WO2019114506A1

WO2019114506A1 - Compilation optimization method and device, storage medium, intelligent terminal and server

Info

Publication number: WO2019114506A1
Application number: PCT/CN2018/116586
Authority: WO
Inventors: 陈岩; 刘耀勇
Original assignee: Oppo广东移动通信有限公司
Priority date: 2017-12-14
Filing date: 2018-11-21
Publication date: 2019-06-20
Also published as: CN109960507B; CN109960507A

Abstract

A compilation optimization method and device, a storage medium, an intelligent terminal and a server. The method comprises: detecting a target application program which has not been run and which is present in an intelligent terminal, the target application program comprising at least one function; acquiring from a preset server first statistical values corresponding to each function of the target application program, the first statistical values comprising statistical values calculated according to a first frequency corresponding to each function, and the first frequency comprising the frequency of each function executed by a preset user group in the process of using the target application program within a first preset statistical period; determining at least one first hotspot function in the target application program according to at least one first statistical value corresponding one-to-one with at least one function; and compiling each first hotspot function into a local machine code by means of a virtual machine running a pre-compilation mode.

Description

Compilation optimization method, device, storage medium, intelligent terminal and server

The present application claims the priority of the Chinese Patent Application No. PCT Application No.

Technical field

The present disclosure relates to the field of computer technology, for example, to compiler optimization methods, apparatus, storage media, smart terminals, and servers.

Background technique

With the rapid development of smart terminal technology, smart devices such as smart phones, tablets and smart TVs have become indispensable electronic devices in people's lives.

An operating system is commonly installed in intelligent terminals, and Android (Android) operating system is one of the most common intelligent operating systems, and has been widely used. The intelligent terminal usually includes the underlying layer and the application layer, and is written in different languages, and needs to be compiled by the virtual machine to enable the application to run normally in the smart terminal. Taking the Android operating system as an example, the underlying layer (such as the Linux kernel layer) is generally written in the C/C++ language, while the application layer is generally written in the Java language, and the Java-based program needs to run on the virtual machine. A virtual machine (VM) is responsible for interpreting or compiling a file or code corresponding to a Java-developed application to an executable file or code to enable the application to run. However, in the process of interpretation or compilation, it usually takes more time and affects the running speed. Therefore, the virtual machine-based compilation scheme still needs improvement.

Summary of the invention

The present disclosure provides a compiler optimization method, apparatus, storage medium, and intelligent terminal, which can optimize a virtual machine based compilation scheme in an intelligent terminal.

The present disclosure provides a compiler optimization method applied to a smart terminal, including:

Detecting that there is an unoperated target application in the smart terminal; wherein the target application includes at least one function;

And acquiring, by the preset server, a first statistic value corresponding to each function in the target application, where the first statistic value includes a statistic value calculated according to the first frequency corresponding to each function, the first The frequency includes a frequency at which each function described by the preset user group is executed during use of the target application during the first preset statistical period;

Determining at least one first hotspot function in the target application according to the at least one first statistical value corresponding to the at least one function;

Each first hotspot function is compiled into native machine code by pre-compilation of the virtual machine.

The present disclosure provides a compilation optimization method applied to a server, including:

Collecting, in a first preset statistical period, a first frequency that each function in the application is executed by the preset user group during use of the application, and calculating the each frequency according to the first frequency that each function is executed The first statistical value corresponding to the function;

Receiving, by the smart terminal, an acquisition request for acquiring at least one first statistic value corresponding to the at least one function in the application, wherein the smart terminal sends the server to the server when detecting that the application is not running The obtaining request;

Sending, to the smart terminal, at least one first statistic value corresponding to the at least one function in the application, the at least one first statistic value is used to indicate that the smart terminal is configured according to the at least one A statistic determines at least one first hotspot function in the application and compiles each first hotspot function into local machine code by way of pre-compilation of the virtual machine.

The present disclosure provides a compiler optimization device, which is configured in a smart terminal, and includes:

Running a monitoring module, configured to detect whether there is an unoperated target application in the smart terminal; wherein the target application includes at least one function;

The statistic value obtaining module is configured to: when detecting that the target application that is not running exists in the smart terminal, obtain a first statistic value corresponding to the function in the target application from the preset server, where the first statistic value includes And calculating, according to the first frequency corresponding to each function, the first frequency includes: in the first preset statistical period, the preset user group is executed during the use of the target application. Frequency

The hotspot function determining module is configured to determine at least one first hotspot function in the target application according to the at least one first statistic value corresponding to the at least one function;

Compile the module, set to compile each first hotspot function to native machine code by compiling the virtual machine before it runs.

The present disclosure provides a compiler optimization apparatus configured in a server, including:

The statistic value calculation module is configured to collect, during the first preset statistic period, a first frequency that the preset user group is executed during the use of the application, and each function is executed according to each function The first frequency calculates a first statistical value corresponding to each function; wherein the application includes at least one function;

Obtaining a request receiving module, configured to receive, by the smart terminal, an acquisition request for acquiring at least one first statistic value corresponding to the at least one function in the application, wherein the smart terminal detects that the application is not running out of time Sending the acquisition request to the server;

And a statistical value sending module, configured to: when the smart terminal acquires an acquisition request of the at least one first statistic value corresponding to the at least one function in the application, Transmitting at least one first statistic value corresponding to a function to the smart terminal, the at least one first statistic value is used to indicate that the smart terminal determines the application according to the at least one first statistic value At least one first hotspot function, and compiling each first hotspot function into local machine code by way of pre-compilation of the virtual machine.

The present disclosure provides a computer readable storage medium having stored thereon a computer program that, when executed by a processor, implements a compilation optimization method as described in embodiments of the present disclosure.

The present disclosure provides a smart terminal including a memory, a processor, and a computer program stored on the memory and executable by the processor, the processor executing the computer program to implement compilation optimization as described in the embodiments of the present disclosure method.

The compiling optimization scheme provided by the present disclosure enables the smart terminal to obtain the big data statistics for multiple functions in the application from the preset server before running the application for the first time, and determine the hotspot function that needs to be pre-compiled according to the statistical situation, and then Compile the hotspot function into local machine code by compiling the virtual machine before running, which improves the efficiency of the application for the first time.

The compiler optimization solution provided by the disclosure enables the server to calculate the frequency of execution of different functions of the preset user group in the process of using the application by using the big data method, thereby calculating the first statistic value, and sending the first statistic value to the smart The terminal instructs the smart terminal to pre-compile the hotspot function before running the application for the first time, improving the efficiency of the application for the first time.

DRAWINGS

1 is a schematic flow chart of a compiling optimization method provided by an embodiment;

2 is a schematic flowchart of a compiling optimization method provided by another embodiment;

3 is a schematic flowchart of a compiling optimization method provided by another embodiment;

4 is a schematic flowchart of a compiling optimization method provided by another embodiment;

FIG. 5 is a signaling diagram of an intelligent terminal interacting with a server according to an embodiment;

6 is a structural block diagram of a compiler optimization apparatus according to an embodiment;

FIG. 7 is a structural block diagram of a compiler optimization apparatus according to another embodiment;

FIG. 8 is a schematic structural diagram of an intelligent terminal according to an embodiment; FIG.

