments, or they may be of the kind well-known and available to those having skill in the computer software arts. 
     Examples of non-transitory computer-readable media include magnetic media such as hard disks, floppy disks, and magnetic tape; optical media such as CD ROM disks and DVDs; magneto-optical media such as optical discs; and hardware devices that are specially configured to store and perform program instructions, such as read-only memory (ROM), random access memory (RAM), flash memory, and the like. Examples of program instructions include both machine code, such as produced by a compiler, and files containing higher level code that may be executed by the computer using an interpreter. The described hardware devices may be configured to act as one or more software modules in order to perform the operations of the above-described example embodiments, or vice versa. Any one or more of the software modules described herein may be executed by a dedicated processor unique to that unit or by a processor common to one or more of the modules. The methods for processing data in parallel in a multi-core system may be executed on a general purpose computer or processor or may be executed on a particular machine or processor such as the apparatuses described herein. 
     Although example embodiments have been shown and described, it would be appreciated by those skilled in the art that changes may be made in these example embodiments without departing from the principles and spirit of the disclosure, the scope of which is defined in the claims and their equivalents.