CN107220117B - Virtual task synthesis method and system under NUMA (non Uniform memory Access) architecture - Google Patents
Virtual task synthesis method and system under NUMA (non Uniform memory Access) architecture Download PDFInfo
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- CN107220117B CN107220117B CN201710380134.4A CN201710380134A CN107220117B CN 107220117 B CN107220117 B CN 107220117B CN 201710380134 A CN201710380134 A CN 201710380134A CN 107220117 B CN107220117 B CN 107220117B
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- G06—COMPUTING; CALCULATING OR COUNTING
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- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/48—Program initiating; Program switching, e.g. by interrupt
- G06F9/4806—Task transfer initiation or dispatching
- G06F9/4843—Task transfer initiation or dispatching by program, e.g. task dispatcher, supervisor, operating system
- G06F9/4881—Scheduling strategies for dispatcher, e.g. round robin, multi-level priority queues
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
- G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
- G06F9/5011—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resources being hardware resources other than CPUs, Servers and Terminals
- G06F9/5016—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resources being hardware resources other than CPUs, Servers and Terminals the resource being the memory
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Abstract
The application discloses a virtual task synthesis method and a virtual task synthesis system under a NUMA (non Uniform memory Access) architecture, wherein the method comprises the following steps: creating a task information base; calling task information in a task information base, synthesizing a corresponding task by using the called task information to obtain a virtual task, and performing information perfecting processing on an experience base by using an operation result of the virtual task; the experience base is a database used for recording information related to task operation. According to the method and the device, the task information base is established firstly, the task information in the task information base is called subsequently, so that the purpose of synthesizing the virtual task can be achieved, the experience information in the experience base can be perfected by using the running result of the virtual task, and the experience information can be perfected without waiting for the arrival of the real task, so that the collection efficiency of the experience information can be effectively improved.
Description
Technical Field
The invention relates to the technical field of NUMA (non Uniform memory Access), in particular to a virtual task synthesis method and system under a NUMA architecture.
Background
Currently, a NUMA Architecture (Non-uniform Memory Architecture) has a plurality of Memory nodes, each Memory node and a corresponding multi-core system form a Memory region, and an independent and private Memory controller is provided in each Memory region, so that when a thread accesses a local Memory node, a better experience is brought to a user in terms of access time.
In order to improve the performance of the system under the NUMA architecture, it is necessary to determine in advance empirical information corresponding to different tasks in the running process, such as empirical information including a resource optimization policy. However, if the corresponding experience information is determined only by the operation result of the real task in the actual operation process, a large amount of manpower and material resources are undoubtedly consumed, and the efficiency is very low.
Disclosure of Invention
In view of this, the present invention provides a virtual task synthesis method and system under a NUMA architecture, which can synthesize a virtual task, so that experience information can be improved by using a running result of the virtual task, and experience information can be improved without waiting for a real task to arrive, thereby improving the efficiency of collecting experience information. The specific scheme is as follows:
a virtual task synthesis method under a NUMA architecture comprises the following steps:
creating a task information base;
calling task information in the task information base, synthesizing a corresponding task by using the called task information to obtain a virtual task, and performing information perfecting processing on an experience base by using an operation result of the virtual task;
the experience base is a database used for recording information related to task operation.
Optionally, the process of creating the task information base includes:
and recording the task information of each real task running under the NUMA architecture to obtain the task information base.
Optionally, the process of creating the task information base includes:
recording task information of each real task running under the NUMA architecture to obtain an initial information base;
and classifying the information in the initial information set by using a machine learning algorithm to obtain the task information base.
Optionally, the machine learning algorithm is a data mining algorithm or a neural network algorithm.
Optionally, the process of retrieving the task information located in the task information base and synthesizing the corresponding task by using the retrieved task information includes:
calling out a plurality of groups of task information from the task information base by using an exhaustion method;
and synthesizing corresponding tasks by utilizing each group of task information to obtain a plurality of groups of virtual tasks.
The invention also correspondingly discloses a virtual task synthesis system under the NUMA architecture, which comprises the following steps:
the information base establishing module is used for establishing a task information base;
the task information calling module is used for calling the task information in the task information base;
the virtual task synthesis module is used for synthesizing a corresponding task by using the task information called out by the task information calling module to obtain a virtual task so as to complete information of the experience base by using the running result of the virtual task;
the experience base is a database used for recording information related to task operation.