FIG. 9 is a schematic structural diagram of an intelligent terminal according to another embodiment;

FIG. 10 is a schematic structural diagram of a server according to an embodiment.

Detailed ways

The technical solutions of the present disclosure will be described below with reference to the accompanying drawings and specific embodiments. The specific embodiments described herein are merely illustrative of the disclosure and are not intended to be limiting. In addition, for the convenience of description, only some but not all of the structures related to the present disclosure are shown in the drawings.

In order to better understand the technical solution of the present disclosure, the Android operating system is taken as an example to introduce a simple introduction of two virtual machine Dalvik virtual machines (DVM) and ART (Android Runtime) virtual machines. .

Dalvik is a virtual machine designed and developed by Google for the Android platform. It is an important part of the Android platform and supports the running of Java applications in the dex (Dalvik Executable) format. The dex format is a compression format specially designed for Dalvik. In Java programs, Java classes are compiled into one or more class files, and then the class files are converted to dex files by the DEX tool, and the Dalvik virtual machine reads commands from them. And data. The Dalvik virtual machine is an interpretation execution mechanism that is responsible for interpreting the dex file as a machine code. The Java bytecode is parsed and run in the dex file at each run time, but the disadvantage is poor performance and slow operation. As the Android version continues to upgrade, the Dalvik virtual machine has a Just In Time Compiler (JIT). When the application runs, whenever a new class is encountered, the JIT compiler will compile the class. The compiled code will be optimized to the native code so that the next time it reaches the same logic (also called a method or function), the speed will be faster, but using JIT is not necessarily the case. Speed up execution, if most of the code is executed a small number of times, then the compilation time is not necessarily less than the time to execute dex, and the dex bytecode translation cost machine code is the operation of the application (Application, APP) Every time the application is re-run, the translation work needs to be redone, that is, every time the application is reopened, JIT compilation is required.

The ART virtual machine adopts Ahead of Time (AOT), which is also called pre-compilation or pre-compilation. It is pre-compiled for all applications (after installing an application for the first time). Execution file (also known as local machine code or local machine code), after the application starts, no additional translation work is required, and the local machine code is used directly to speed up the operation, but the disadvantage is that the first boot time is too long. It may take up to ten minutes, resulting in a bad user experience and a certain amount of storage space.

In the present disclosure, for the Android operating system, a combination of the ART virtual machine, the JIT, and the explanation execution can be used to optimize the compilation scheme of the virtual machine, and the operation efficiency of the application in the smart terminal can be improved. For example, cancel the process of compiling all applications when booting for the first time, and change to a function based on the usage habit of the intelligent terminal user for high-frequency access or execution (also called execution hotspot or hotspot function). The faster the effect is, however, before the sufficient amount of local statistical data is obtained, the application in the intelligent terminal still needs to run in an interpreted and executed manner, and the speed is slow. Therefore, based on the insufficient statistical data, a big data based method is proposed. The statistical result determines the scheme of the hotspot function, which will be described below in conjunction with the specific embodiments.

FIG. 1 is a schematic flowchart of a compiling optimization method provided by an embodiment. The method may be implemented by a compiler optimization apparatus. The apparatus may be implemented by software and/or hardware, and may be integrated into an intelligent terminal. As shown in FIG. 1, the method includes the following steps.

Step 1010: It is detected that there is an target application that is not running in the smart terminal.

In an embodiment, the smart terminal in this embodiment may include a smart device installed with an operating system, such as a smart phone, a tablet computer, a media player, and a smart home appliance. The type of the operating system is not limited in this embodiment, and may be an Android operating system, an iOS operating system, and a Windows (Windows) operating system. For convenience of description, the Android operating system will be taken as an example for description.

In this embodiment, the target application that has not been run may include an application that has been installed in the smart terminal but has not been run, and may also include an application during the installation process. The detecting the presence or absence of the target application that has not been run in the smart terminal may include: when the smart terminal is powered on, detecting that the application installation file download event is triggered or downloaded after detecting the installation of the application, and When it is detected that the user triggers the program detection event not running (such as the user starts the application to run the acceleration switch for the first time).

Step 1020: Acquire a first statistic value corresponding to each function in the target application from a preset server.

In this embodiment, the first statistic value includes a statistic value calculated according to the first frequency corresponding to each function, where the first frequency includes the preset user group in the first preset statistic period, The frequency at which each function described in the target application process is executed.

In an embodiment, the step of calculating the first statistical value and collecting relevant data for calculating the first statistical value may be performed by a preset server. The first preset statistical period may be determined according to the characteristics of the smart terminal or the user's use requirements, etc., and the disclosure is not limited. For example, the smart terminal is a smart phone, and most users use it for a long time almost every day. The first preset statistical period can be set shorter, such as one week.

In an embodiment, the preset user group may include all smart terminal users who have used the target application, or may select some users as a sample to form a preset user group, such as 20,000 users who agree to the data collection terms in advance. The preset user group may also be a group having the same user attribute, such as a female user group, a user group of a specified age group (such as 20 to 30 years old), and the like. The disclosure does not limit the collection objects of the user group, and can be selected according to actual needs.

In an embodiment, the types of smart terminals actually used by the preset user group when using the target application may be the same or different, and may be determined by the difference of the same application in different smart terminals. Take WeChat as an example. If the functions included in the mobile version of WeChat and the tablet version of WeChat are quite different, for smartphones, only the usage data of the default user group when using WeChat through the smartphone can be collected; if the mobile version is WeChat and tablet The functions included in WeChat are basically the same or identical. For smartphones, the usage data of the default user group when using WeChat through smartphones and tablets can also be collected.

In an application, usually one or more classes are included. Each class includes one or more functions (also called methods). Different functions generally correspond to different functions. During the running of the application, The frequency of use of different functions is different depending on the user's usage habits. Taking WeChat as an example, the chat function and the circle of friends are more common. The corresponding function is executed (or called) at a higher frequency and is more important, and the collection function or card package function may not be used frequently for many users. Then the corresponding function will be executed at a lower frequency and less important. In the present disclosure, the frequency at which multiple functions are executed by the preset user group in the process of using an application in the first preset statistical period is collected, and then the statistical value corresponding to each function is calculated, and the statistical value may reflect The frequency and importance of the corresponding function is used to facilitate the intelligent terminal to determine the hotspot function for performing precompilation related operations. In this embodiment, the first statistic value includes a statistic value calculated according to the first frequency corresponding to each function, and the method for calculating the first statistic value is not limited in the disclosure, for example, multiple first frequencies may be taken. The highest value in the middle may be an average value or a weighted average of a plurality of first frequencies, and the like.

Step 1030: Determine at least one first hotspot function in the target application according to the at least one first statistic value corresponding to the at least one function.

