202 of the constant table 201 storing char, byte or int values representing the ASCII characters of the string.

In an embodiment, field reference structures 205 of the constant table 201 hold an index into the constant table 201 to one of the class information structures 203 representing the class defining the field and an index into the constant table 201 to one of the name and type information structures 204 that provides the name and descriptor of the field. Method reference structures 206 of the constant table 201 hold an index into the constant table 201 to one of the class information structures 203 representing the class defining the method and an index into the constant table 201 to one of the name and type information structures 204 that provides the name and descriptor for the method. The class information structures 203 hold an index into the constant table 201 to one of the value structures 202 holding the name of the associated class.

The name and type information structures 204 hold an index into the constant table 201 to one of the value structures 202 storing the name of the field/method and an index into the constant table 201 to one of the value structures 202 storing the descriptor.

In an embodiment, class metadata 207 includes metadata for the class, such as version number(s), number of entries in the constant pool, number of fields, number of methods, access flags (whether the class is public, private, final, abstract, etc.), an index to one of the class information structures 203 of the constant table 201 that identifies the present class, an index to one of the class information structures 203 of the constant table 201 that identifies the superclass (if any), and so forth.

In an embodiment, the field structures 208 represent a set of structures that identifies the various fields of the class. The field structures 208 store, for each field of the class, accessor flags for the field (whether the field is static, public, private, final, etc.), an index into the constant table 201 to one of the value structures 202 that holds the name of the field, and an index into the constant table 201 to one of the value structures 202 that holds a descriptor of the field.

In an embodiment, the method structures 209 represent a set of structures that identifies the various methods of the class. The method structures 209 store, for each method of the class, accessor flags for the method (e.g. whether the method is static, public, private, synchronized, etc.), an index into the constant table 201 to one of the value structures 202 that holds the name of the method, an index into the constant table 201 to one of the value structures 202 that holds the descriptor of the method, and the virtual machine instructions that correspond to the body of the method as defined in the source code files 101.

In an embodiment, a descriptor represents a type of a field or method. For example, the descriptor may be implemented as a string adhering to a particular syntax. While the exact syntax is not critical, a few examples are described below.

In an example where the descriptor represents a type of the field, the descriptor identifies the type of data held by the field. In an embodiment, a field can hold a basic type, an object, or an array. When a field holds a basic type, the descriptor is a string that identifies the basic type (e.g., “B”=byte, “C”=char, “D”=double, “F”=float, “I”=int, “J”=long int, etc.). When a field holds an object, the descriptor is a string that identifies the class name of the object (e.g., “L ClassName”). “L” in this case indicates a reference, thus “L ClassName” represents a reference to an object of class ClassName. When the field is an array, the descriptor identifies the type held by the array. For example, “[B” indicates an array of bytes, with “[” indicating an array and “B” indicating that the array holds the basic type of byte. However, since arrays can be nested, the descriptor for an array may also indicate the nesting. For example, “[L ClassName” indicates an array where each index holds an array that holds objects of class ClassName. In some embodiments, the ClassName is fully qualified and includes the simple name of the class, as well as the pathname of the class. For example, the ClassName may indicate where the file is stored in the package, library, or file system hosting the class file 200.

In the case of a method, the descriptor identifies the parameters of the method and the return type of the method. For example, a method descriptor may follow the general form “({ParameterDescriptor}) ReturnDescriptor”, where the {ParameterDescriptor} is a list of field descriptors representing the parameters and the ReturnDescriptor is a field descriptor identifying the return type. For instance, the string “V” may be used to represent the void return type. Thus, a method defined in the source code files 101 as “Object m (int I, double d, Thread t) { . . . }” matches the descriptor “(I D L Thread) L Object”.