Optionally, the information base creating module is specifically configured to record task information of each real task running under the NUMA architecture, so as to obtain the task information base.
Optionally, the information base creating module includes:
the information recording unit is used for recording the task information of each real task running under the NUMA architecture to obtain an initial information base;
and the information classification unit is used for classifying the information in the initial information set by using a machine learning algorithm to obtain the task information base.
Optionally, the information classification unit is specifically configured to classify the information in the initial information set by using a data mining algorithm or a neural network algorithm to obtain the task information base.
Optionally, the virtual task synthesizing module includes:
the information calling unit is used for calling out a plurality of groups of task information from the task information base by using an exhaustion method;
and the task synthesis unit is used for synthesizing corresponding tasks by utilizing each group of task information to obtain a plurality of groups of virtual tasks.
In the invention, the virtual task synthesis method under the NUMA architecture comprises the following steps: creating a task information base; calling task information in a task information base, synthesizing a corresponding task by using the called task information to obtain a virtual task, and performing information perfecting processing on an experience base by using an operation result of the virtual task; the experience base is a database used for recording information related to task operation.
Therefore, the invention firstly creates the task information base, then the aim of synthesizing the virtual task can be realized by calling the task information in the task information base, and then the experience information in the experience base can be perfected by using the running result of the virtual task without waiting for the arrival of the real task, thereby effectively improving the collection efficiency of the experience information.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
FIG. 1 is a flow diagram of a method for virtual task composition under another NUMA architecture, as disclosed in embodiments of the present invention;
fig. 2 is a schematic structural diagram of a virtual task composition system under a NUMA architecture, disclosed in an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the invention discloses a virtual task synthesis method under a NUMA (non Uniform memory Access) architecture, which is shown in figure 1 and comprises the following steps:
step S101: and creating a task information base.
It will be appreciated that the task information base described above is a database that stores task information for the tasks themselves. The task information includes a task type and/or a parameter type and/or a specific parameter value, and the task type of the corresponding task can be determined according to the task type and the specific parameter value.
In the practical application process of the NUMA architecture, the types of parameters included in different types of tasks may be different, for example, for a type a process task, the types of parameters may specifically include a parameter a, a parameter B, a parameter c, and a parameter d, and for a type B process task, the types of parameters may include a parameter a, a parameter e, a parameter f, and a parameter g. That is, there may be differences in the types of parameters that different types of tasks include. In addition, for different tasks belonging to the same type, their respective parameter values of the same parameter often have different parameter values. For example, for task 1 and task 2, which are both of type a, each of them contains the same type of parameter, but the corresponding parameter values will be different.
Step S102: and calling the task information in the task information base, synthesizing a corresponding task by using the called task information to obtain a virtual task, and performing information perfection processing on the experience base by using the running result of the virtual task.
The experience base is a database used for recording information related to task operation.
Therefore, the task information base is created firstly, the task information in the task information base is called subsequently, the purpose of synthesizing the virtual task can be achieved, the experience information in the experience base can be perfected by using the running result of the virtual task, and the experience information can be perfected without waiting for the arrival of the real task, so that the collection efficiency of the experience information can be effectively improved.
The embodiment of the invention discloses a virtual task synthesis method under a specific NUMA (non Uniform memory Access) architecture, which comprises the following steps:
step S21: and creating a task information base.
In a specific embodiment, the process of creating a task information base includes: and recording the task information of each real task running under the NUMA architecture to obtain a task information base.
That is, the embodiment of the present invention may specifically create the task information base by recording the task information of the real task, and specifically create the task information base by recording the task type, the parameter type, and the specific parameter value of the real task.
In another specific embodiment, the process of creating the task information base includes: the method comprises the steps of recording task information of each real task running under the NUMA framework to obtain an initial information base, and then classifying information in the initial information set by utilizing a machine learning algorithm to obtain the task information base.
It should be noted that the machine learning algorithm may be specifically a data mining algorithm or a neural network algorithm, for example, the initial information set is classified by a WEKA machine learning tool (WEKA, i.e., waikoto Environment for Knowledge Analysis), so as to obtain the task information base.