In this embodiment, since the function is compiled into the local machine code by the pre-compilation mode of the virtual machine, the local machine code occupies a certain storage space in the intelligent terminal, so it is necessary to selectively determine the hotspot function for compiling. In this embodiment, a corresponding function with a higher first statistical value may be selected as the first hotspot function. For example, there are a total of A functions in the target application, sorted from high to low according to the first statistical value, and the first bA functions can be selected as the first hotspot function, b is a coefficient smaller than 1, and bA can be rounded up or down. The value of b can be freely set, or can be determined according to the current remaining storage space of the smart terminal; for example, the front B (B is less than or equal to A) functions can be selected as the first hotspot function, and B can be a fixed value, or The value determined dynamically according to the current remaining storage space of the smart terminal; for example, a statistical threshold may be preset, which may be recorded as a first preset statistical threshold, and the corresponding first statistical value reaches a function of the first preset statistical threshold. Determined as the first hotspot function in the target application, the advantage of this setting is to ensure that the more frequently used functions are precompiled. If there are only a few functions in the application that satisfy the requirements, the frequency of the application itself is used. It is not high, so you will not precompile functions that do not meet the requirements, in order to save storage space for hotspot functions of other applications.

Step 1040: Compile each first hotspot function into local machine code by means of pre-compilation of the virtual machine.

In this embodiment, after the first hotspot function is determined, the first hotspot function is compiled into the local machine code by the pre-compilation of the virtual machine, and when the target application is first started, when the first hotspot function needs to be executed, Direct use of the corresponding local machine code, without the need to interpret execution or compile in a timely manner, can effectively improve the efficiency of the first application of the target application. The compiling process of the first hotspot function may be determined by a virtual machine used in the operating system of the smart terminal, which is not limited in this embodiment. It can be compiled after the target application is installed, before the first run, or during the installation of the target application. In an embodiment, for other functions in the target application that are not determined as the first hotspot function, if the other functions need to be executed during the running of the target application, the execution may be interpreted in a manner that does not affect the target. The application is running.

The compiler optimization method provided in this embodiment detects that there is an unexecuted target application in the smart terminal, and for each function in the target application, acquires a first statistic value corresponding to each function from the preset server. The first statistic value includes a value calculated according to a frequency at which each function of the preset user group is executed in the process of using the target application in the first preset statistical period, and then determining the target application according to the first statistic value. The first hotspot function in the program compiles the determined first hotspot function into local machine code by means of pre-compilation of the virtual machine. By adopting the above technical solution, the smart terminal can obtain the big data statistics for multiple functions in the application from the preset server before running the application for the first time, and determine the hot function that needs to be pre-compiled according to the statistical situation, and then pass the virtual The way to compile before running the machine compiles the hotspot function into local machine code, which improves the efficiency of the application for the first time. For the Android operating system, the present embodiment innovatively combines the ART virtual machine, the JIT, and the interpretation execution, and assists with the statistical results of the big data, thereby effectively improving the running speed and efficiency of the application in the intelligent terminal, and accumulating to the foot. The amount of native statistics does not need to be interpreted to perform all functions in the target application.

In some embodiments, the first statistic value corresponding to each function is determined by collecting, in the first preset statistic period, each user in the preset user group as described in the process of using the target application. The first frequency of the function is performed; the usage intensity information corresponding to each user in the preset user group is acquired in the first preset statistical period; and the determining is performed according to the usage intensity information of each user a first weighting coefficient corresponding to each user, and performing a weighted average operation according to the first weighting coefficient and the first frequency, to obtain a first statistical value corresponding to each function. The advantage of this setup is that it takes into account the differences between different individuals in the pre-set user community and the impact on the statistical results. In an embodiment, the usage strength information may include the duration of use of the user, the duration of use of the smart terminal, or the duration of use of the target application, and the usage strength information may also include the total number of applications installed in the smart terminal. Etc., the disclosure is not specifically limited. The usage duration may be the usage duration in the first preset statistical period, such as the cumulative usage duration in one week, or the usage duration in the first preset statistical period, such as the unit time is one day, and the usage duration is one day. The cumulative usage time within.

In some embodiments, after the first hotspot function is compiled into the local machine code by the pre-compilation by the virtual machine, the method further includes: recording, by the user of the smart terminal, in the second preset statistical period. a second frequency in which each function in the target application is executed in the target application process, performing a weighted average operation according to a preset weight coefficient, the first statistical value, and the second frequency, to obtain each of the a second statistic corresponding to the function, where the preset weight coefficient includes a first preset weight coefficient and a second preset weight coefficient, the first statistic value corresponding to the first preset weight coefficient, and the second frequency corresponding to the first Two preset weight coefficients; determining at least one second hotspot function in the target application according to at least one second statistical value corresponding to the at least one function; and applying the target according to the at least one second hotspot function The compilation strategy of the program is updated. The advantage of this setting is that the hotspot function can be changed according to the user's personal usage habits of the smart terminal, so that the pre-compiled hotspot function is more suitable for the user's personal habits and preferences. The second preset statistical period may be the same as the first preset statistical period, or may be different, and is not limited in this embodiment. For example, the first statistic value is P, the second frequency is q, the first preset weight coefficient is k1, the second preset weight coefficient is k2, and the second statistic value Q, then Q=(k1*P+k2q) The values of /2, k1 and k2 are not limited in this embodiment. In general, k2 can be greater than k1 because the second frequency is more responsive to the user's personal habits, for example, k2 is 0.7 and k1 is 0.3. The manner in which the second hotspot function is determined may be similar to the manner in which the first hotspot function is determined. For example, determining whether the second statistical value corresponding to each function reaches a second preset statistical threshold, and if so, determining each function as a second hotspot function in the target application. The second preset statistical threshold may be the same as the first preset statistical threshold, or may be different, and is not limited in this embodiment.

In an embodiment, the updating the compilation policy of the target application according to the at least one second hotspot function comprises: determining that the second hotspot function in the at least one second hotspot function is not compiled Compiling the uncompiled second hotspot function into local machine code by way of pre-compilation of the virtual machine; and/or determining that the first hotspot function that has been compiled in the at least one first hotspot function does not belong to at least one The second hotspot function deletes the local machine code corresponding to the first hotspot function that has been compiled. The advantage of the former is that it allows the function that can be compiled before running to be more suitable for the user's personal usage habits; the latter has the advantage that the local machine code of the first hotspot function that is used by the user who does not fit the smart terminal can be cleared. The storage space is released, so that the storage resources of the smart terminal can be more rationally utilized; both of the advantages include that the second hotspot function that fits the user's personal usage habits can be compiled into the local machine code, and can be cleared. Users who do not fit the smart terminal use the local machine code of the habit, and conform to the user's use requirements as a whole, and optimize the overall performance of the smart terminal.