In an embodiment, the virtual machine instructions held in the method structures 209 include operations that reference entries of the constant table 201. Using Java as an example, consider the following class:

- class A
- {
- int add12and13( ) {
- return B.addTwo(12, 13);
- }
- }

In the above example, the Java method add12and13 is defined in class A, takes no parameters, and returns an integer. The body of method add12 and13 calls static method addTwo of class B that takes the constant integer values 12 and 13 as parameters, and returns the result. Thus, in the constant table 201, the compiler 102 includes, among other entries, a method reference structure that corresponds to the call to the method B.addTwo. In Java, a call to a method compiles down to an invoke command in the bytecode of the JVM (in this case invokestatic as addTwo is a static method of class B). The invoke command is provided an index into the constant table 201 corresponding to the method reference structure that identifies the class defining addTwo “B”, the name of addTwo “addTwo”, and the descriptor of addTwo “(I I) I”. For example, assuming the aforementioned method reference is stored at index 4, the bytecode instruction may appear as “invokestatic #4”.

Since the constant table 201 refers to classes, methods, and fields symbolically with structures carrying identifying information, rather than direct references to a memory location, the entries of the constant table 201 are referred to as “symbolic references”. One reason that symbolic references are utilized for the class files 103 is because, in some embodiments, the compiler 102 is unaware of how and where the classes will be stored once loaded into the runtime environment 113. As will be described in Section 2.3, eventually the run-time representations of the symbolic references are resolved into actual memory addresses by the virtual machine 104 after the referenced classes (and associated structures) have been loaded into the runtime environment and allocated concrete memory locations.

2.2 Example Virtual Machine Architecture

FIG. 3 illustrates an example virtual machine memory layout 300 in block diagram form according to an embodiment. In order to provide clear examples, the remaining discussion will assume that the virtual machine 104 adheres to the virtual machine memory layout 300 depicted in FIG. 3. In addition, although components of the virtual machine memory layout 300 may be referred to as memory “areas”, there is no requirement that the memory areas be contiguous.

In the example illustrated by FIG. 3, the virtual machine memory layout 300 is divided into a shared area 301 and a thread area 307. The shared area 301 represents an area in memory where structures shared among the various threads executing on the virtual machine 104 are stored. The shared area 301 includes a heap 302 and a per-class area 303. In an embodiment, the heap 302 represents the run-time data area from which memory for class instances and arrays is allocated. In an embodiment, the per-class area 303 represents the memory area where the data pertaining to the individual classes are stored. In an embodiment, the per-class area 303 includes, for each loaded class, a run-time constant pool 304 representing data from the constant table 201 of the class, field, and method data 306 (for example, to hold the static fields of the class), and the method code 305 representing the virtual machine instructions for methods of the class.

The thread area 307 represents a memory area where structures specific to individual threads are stored. In FIG. 3, the thread area 307 includes thread structures 308 and thread structures 311, representing the per-thread structures utilized by different threads. To provide clear examples, the thread area 307 depicted in FIG. 3 assumes two threads are executing on the virtual machine 104. However, in a practical environment, the virtual machine 104 may execute any arbitrary number of threads, with the number of thread structures scaled accordingly.

In an embodiment, thread structures 308 includes program counter 309 and virtual machine stack 310. Similarly, thread structures 311 includes program counter 312 and virtual machine stack 313. In an embodiment, program counter 309 and program counter 312 store the current address of the virtual machine instruction being executed by their respective threads.

Thus, as a thread steps through the instructions, the program counters are updated to maintain an index to the current instruction. In an embodiment, virtual machine stack 310 and virtual machine stack 313 each store frames for their respective threads that hold local variables and partial results and is also used for method invocation and return.

In an embodiment, a frame is a data structure used to store data and partial results, return values for methods, and perform dynamic linking. A new frame is created each time a method is invoked. A frame is destroyed when the method that caused the frame to be generated completes. Thus, when a thread performs a method invocation, the virtual machine 104 generates a new frame and pushes that frame onto the virtual machine stack associated with the thread.

When the method invocation completes, the virtual machine 104 passes back the result of the method invocation to the previous frame and pops the current frame off of the stack. In an embodiment, for a given thread, one frame is active at any point. This active frame is referred to as the current frame, the method that caused generation of the current frame is referred to as the current method, and the class to which the current method belongs is referred to as the current class.

FIG. 4 illustrates an example frame 400 in block diagram form according to an embodiment. In order to provide clear examples, the remaining discussion will assume that frames of virtual machine stack 310 and virtual machine stack 313 adhere to the structure of frame 400.