That is, after the task information of a large number of real tasks is recorded, the embodiment of the invention can classify the recorded task information by using a machine learning algorithm, so as to perform operation and maintenance processing on the task information base.
Step S22: and calling the task information in the task information base, synthesizing a corresponding task by using the called task information to obtain a virtual task, and performing information perfection processing on the experience base by using the running result of the virtual task.
In a specific embodiment, the process of retrieving task information located in a task information base and synthesizing a corresponding task by using the retrieved task information may specifically include: the method comprises the steps of firstly determining the task running information which is actually lacked in the current experience base, then determining the task type corresponding to the task running information which is actually lacked in the current experience base, then calling the corresponding task information from the task information base according to the determined task type, then synthesizing the corresponding task by using the task information, thus obtaining the corresponding virtual task, and obtaining the running result containing the corresponding task running information by running the virtual task, so that the experience base which is lacked in the corresponding task running information can be perfected by using the running result.
In another specific embodiment, the process of retrieving task information located in the task information base and synthesizing a corresponding task by using the retrieved task information may specifically include: the method comprises the steps of obtaining a task type sent by a user terminal, calling corresponding task information from a task information base according to the obtained task type, synthesizing a corresponding task by using the task information, obtaining a corresponding virtual task, obtaining an operation result containing corresponding task operation information by operating the virtual task, and completing an experience base by using the operation result.
In another specific embodiment, the process of retrieving the task information located in the task information base and synthesizing the corresponding task by using the retrieved task information may specifically include: and calling out multiple groups of task information from the task information base by using an exhaustion method, and synthesizing corresponding tasks by using each group of task information to obtain multiple groups of virtual tasks. That is, in the embodiment of the present invention, after task information such as various parameter types and parameter values in the task information base is arranged and combined differently, a plurality of groups of task information are obtained correspondingly, wherein a corresponding virtual task can be synthesized by using each group of task information. In this embodiment, a more comprehensive virtual task can be synthesized through the third embodiment, which is beneficial to improving the universality of the experience base.
Corresponding to the previous embodiment, the embodiment of the present invention further discloses a virtual task composition system under a NUMA architecture, as shown in fig. 2, the system includes:
an information base creating module 101, configured to create a task information base;
the task information calling module 102 is used for calling the task information in the task information base;
the virtual task synthesis module 103 is configured to synthesize a corresponding task by using the task information called by the task information calling module 102 to obtain a virtual task, so as to perform information improvement processing on the experience base by using an operation result of the virtual task;
the experience base is a database used for recording information related to task operation.
It will be appreciated that the task information base described above is a database that stores task information for the tasks themselves. The task information includes a task type and/or a parameter type and/or a specific parameter value, and the task type of the corresponding task can be determined according to the task type and the specific parameter value.
In the practical application process of the NUMA architecture, the types of parameters included in different types of tasks may be different, for example, for a type a process task, the types of parameters may specifically include a parameter a, a parameter B, a parameter c, and a parameter d, and for a type B process task, the types of parameters may include a parameter a, a parameter e, a parameter f, and a parameter g. That is, there may be differences in the types of parameters that different types of tasks include. In addition, for different tasks belonging to the same type, their respective parameter values of the same parameter often have different parameter values. For example, for task 1 and task 2, which are both of type a, each of them contains the same type of parameter, but the corresponding parameter values will be different.
In a specific embodiment, the information base creating module 101 may be specifically configured to record task information of each real task running under a NUMA architecture, so as to obtain a task information base.
In another specific embodiment, the information base creating module 101 may specifically include an information recording unit and an information classifying unit; wherein the content of the first and second substances,
the information recording unit is used for recording the task information of each real task running under the NUMA architecture to obtain an initial information base;
and the information classification unit is used for classifying the information in the initial information set by using a machine learning algorithm to obtain a task information base.
The information classification unit may be specifically configured to classify information in the initial information set by using a data mining algorithm or a neural network algorithm to obtain a task information base. And classifying the initial information set by a WEKA machine learning tool to obtain the task information base.
That is, after the task information of a large number of real tasks is recorded, the embodiment of the invention can classify the recorded task information by using a machine learning algorithm, so as to perform operation and maintenance processing on the task information base.