In some embodiments, before acquiring, by the preset server, the first statistic value corresponding to each function in the target application, the method further includes: acquiring target attribute information of the user of the smart terminal. The obtaining, by the preset server, the first statistic value corresponding to each function, includes: acquiring, by using a preset server, a first statistic value corresponding to each function that matches the target attribute information. The advantage of this setting is that the first statistical value based on big data can be made more suitable for the current user's attributes, so that the obtained first statistical value is more accurate. The attribute information may include, for example, a gender, an age group, a career, an area, and the like. When the first server collects the first statistical value related data, the preset server may collect and calculate the corresponding first statistical value according to the attribute information classification, when the smart When the terminal needs to obtain the first statistic value from the preset server, the preset server may find the corresponding first statistic value according to the target attribute information of the user of the smart terminal sent by the smart terminal, and feed back to the smart terminal, so that the smart terminal is more accurate. Determine the hotspot function.

In some embodiments, the target application may include multiple, then the at least one first hotspot function in the target application may be determined according to the at least one first statistical value corresponding to the at least one function. The method includes: determining, according to a comprehensive ranking of the first statistical values corresponding to all the functions in the plurality of applications, the first hotspot function corresponding to each application. The advantage of this setting is that, due to the limited storage space in the smart terminal, in order to reduce the occupation of the storage space by the local machine code, the big data statistics corresponding to multiple functions in multiple applications can be comprehensively considered to determine the need to run before running. Compiled hotspot function. In an embodiment, when detecting that the smart terminal is powered on for the first time, the target application includes all applications loaded in the smart terminal; and the at least one first one corresponding to the at least one function Determining, by a statistic value, the at least one first hotspot function in the target application, comprising: determining, according to a comprehensive ranking of the first statistic values corresponding to all the functions in all the applications, at least one first hotspot corresponding to each application function. In this way, when the smart terminal is first booted up, it can selectively pre-compile the hotspot function based on big data. Compared with the traditional ART virtual machine working mode, it can avoid the disadvantages of centralized boot compiling, effectively reduce the boot waiting time, and can do In the case of insufficient local statistical data, using the big data to accurately filter out the hotspot function can take into account the usage scenarios of most users, without having to perform all the explanations and executions before accumulating enough local statistical data. The function, which uses the statistical results of big data to avoid the vacuum period of individual users, allows users to enjoy smooth application execution and improve the running speed of the application.

FIG. 2 is a schematic flowchart of a compiling optimization method provided by another embodiment, where the method includes the following steps.

Step 2010: It is detected that there is an target application that has not been run in the smart terminal.

In this embodiment, the target application includes at least one function.

Step 2020: Obtain target attribute information of a user of the smart terminal.

Step 2030: Acquire, from a preset server, a first statistic value corresponding to each function in the target application that matches the target attribute information.

Step 2040: Determine at least one first hotspot function in the target application according to the at least one first statistic value corresponding to the at least one function.

Step 2050: Compile each first hotspot function into local machine code by means of pre-compilation of the virtual machine.

Step 2060: Record, in a second preset statistical period, a second frequency that is executed by each user in the target application during use of the target application by the user of the smart terminal, according to a preset weight coefficient, first The statistic value and the second frequency are subjected to a weighted averaging operation to obtain a second statistic value corresponding to each function.

Step 2070: Determine at least one second hotspot function in the target application according to the at least one second statistic value corresponding to the at least one function.

Step 2080: When it is determined that the first hotspot function that has been compiled in the at least one first hotspot function does not belong to the at least one second hotspot function, delete the local machine code corresponding to the first hotspot function that has been compiled, and determine at least one When the second hotspot function in the second hotspot function is not compiled, the uncompiled second hotspot function is compiled into the local machine code by the pre-compilation of the virtual machine.

In the compiler optimization method provided by the embodiment, the smart terminal can obtain the big data statistics for multiple functions in the application from the preset server before running the application for the first time, and determine the hot function that needs to be pre-compiled according to the statistical situation. In turn, the hotspot function is compiled into the local machine code by means of pre-compilation of the virtual machine to improve the first-time running efficiency of the application. Subsequently, the usage habit data of the local user is continuously collected, combined with the statistical data of the big data and the usage data of the local user. The hotspot function performs self-feedback adjustment, so that the second hotspot function that fits the user's personal usage habits is compiled into the local machine code, and the local machine code that does not fit the user's usage habit of the smart terminal is cleared, and the user's use requirement is integrated as a whole. Rationally utilize the storage space of the intelligent terminal to optimize the overall performance of the intelligent terminal.

FIG. 3 is a schematic flowchart diagram of a compiling optimization method provided by another embodiment. The method may be implemented by a compiler optimization apparatus, where the apparatus may be implemented by software and/or hardware, and may be integrated into a server. As shown in FIG. 3, the method includes the following steps.

Step 3010: Collect, in a first preset statistical period, a first frequency that each function in the application is executed by the preset user group during use of the application, and calculate each of the first frequency according to the first frequency. The first statistical value corresponding to the function.

In this embodiment, the application includes at least one function.

In an embodiment, the server may perform statistics on an application published in the application store or in the market. When the preset user group uses the smart terminal, the smart terminal uploads data such as the time and frequency of the user's use of the application to the server. The server performs statistics based on the collected data, and obtains a first statistical value corresponding to each function in each application.

An implementation for determining the first statistical value is given below as a schematic illustration. A preset data structure is established in the server. For example, the preset data structure may be in the form of a package name -Key-Value. The package name refers to the package name corresponding to the application, which is the identity of the application, and has global uniqueness; the value of the Key can be the class name + execution function name, and the class name and execution function name are generally characters. In the form of a string, in order to facilitate statistics and calculations, the string can be converted. For example, the hash name (Hash) is used to map the class name + execution function name to a fixed-length value, for example, using a message digest algorithm (Message Digest) Algorithm, MD) The fifth version of MD5 encoding converts the class name + execution function name into a form of 8 bytes (Byte); Value can be the frequency information actually used by the function. After receiving statistics from a large number of users (when the number of samples of the preset user group is sufficient), the cloud server assigns a weight to each user based on the user's mobile phone usage intensity, and performs a weighted average calculation to obtain a global application. Weight information for each function in each class (ie, the first statistic in this embodiment).

Step 3020: Receive an acquisition request of the smart terminal to acquire at least one first statistic value corresponding to at least one function in the application.

In this embodiment, the smart terminal sends the acquisition request to the server when detecting that the application is not running.

Step 3030: Send at least one first statistic value corresponding to the at least one function in the application to the smart terminal.

In this embodiment, the at least one first statistic value is used to indicate that the smart terminal determines at least one first hotspot function in the application according to the at least one first statistic value, and compiles before running through a virtual machine. The manner of compiling each of the at least one first hotspot function into local machine code.