In an embodiment, frame 400 includes local variables 401, operand stack 402, and run-time constant pool reference table 403. In an embodiment, the local variables 401 are represented as an array of variables that each hold a value, for example, Boolean, byte, char, short, int, float, or reference. Further, some value types, such as longs or doubles, may be represented by more than one entry in the array. The local variables 401 are used to pass parameters on method invocations and store partial results. For example, when generating the frame 400 in response to invoking a method, the parameters may be stored in predefined positions within the local variables 401, such as indexes 1-N corresponding to the first to Nth parameters in the invocation.

In an embodiment, the operand stack 402 is empty by default when the frame 400 is created by the virtual machine 104. The virtual machine 104 then supplies instructions from the method code 305 of the current method to load constants or values from the local variables 401 onto the operand stack 402. Other instructions take operands from the operand stack 402, operate on them, and push the result back onto the operand stack 402. Furthermore, the operand stack 402 is used to prepare parameters to be passed to methods and to receive method results. For example, the parameters of the method being invoked could be pushed onto the operand stack 402 prior to issuing the invocation to the method. The virtual machine 104 then generates a new frame for the method invocation where the operands on the operand stack 402 of the previous frame are popped and loaded into the local variables 401 of the new frame. When the invoked method terminates, the new frame is popped from the virtual machine stack and the return value is pushed onto the operand stack 402 of the previous frame.

In an embodiment, the run-time constant pool reference table 403 contains a reference to the run-time constant pool 304 of the current class. The run-time constant pool reference table 403 is used to support resolution. Resolution is the process whereby symbolic references in the constant pool 304 are translated into concrete memory addresses, loading classes as necessary to resolve as-yet-undefined symbols and translating variable accesses into appropriate offsets into storage structures associated with the run-time location of these variables.

2.3 Loading, Linking, and Initializing

In an embodiment, the virtual machine 104 dynamically loads, links, and initializes classes. Loading is the process of finding a class with a particular name and creating a representation from the associated class file 200 of that class within the memory of the runtime environment 113. For example, creating the run-time constant pool 304, method code 305, and field and method data 306 for the class within the per-class area 303 of the virtual machine memory layout 300. Linking is the process of taking the in-memory representation of the class and combining it with the run-time state of the virtual machine 104 so that the methods of the class can be executed. Initialization is the process of executing the class constructors to set the starting state of the field and method data 306 of the class and/or create class instances on the heap 302 for the initialized class.

The following are examples of loading, linking, and initializing techniques that may be implemented by the virtual machine 104. However, in many embodiments the steps may be interleaved, such that an initial class is loaded, then during linking a second class is loaded to resolve a symbolic reference found in the first class that in turn causes a third class to be loaded, and so forth. Thus, progress through the stages of loading, linking, and initializing can differ from class to class. Further, some embodiments may delay (perform “lazily”) one or more functions of the loading, linking, and initializing process until the class is actually required. For example, resolution of a method reference may be delayed until a virtual machine instruction invoking the method is executed. Thus, the exact timing of when the steps are performed for each class can vary greatly between implementations.

To begin the loading process, the virtual machine 104 starts up by invoking the class loader 107 that loads an initial class. The technique by which the initial class is specified will vary from embodiment to embodiment. For example, one technique may have the virtual machine 104 accept a command line argument on startup that specifies the initial class.

To load a class, the class loader 107 parses the class file 200 corresponding to the class and determines whether the class file 200 is well-formed (meets the syntactic expectations of the virtual machine 104). If not, the class loader 107 generates an error. For example, in Java the error might be generated in the form of an exception that is thrown to an exception handler for processing. Otherwise, the class loader 107 generates the in-memory representation of the class by allocating the run-time constant pool 304, method code 305, and field and method data 306 for the class within the per-class area 303.

In some embodiments, when the class loader 107 loads a class, the class loader 107 also recursively loads the super-classes of the loaded class. For example, the virtual machine 104 may ensure that the super-classes of a particular class are loaded, linked, and/or initialized before proceeding with the loading, linking, and initializing process for the particular class.

During linking, the virtual machine 104 verifies the class, prepares the class, and performs resolution of the symbolic references defined in the run-time constant pool 304 of the class.