In a specific embodiment, the virtual task synthesis module 103 may be specifically configured to determine task operation information actually lacking in a current experience base, then determine a task type corresponding to the task operation information actually lacking in the current experience base, then retrieve corresponding task information from the task information base according to the determined task type, and then synthesize a corresponding task by using the task information, thereby obtaining a corresponding virtual task.
In another specific embodiment, the virtual task synthesis module 103 may be specifically configured to acquire a task type sent by a user terminal, call corresponding task information from the task information base according to the acquired task type, and synthesize a corresponding task by using the task information, so as to obtain a corresponding virtual task.
In another specific embodiment, the virtual task synthesis module 103 may specifically include an information retrieving unit and a task synthesis unit; wherein the content of the first and second substances,
the information calling unit is used for calling out a plurality of groups of task information from the task information base by using an exhaustion method;
and the task synthesis unit is used for synthesizing corresponding tasks by utilizing each group of task information to obtain a plurality of groups of virtual tasks.
In this embodiment, a more comprehensive virtual task can be synthesized through the third embodiment, which is beneficial to improving the universality of the experience base.
Therefore, the task information base is created firstly, the task information in the task information base is called subsequently, the purpose of synthesizing the virtual task can be achieved, the experience information in the experience base can be perfected by using the running result of the virtual task, and the experience information can be perfected without waiting for the arrival of the real task, so that the collection efficiency of the experience information can be effectively improved.
Finally, it should also be noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The embodiments are described in a progressive manner in the specification, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The principles and embodiments of the present invention are explained herein using specific examples, which are presented only to assist in understanding the method and its core concepts. It should be noted that, for those skilled in the art, it is possible to make various improvements and modifications to the present invention without departing from the principle of the present invention, and those improvements and modifications also fall within the scope of the claims of the present invention.
Claims (8)
1. A virtual task synthesis method under a NUMA architecture is characterized by comprising the following steps:
creating a task information base by recording task information of each real task running under the NUMA architecture;
calling task information in the task information base, synthesizing a corresponding task by using the called task information to obtain a virtual task, and performing information perfecting processing on an experience base by using an operation result of the virtual task;
the experience base is a database used for recording information related to task operation.
2. The method for synthesizing virtual tasks under a NUMA architecture according to claim 1 wherein said process of creating a task information base comprises:
recording task information of each real task running under the NUMA architecture to obtain an initial information base;
and classifying the information in the initial information set by using a machine learning algorithm to obtain the task information base.
3. The method of claim 2, wherein the machine learning algorithm is a data mining algorithm or a neural network algorithm.
4. The method according to any one of claims 1 to 3, wherein the process of retrieving the task information located in the task information base and synthesizing the corresponding task using the retrieved task information includes:
calling out a plurality of groups of task information from the task information base by using an exhaustion method;
and synthesizing corresponding tasks by utilizing each group of task information to obtain a plurality of groups of virtual tasks.
5. A virtual task composition system under a NUMA architecture, comprising:
the information base creating module is used for creating a task information base in a mode of recording task information of each real task running under the NUMA architecture;
the task information calling module is used for calling the task information in the task information base;
the virtual task synthesis module is used for synthesizing a corresponding task by using the task information called out by the task information calling module to obtain a virtual task so as to complete information of the experience base by using the running result of the virtual task;
the experience base is a database used for recording information related to task operation.
6. The NUMA architectural virtual task composition system of claim 5, wherein the information base creation module comprises:
the information recording unit is used for recording the task information of each real task running under the NUMA architecture to obtain an initial information base;
and the information classification unit is used for classifying the information in the initial information set by using a machine learning algorithm to obtain the task information base.
7. The virtual task synthesis system under a NUMA architecture according to claim 6, wherein the information classification unit is specifically configured to classify information in the initial information set by using a data mining algorithm or a neural network algorithm to obtain the task information base.
8. A virtual task composition system under a NUMA architecture as claimed in any of claims 5 to 7, wherein the virtual task composition module comprises:
the information calling unit is used for calling out a plurality of groups of task information from the task information base by using an exhaustion method;
and the task synthesis unit is used for synthesizing corresponding tasks by utilizing each group of task information to obtain a plurality of groups of virtual tasks.
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