In the compiler optimization method provided in this embodiment, the server collects, for each function in the application, the first frequency that each function of the preset user group is executed during the use of the application program in the first preset statistical period. And calculating, according to the first frequency, the first statistic value corresponding to each function, and when receiving the acquisition request of the at least one first statistic value corresponding to the at least one function in the application by the smart terminal, at least one The first statistic value is sent to the smart terminal to instruct the smart terminal to determine, according to the at least one first statistic value, a hotspot function that needs to be compiled by means of pre-compilation of the virtual machine. By adopting the above technical solution, the server can calculate the frequency of execution of different functions in the process of using the application by the preset user group by using the big data method, and then calculate the first statistic value, and send the first statistic value to the intelligent terminal to guide The smart terminal pre-compiles the hotspot function before running the application for the first time, improving the efficiency of the application for the first time.

FIG. 4 is a schematic flowchart of a compiling optimization method provided by another embodiment, which is described in a scenario in which an intelligent terminal interacts with a preset server, and the method includes the following steps.

Step 4010: The server collects, in a first preset statistical period, a first frequency that each function is executed by the preset user group in using different applications, and calculates a corresponding function according to the first frequency that each function is executed. The first statistic.

Step 4020: When detecting that there is a target application that has not been run, the smart terminal sends an acquisition request for acquiring a first statistic value corresponding to each function in the target application to the server.

Step 4030: The server searches for a first statistic value corresponding to each function in the target application according to the obtaining request, and sends the first statistic value to the smart terminal.

Step 4040: The smart terminal determines a first hotspot function in the target application according to the first statistic value, and compiles the first hotspot function into a local machine code by using a pre-compilation manner of the virtual machine.

Step 4050: The smart terminal records, in the second preset statistical period, a second frequency that each user of the smart terminal performs during the process of using the target application.

Step 4060: The smart terminal determines a second hotspot function in the target application according to the second statistical value, and updates the compilation policy of the target application according to the second hotspot function.

FIG. 5 is a signaling diagram of an intelligent terminal interacting with a server according to an embodiment. The foregoing embodiment may be understood by using the signaling diagram.

In the compiling optimization method provided by the embodiment, the server performs the historical habit data of the application group by using the preset user group, and forms a big data statistical result to be fed back to the smart terminal, and the smart terminal determines the hotspot function in the non-running application based on the big data statistical result. , pre-compilation of hotspot functions, effectively improve the speed of the application.

6 is a structural block diagram of a compiler optimization apparatus provided by an embodiment. The apparatus may be implemented by software and/or hardware, and is generally integrated in an intelligent terminal, and may optimize a virtual machine based compilation in a smart terminal by executing a compiler optimization method. Program. As shown in FIG. 6, the device includes: an operation monitoring module 601, configured to detect whether there is an unexecuted target application in the smart terminal, where the target application includes at least one function; the first statistic value obtaining module 602, setting Obtaining, by the preset server, a first statistic value corresponding to each function in the target application, when the target application that is not running is detected in the smart terminal, where the first statistic value includes a statistic value calculated by the first frequency corresponding to the function, where the first frequency includes a frequency at which each function of the preset user group is executed during the use of the target application program in the first preset statistical period; a hotspot function determining module 603, configured to determine at least one first hotspot function in the target application according to the at least one first statistical value corresponding to the at least one function; the compiling module 604 is configured to pass the virtual machine The pre-run compilation method compiles each first hotspot function into native machine code.

The compiler optimization apparatus provided in this embodiment detects that there is an unexecuted target application in the smart terminal, and obtains, for each function in the target application, a first statistic value corresponding to each function from the preset server, The first statistic value includes a value calculated according to a frequency at which each function of the preset user group is executed during the use of the target application program in the first preset statistical period, and then the target application is determined according to the first statistic value. The first hotspot function in the first hotspot function compiles the determined first hotspot function into local machine code by means of pre-compilation of the virtual machine. By adopting the above technical solution, the smart terminal can obtain the big data statistics for multiple functions in the application from the preset server before running the application for the first time, and determine the hot function that needs to be pre-compiled according to the statistical situation, and then pass the virtual The way to compile before running the machine compiles the hotspot function into local machine code, which improves the efficiency of the application for the first time.

In an embodiment, the first statistical value corresponding to each function is determined by:

Collecting, in a first preset statistical period, a first frequency that each function in the preset user group is executed during use of the target application, where the preset user group includes multiple users; Obtaining the usage intensity information corresponding to each user in the preset user group in the first preset statistical period; determining the first weight corresponding to each user according to the usage intensity information of each user a coefficient, and performing a weighted averaging operation according to the plurality of first weight coefficients corresponding to the plurality of users and the plurality of first frequencies corresponding to the plurality of users, to obtain a first statistic corresponding to each function value.

In an embodiment, the apparatus further includes a second statistic value obtaining module configured to: after the first hotspot function is compiled into the local machine code in the manner of compiling by the virtual machine before running, in the second preset statistic Recording, in a period, a second frequency at which each function in the target application is executed by a user of the smart terminal during use of the target application, according to a preset weight coefficient, the first statistical value, and the Performing a weighted averaging operation on the second frequency to obtain a second statistic value corresponding to each function, where the preset weight coefficient includes a first preset weight coefficient and a second preset weight coefficient, where the first statistic value corresponds to a first preset weight coefficient, the second frequency corresponding to the second preset weight coefficient; a second hotspot function determining module, configured to determine, according to the at least one second statistic value corresponding to the at least one function At least one second hotspot function in the target application; a compiling policy update module configured to apply to the target according to the at least one second hotspot function The compilation strategy of the sequence is updated.

In an embodiment, the compiling strategy update module is configured to: when determining that the second hotspot function in the at least one second hotspot function is not compiled, the method of compiling before running through the virtual machine is not compiled. The second hotspot function is compiled into local machine code; and/or, when it is determined that the first hotspot function that has been compiled in the at least one first hotspot function does not belong to the at least one second hotspot function, deleting the already The local machine code corresponding to the first hotspot function being compiled.

In an embodiment, the first hotspot function determining module 603 is configured to determine at least one first hotspot function in the target application according to at least one first statistic value corresponding to the at least one function. And determining whether the first statistic value corresponding to each function reaches a first preset statistic threshold, and if so, determining each function as a first hotspot function in the target application.

In an embodiment, the apparatus further includes an attribute information acquiring module, configured to acquire a user of the smart terminal before acquiring the first statistic value corresponding to each function in the target application from the preset server The first statistic value obtaining module 602 is configured to acquire, by using a preset server, a first statistic value corresponding to each function in the target application program: obtaining and locating from a preset server The first statistical value corresponding to each function in the target application that matches the target attribute information.

In an embodiment, when detecting that the smart terminal is powered on for the first time, the target application includes all applications loaded in the smart terminal; the first hotspot function determining module 603 is configured to be configured according to the following manner The at least one first statistic value corresponding to the at least one function determines at least one first hotspot function in the target application: determining, according to a comprehensive ranking of the first statistic values respectively corresponding to all functions in all applications At least one first hotspot function corresponding to each application.