To verify the class, the virtual machine 104 checks whether the in-memory representation of the class is structurally correct. For example, the virtual machine 104 may check that each class except the generic class Object has a superclass, check that final classes have no sub-classes and final methods are not overridden, check whether constant pool entries are consistent with one another, check whether the current class has correct access permissions for classes/fields/structures referenced in the constant pool 304, check that the virtual machine 104 code of methods will not cause unexpected behavior (e.g. making sure a jump instruction does not send the virtual machine 104 beyond the end of the method), and so forth. The exact checks performed during verification are dependent on the implementation of the virtual machine 104. In some cases, verification may cause additional classes to be loaded, but does not necessarily require those classes to also be linked before proceeding. For example, assume Class A contains a reference to a static field of Class B. During verification, the virtual machine 104 may check Class B to ensure that the referenced static field actually exists that might cause loading of Class B, but not necessarily the linking or initializing of Class B. However, in some embodiments, certain verification checks can be delayed until a later phase, such as being checked during resolution of the symbolic references. For example, some embodiments may delay checking the access permissions for symbolic references until those references are being resolved.

To prepare a class, the virtual machine 104 initializes static fields located within the field and method data 306 for the class to default values. In some cases, setting the static fields to default values may not be the same as running a constructor for the class. For example, the verification process may zero out or set the static fields to values that the constructor would expect those fields to have during initialization.

During resolution, the virtual machine 104 dynamically determines concrete memory address from the symbolic references included in the run-time constant pool 304 of the class. To resolve the symbolic references, the virtual machine 104 utilizes the class loader 107 to load the class identified in the symbolic reference (if not already loaded). Once loaded, the virtual machine 104 has knowledge of the memory location within the per-class area 303 of the referenced class and its fields/methods. The virtual machine 104 then replaces the symbolic references with a reference to the concrete memory location of the referenced class, field, or method. In an embodiment, the virtual machine 104 caches resolutions to be reused in case the same class/name/descriptor is encountered when the virtual machine 104 processes another class. For example, in some cases, class A and class B may invoke the same method of class C. Thus, when resolution is performed for class A, that result can be cached and reused during resolution of the same symbolic reference in class B to reduce overhead.

In some embodiments, the step of resolving the symbolic references during linking is optional. For example, an embodiment may perform the symbolic resolution in a “lazy” fashion, delaying the step of resolution until a virtual machine instruction that requires the referenced class/method/field is executed.

During initialization, the virtual machine 104 executes the constructor of the class to set the starting state of that class. For example, initialization may initialize the field and method data 306 for the class and generate/initialize any class instances on the heap 302 created by the constructor. For example, the class file 200 for a class may specify that a particular method is a constructor that is used for setting up the starting state. Thus, during initialization, the virtual machine 104 executes the instructions of that constructor.

In some embodiments, the virtual machine 104 performs resolution on field and method references by initially checking whether the field/method is defined in the referenced class. Otherwise, the virtual machine 104 recursively searches through the super-classes of the referenced class for the referenced field/method until the field/method is located, or the top-level superclass is reached, in which case an error is generated.

3. Multi-Threading Recommendation Architecture

FIG. 5 illustrates a system 500 in accordance with one or more embodiments. As illustrated in FIG. 5, system 500 includes a computing architecture 100, as described in FIG. 1, a multi-threading implementation engine 510, and a data repository 520. In one or more embodiments, the system 500 may include more or fewer components than the components illustrated in FIG. 5. The components illustrated in FIG. 5 may be local to or remote from each other. The components illustrated in FIG. 5 may be implemented in software and/or hardware. Each component may be distributed over multiple applications and/or machines. Multiple components may be combined into one application and/or machine. Operations described with respect to one component may instead be performed by another component.

In one or more embodiments, multi-threading implementation engine 510 refers to hardware and/or software configured to perform operations described herein for recommending source code segments for applying parallel multi-thread processing. Examples of operations for recommending source code segments for applying parallel multi-thread processing are described below with reference to FIG. 6.