FIG. 7 is a structural block diagram of a compiler optimization apparatus provided by another embodiment, which may be implemented by software and/or hardware, generally integrated in a server, and may optimize a virtual machine based compilation in a smart terminal by executing a compiler optimization method. Program. As shown in FIG. 7, the apparatus includes: a statistical value calculation module 701, configured to collect, during a first preset statistical period, a first user group in which the function is executed in the application process during use of the application program a frequency, calculating a first statistic value corresponding to each function according to the first frequency, the application program includes at least one function, and an acquisition request receiving module 702 is configured to receive the smart terminal to obtain the application program. An acquisition request of at least one first statistic value corresponding to the at least one function, wherein the smart terminal sends the acquisition request to the server when detecting that the application is not running; the statistic value sending module 703, configured to: when the smart terminal acquires an acquisition request of the at least one first statistic value corresponding to the at least one function in the application, the at least one function in the application Transmitting at least one first statistic value of the one-to-one correspondence to the smart terminal, where the at least one first statistic value is used to indicate that the smart terminal is configured according to the The at least one first statistical value determines at least one first hotspot function in the application, and each first hotspot function is compiled into local machine code by way of pre-compilation of the virtual machine.

In the compiler optimization apparatus provided by the embodiment, the server collects, for each function in the application, the first frequency that each function of the preset user group is executed during the use of the application program in a first preset statistical period, Calculating, according to the first frequency, the first statistic value corresponding to each function, and when receiving the acquisition request of the at least one first statistic value corresponding to the at least one function in the application, A first statistic value is sent to the smart terminal to instruct the smart terminal to determine, according to the first statistic value, a hotspot function that needs to be compiled by means of pre-compilation of the virtual machine. By adopting the above technical solution, the server can calculate the frequency of execution of different functions in the process of using the application by the preset user group by using the big data method, and then calculate the first statistic value, and send the first statistic value to the intelligent terminal to guide The smart terminal pre-compiles the hotspot function before running the application for the first time, improving the efficiency of the application for the first time.

The embodiment further provides a storage medium containing computer executable instructions for executing a compilation optimization method when executed by a computer processor, the method comprising: detecting that there is an unrun in the smart terminal a target application, wherein the target application includes at least one function; acquiring, from a preset server, a first statistic value corresponding to each function in the target application, the first statistic value including corresponding to each function The first frequency calculates a statistical value, where the first frequency includes a frequency at which each function of the preset user group is executed during use of the target application in the first preset statistical period; At least one first statistic value corresponding to a function one-to-one determines at least one first hotspot function in the target application; each first hotspot function is compiled into local machine code by way of pre-compilation of the virtual machine.

Storage medium - any type of storage device or storage device. The term "storage medium" is intended to include: a mounting medium such as a Compact Disc Read-Only Memory (CD-ROM), a floppy disk or a tape device; a computer system memory or a random access memory such as a dynamic random Dynamic Random Access Memory (DRAM), Double Data Rate Random Access Memory (DDRRAM), Static Random Access Memory (SRAM), and extended data output random storage Extended Data Output Random Access Memory (EDORAM), Rambus Random Access Memory (RAM), etc.; non-volatile memory such as flash memory, magnetic media (such as hard disk or optical storage) Register or other similar type of memory element, etc. The storage medium may also include other types of memory or a combination thereof. Additionally, the storage medium may be located in a first computer system in which the program is executed, or may be located in a different second computer system, the second computer system being coupled to the first computer system via a network, such as the Internet. The second computer system can provide program instructions to the first computer for execution. The term "storage medium" can include two or more storage media that can reside in different locations (eg, in different computer systems connected through a network). A storage medium may store program instructions (eg, program instructions are a computer program) executable by one or more processors.

The storage medium containing the computer executable instructions provided by the embodiment, the computer executable instructions are not limited to the compiler optimization operation as described above, and may also execute the compiler optimization method provided by any embodiment of the present disclosure corresponding to the smart terminal. Related operations in .

The embodiment further provides another storage medium containing computer executable instructions for executing a compilation optimization method when executed by a computer processor, the method comprising: collecting during a first predetermined statistical period Presetting a first frequency that each function in the application is executed during a process of using an application, and calculating, according to the first frequency, a first statistic corresponding to each function, the application Including at least one function; receiving, by the smart terminal, an acquisition request for acquiring at least one first statistic value corresponding to at least one function in the application, wherein the smart terminal detects that the application is not running, Sending the acquisition request to the server; sending at least one first statistic value corresponding to the at least one function in the application to the smart terminal, where the at least one first statistic value is used to indicate Determining, by the smart terminal, at least one first hotspot function in the application according to the at least one first statistic value Before running the virtual machine compiler of the first embodiment each function of the local hotspot compiled machine code.

The specific details of the computer storage medium of this embodiment are similar to those of the computer storage medium described above, and are not described herein again.

The embodiment provides an intelligent terminal, and the compiling and optimizing device of the corresponding smart terminal provided by the embodiment is integrated in the smart terminal. FIG. 8 is a schematic structural diagram of an intelligent terminal according to an embodiment. The smart terminal 800 can include a memory 801, a processor 802, and a computer program stored on the memory 801 and executable by the processor 802, the processor 802 executing the computer program to implement a corresponding implementation as described in an embodiment of the present disclosure. Compilation optimization method for intelligent terminals.

The smart terminal provided in this embodiment can obtain the big data statistics for multiple functions in the application from the preset server before running the application for the first time, and determine the hot function that needs to be pre-compiled according to the statistical situation, and then pass the virtual The way to compile before running the machine compiles the hotspot function into local machine code, which improves the efficiency of the application for the first time.

FIG. 9 is a schematic structural diagram of another smart terminal provided by another embodiment. The smart terminal 900 may include: a casing (not shown in FIG. 9), a memory 901, and a central processing unit (CPU) 902. (also referred to as a processor, hereinafter referred to as CPU), a circuit board (not shown in FIG. 9), and a power supply circuit (not shown in FIG. 9). The circuit board is disposed inside a space enclosed by the casing; the CPU 902 and the memory 901 are disposed on the circuit board; and the power circuit is configured to be at least one circuit or device of the smart terminal The memory 901 is configured to store executable program code; the CPU 902 runs a computer program corresponding to the executable program code by reading executable program code stored in the memory 901 to implement the following steps Detecting that there is an unexecuted target application in the smart terminal, wherein the target application includes at least one function; acquiring, from the preset server, a first statistic value corresponding to each function in the target application, the a statistic value includes a statistic value calculated according to the first frequency corresponding to each function, where the first frequency includes a preset user group in the first preset statistic period, in the process of using the target application Frequency at which a function is executed; determining the target application based on at least one first statistic value corresponding to the at least one function At least one first hot function; by way of the front of the virtual machine running the compiler to compile the function of each of the first hot native machine code.

The smart terminal further includes: a peripheral interface 903, a radio frequency (RF) circuit 905, an audio circuit 906, a speaker 911, a power management chip 908, an input/output (I/O) subsystem 909, and other inputs/controls. Device 910, touch screen 912, other input/control devices 910, and external port 904, these components communicate over one or more communication buses or signal lines 907.

The smart terminal 900 illustrated in FIG. 9 is merely one example of a smart terminal, and the smart terminal 900 may have more or fewer components than those illustrated in FIG. 9, and two or more components may be combined, Or it can have different component configurations. The various components shown in Figure 9 can be implemented in hardware, software, or a combination of hardware and software, including one or more signal processing and/or application specific integrated circuits.

The smart terminal for optimizing virtual machine compilation provided by the embodiment is described below. The smart terminal takes a mobile phone as an example.

The memory 901 can be accessed by the CPU 902 and the peripheral interface 903, etc., and the memory 901 can include a high speed random access memory, and can also include a nonvolatile memory such as one or more magnetic disk storage devices, flash memory devices. Or other volatile solid-state storage devices.

A peripheral interface 903 that can connect the input and output peripherals of the device to the CPU 902 and the memory 901.

I/O subsystem 909, which can connect input and output peripherals on the device, such as touch screen 912 and other input/control devices 910, to peripheral interface 903. The I/O subsystem 909 can include a display controller 9091 and one or more input controllers 9092 that are configured to control other input/control devices 910. Wherein, one or more input controllers 9092 receive electrical signals from other input/control devices 910 or transmit electrical signals to other input/control devices 910, and other input/control devices 910 may include physical buttons (pressing buttons and rocker buttons, etc.) ), dial, slide switch, joystick, and click wheel. In this embodiment, the input controller 9092 can be connected to any one of the following: a keyboard, an infrared port, a USB interface, and a pointing device such as a mouse.

The touch screen 912 is an input interface and an output interface between the user intelligent terminal and the user, and displays the visual output to the user. The visual output may include graphics, text, icons, videos, and the like.

Display controller 9091 in I/O subsystem 909 receives an electrical signal from touch screen 912 or an electrical signal to touch screen 912. The touch screen 912 detects the contact on the touch screen 912, and the display controller 9091 converts the detected contact into an interaction with the user interface object displayed on the touch screen 912, that is, realizes human-computer interaction, and the user interface object displayed on the touch screen 912 may be Run the game's icons or icons that are networked to the appropriate network. In this embodiment, the device may further include a light mouse, which is a touch sensitive surface that does not display a visual output, or an extension of a touch sensitive surface formed by the touch screen.

The RF circuit 905 is mainly configured to establish communication between the mobile phone and the wireless network (ie, the network side), and implement data reception and transmission between the mobile phone and the wireless network. For example, sending and receiving short messages and emails. In one embodiment, RF circuit 905 receives and transmits an RF signal, also referred to as an electromagnetic signal, and RF circuit 905 converts the electrical signal into an electromagnetic signal or converts the electromagnetic signal into an electrical signal, and through the electromagnetic signal and communication network And other devices to communicate. RF circuitry 905 may include known circuitry for performing these functions, including but not limited to an antenna system, an RF transceiver, one or more amplifiers, a tuner, one or more oscillators, a digital signal processor, Coder-CODEter (CODEC) chipset and Subscriber Identity Module (SIM) and so on.

The audio circuit 906 is primarily configured to receive audio data from the peripheral interface 903, convert the audio data into an electrical signal, and transmit the electrical signal to the speaker 911.

The speaker 911 is arranged to restore the voice signal received by the mobile phone from the wireless network through the RF circuit 905 to sound and play the sound to the user.

The power management chip 908 is configured to provide power and power management for the hardware connected to the CPU 902, the I/O subsystem, and the peripheral interface.

The embodiment further provides a server in which the compiler optimization device of the corresponding server provided by the embodiment of the present disclosure can be integrated. FIG. 10 is a schematic structural diagram of a server according to another embodiment. The server 1000 may include a memory 1001, a computer program stored on the memory 1001 and operable on the processor 1002, and the processor 1002 executing the computer program to implement a corresponding server as described in the embodiment of the present disclosure. Compilation optimization method.

The server provided in this embodiment can calculate the frequency of execution of different functions of the preset user group in the process of using the application program by using the big data method, and then calculate the first statistic value, and send the first statistic value to the intelligent terminal to guide The smart terminal pre-compiles the hotspot function before running the application for the first time, improving the efficiency of the application for the first time.

The compiler optimization device, the storage medium, the smart terminal and the server provided in the above embodiments can execute the compiler optimization method provided by the corresponding embodiment of the present disclosure, and have the corresponding functional modules and beneficial effects of the execution method. For the technical details not described in the above embodiments, reference may be made to the compiler optimization method provided by the related embodiments of the present disclosure.

Claims

A compiler optimization method applied to a smart terminal, including:

Detecting that there is an inactive target application in the smart terminal; wherein the target application includes at least one function;

And acquiring, by the preset server, a first statistic value corresponding to each function in the target application, where the first statistic value includes a statistic value calculated according to the first frequency corresponding to each function, the first The frequency includes a frequency at which each function described by the preset user group is executed during use of the target application during the first preset statistical period;

Determining at least one first hotspot function in the target application according to the at least one first statistical value corresponding to the at least one function;

Each first hotspot function is compiled into native machine code by pre-compilation of the virtual machine.
The method of claim 1 wherein the first statistical value corresponding to each function is determined by:

Collecting, in a first preset statistical period, a first frequency that each function in the preset user group is executed during use of the target application; wherein the preset user group includes multiple user;

Obtaining usage intensity information corresponding to each user in the preset user group in the first preset statistical period;

Determining, according to the usage intensity information corresponding to each user, a first weighting coefficient corresponding to each user, and correspondingly corresponding to the plurality of users according to the plurality of first weighting coefficients corresponding to the plurality of users The plurality of first frequencies are subjected to a weighted averaging operation to obtain a first statistic value corresponding to each of the functions.
The method according to claim 1 or 2, after compiling each first hotspot function into local machine code by means of pre-compilation by the virtual machine, further comprising:

Recording, in a second preset statistical period, a second frequency that each function in the target application is executed by a user of the smart terminal during use of the target application, according to a preset weight coefficient, the first Performing a weighted average operation on the statistic value and the second frequency to obtain a second statistic value corresponding to each function, where the preset weight coefficient includes a first preset weight coefficient and a second preset weight coefficient, The first statistic value corresponds to the first preset weight coefficient, and the second frequency corresponds to the second preset weight coefficient;

Determining at least one second hotspot function in the target application according to the at least one second statistical value corresponding to the at least one function;

Updating the compilation policy of the target application according to the at least one second hotspot function.
The method of claim 3, wherein the updating the compilation policy of the target application according to the at least one second hotspot function comprises at least one of the following:

When it is determined that the second hotspot function in the at least one second hotspot function is not compiled, compiling the uncompiled second hotspot function into local machine code by way of pre-compilation of the virtual machine;

with,

The local machine code corresponding to the compiled first hotspot function is deleted when it is determined that the first hotspot function that has been compiled in the at least one first hotspot function does not belong to the at least one second hotspot function.
The method according to any one of claims 1 to 4, wherein the at least one first hotspot function in the target application is determined according to at least one first statistical value corresponding to the at least one function, include:

Determining whether the first statistical value corresponding to each function reaches a first preset statistical threshold, and if so, determining each function as a first hotspot function in the target application.
The method according to claim 1, before the obtaining, by the preset server, the first statistic corresponding to each function in the target application, the method further includes:

Obtaining target attribute information of a user of the smart terminal;

The obtaining, by the preset server, the first statistic value corresponding to each function, includes:

And acquiring, from the preset server, a first statistic value corresponding to each function in the target application that matches the target attribute information.
The method according to claim 1, wherein when it is detected that the smart terminal is powered on for the first time, the target application includes all applications loaded in the smart terminal;

Determining, according to the at least one first statistic value corresponding to the at least one function, the at least one first hotspot function in the target application, including:

At least one first hotspot function corresponding to each application in all applications is determined according to a comprehensive ranking of the first statistical values corresponding to all functions in all applications.
The method of claim 3, wherein the determining the at least one second hotspot function in the target application based on the at least one second statistical value of the at least one function-to-one correspondence comprises: determining each function Whether the corresponding second statistical value reaches the second preset statistical threshold, and if so, determining each function as a second hotspot function in the target application.
A compilation optimization method applied to a server, including:

Collecting, in a first preset statistical period, a first frequency that each function function in the application is executed by the preset user group during use of the application, and calculating according to the first frequency that each function is executed The first statistical value corresponding to each function; wherein the application includes at least one function;

Receiving, by the smart terminal, an acquisition request for acquiring at least one first statistic value corresponding to the at least one function in the application, wherein the smart terminal detects that the application is not running, to the The server sends the acquisition request;

Sending, to the smart terminal, at least one first statistic value corresponding to the at least one function in the application, the at least one first statistic value is used to indicate that the smart terminal is configured according to the at least one A statistic determines at least one first hotspot function in the application and compiles each first hotspot function into local machine code by way of pre-compilation of the virtual machine.
A compiling and optimizing device is configured in the smart terminal, including:

Running a monitoring module, configured to detect whether there is an unoperated target application in the smart terminal; wherein the target application includes at least one function;

a first statistic value obtaining module, configured to: when detecting that the target application program that is not running in the smart terminal exists, obtain a first statistic value corresponding to each function in the target application program from a preset server, where The first statistic value includes a statistic value calculated according to the first frequency corresponding to each function, where the first frequency includes a preset user group in the process of using the target application program in the first preset statistic period. Describe the frequency at which each function is executed;

The first hotspot function determining module is configured to determine at least one first hotspot function in the target application according to the at least one first statistic value corresponding to the at least one function;

Compile the module, set to compile each first hotspot function to native machine code by compiling the virtual machine before it runs.
The apparatus of claim 10, wherein the first statistical value corresponding to each function is determined by:

Collecting, in a first preset statistical period, a first frequency that each function in the preset user group is executed during use of the target application; wherein the preset user group includes multiple user;

Obtaining usage intensity information corresponding to each user in the preset user group in the first preset statistical period;

Determining, according to the usage intensity information corresponding to each user, a first weighting coefficient corresponding to each user, and correspondingly corresponding to the plurality of users according to the plurality of first weighting coefficients corresponding to the plurality of users The plurality of first frequencies are subjected to a weighted averaging operation to obtain a first statistic value corresponding to each of the functions.
The apparatus according to claim 10 or 11, further comprising: a second statistic value obtaining module, configured to: after compiling each first hotspot function into local machine code in a manner of compiling by the virtual machine before running, Recording, in a second preset statistical period, a second frequency in which each function in the target application is executed by a user of the smart terminal during use of the target application, according to a preset weight coefficient, the first And performing a weighted averaging operation on the second frequency to obtain a second statistic value corresponding to each function, where the preset weight coefficient includes a first preset weight coefficient and a second preset weight coefficient, The first statistic value corresponds to the first preset weight coefficient, and the second frequency corresponds to the second preset weight coefficient;

a second hotspot function determining module, configured to: determine at least one second hotspot function in the target application according to the at least one second statistic value corresponding to the at least one function;

Compiling a policy update module, configured to: update a compilation policy of the target application according to the at least one second hotspot function.
The apparatus of claim 12, wherein the compilation policy update module is configured to perform at least one of: when determining that the second hotspot function in the at least one second hotspot function is not compiled Compiling the uncompiled second hotspot function into local machine code by compiling the virtual machine before running;

with,

The local machine code corresponding to the compiled first hotspot function is deleted when it is determined that the first hotspot function that has been compiled in the at least one first hotspot function does not belong to the at least one second hotspot function.
The device according to any one of claims 10 to 13, wherein the first hotspot function determining module is configured to: determine whether a first statistical value corresponding to each function reaches a first preset statistical threshold, and if so, Each of the functions is then determined as a first hotspot function in the target application.
The apparatus according to claim 10, further comprising: an attribute information acquiring module, configured to acquire the smart terminal before acquiring the first statistic value corresponding to each function in the target application from the preset server Target attribute information of the user;

The first statistic value obtaining module is configured to acquire, by using a preset server, a first statistic value corresponding to each function in the target application program: obtaining, by using a preset server, matching the target attribute information The first statistical value corresponding to each function in the target application.
The apparatus according to claim 10, wherein when it is detected that the smart terminal is powered on for the first time, the target application includes all applications loaded in the smart terminal;

The first hotspot function determining module is configured to: determine, according to a comprehensive sorting of the first statistical values corresponding to all the functions in all the applications, at least one first hotspot function corresponding to each application in all the applications.
A compiler optimization device, configured in the server, comprising:

a statistical value calculation module, configured to collect, during a first preset statistical period, a first frequency that the preset user group is executed in each of the applications in the process of using the application, according to each function Calculating, by the first frequency, a first statistical value corresponding to each function; wherein the application program includes at least one function;

Obtaining a request receiving module, configured to receive, by the smart terminal, an acquisition request for acquiring at least one first statistic value corresponding to the at least one function in the application, wherein the smart terminal detects that the application is not When the operation is out of date, the acquisition request is sent to the server;

And a statistical value sending module, configured to: when the smart terminal acquires an acquisition request of the at least one first statistic value corresponding to the at least one function in the application, Transmitting at least one first statistic value corresponding to a function to the smart terminal, the at least one first statistic value is used to indicate that the smart terminal determines the application according to the at least one first statistic value At least one first hotspot function, and compiling each first hotspot function into local machine code by way of pre-compilation of the virtual machine.
A computer readable storage medium storing a computer program, the computer program being executed by a processor to implement the compilation optimization method of any of claims 1-9.
An intelligent terminal comprising a memory, a processor, and a computer program stored on the memory and operable by the processor, the processor executing the computer program to implement the compiling optimization method according to any one of claims 1-8 .
A server comprising a memory, a processor, and a computer program stored on the memory and executable by the processor, the processor executing the computer program to implement the compiler optimization method of claim 9.