In an embodiment, the multi-threading implementation engine 510 is implemented on one or more digital devices. The term “digital device” generally refers to any hardware device that includes a processor. A digital device may refer to a physical device executing an application or a virtual machine. Examples of digital devices include a computer, a tablet, a laptop, a desktop, a netbook, a server, a web server, a network policy server, a proxy server, a generic machine, a function-specific hardware device, a hardware router, a hardware switch, a hardware firewall, a hardware firewall, a hardware network address translator (NAT), a hardware load balancer, a mainframe, a television, a content receiver, a set-top box, a printer, a mobile handset, a smartphone, a personal digital assistant (PDA), a wireless receiver and/or transmitter, a base station, a communication management device, a router, a switch, a controller, an access point, and/or a client device.

In one or more embodiments, a data repository 540 is any type of storage unit and/or device (e.g., a file system, database, collection of tables, or any other storage mechanism) for storing data. Further, a data repository 540 may include multiple different storage units and/or devices. The multiple different storage units and/or devices may or may not be of the same type or located at the same physical site. Further, a data repository 540 may be implemented or executed on the same computing system as the multi-threading implementation engine 510. Additionally, or alternatively, a data repository 540 may be implemented or executed on a computing system separate from the multi-threading implementation engine 510. The data repository 540 may be communicatively coupled to the multi-threading implementation engine 510 via a direct connection or via a network.

Information describing the source code 101, class files 103, and source code modification rules 141 may be implemented across any of components within the system 500. However, this information is illustrated within the data repository 540 for purposes of clarity and explanation.

In an embodiment, the multi-threading implementation engine 510 is implemented on one or more digital devices. The term “digital device” generally refers to any hardware device that includes a processor. A digital device may refer to a physical device executing an application or a virtual machine. Examples of digital devices include a computer, a tablet, a laptop, a desktop, a netbook, a server, a web server, a network policy server, a proxy server, a generic machine, a function-specific hardware device, a hardware router, a hardware switch, a hardware firewall, a hardware firewall, a hardware network address translator (NAT), a hardware load balancer, a mainframe, a television, a content receiver, a set-top box, a printer, a mobile handset, a smartphone, a personal digital assistant (PDA), a wireless receiver and/or transmitter, a base station, a communication management device, a router, a switch, a controller, an access point, and/or a client device.

A static code analysis engine 511 analyzes source code files 101 to identify portions of the source code files 101 that are candidates for parallel multi-thread processing. For example, the static code analysis engine 511 may identify loop-type code structures and data structures that implement iterative processes or a process that repeats and uses a result of a previous iteration in a subsequent iteration. The static code analysis engine 511 may identify code control flow to identify tasks that may be executed independently from each other. For example, the static code analysis engine 511 may identify branches and conditional statements that can execute concurrently. The static code analysis engine 511 may predict whether a particular segment of source code is likely to be long-running or compute-intensive. A long-running task may include, for example, an iterative task that includes a number of iterations exceeding a threshold. Compute-intensive tasks includes may, for example, be characterized by, for example, a number of branches, a number of mathematical algorithms or computations, or a number of modifications to variables that exceed respective thresholds. The static code analysis engine 511 identifies data dependencies among portions of source code. The static code analysis engine 511 identifies source code segments as candidates for parallel multi-thread processing based on whether the segments include dependencies with other segments. For example, if a process is iterative and compute-intensive but also includes a dependency during the iterative process, the static code analysis engine 511 may reject the code segment as a candidate for parallel multi-thread processing. The dependency analysis may classify different dependencies as either multi-threading candidates or not. For example, a dependency that repeatedly compares a computed value to a threshold value may not be a multi-threading candidate. A dependency that includes a simple summing of a large set of numbers may be a multi-threading candidate since the value of a final sum would not change if multiple-processes simultaneously summed sub-sets of numbers.

In one or more embodiments, a machine learning algorithm 514 trains a multi-threading candidate classification model 515 to identify static code segments that are candidates for multi-threading processes. The machine learning algorithm 514 can be iterated to train a target model f that best maps a set of input variables to an output variable. A machine learning algorithm may include supervised components and/or unsupervised components. Various types of algorithms may be used, such as linear regression, logistic regression, linear discriminant analysis, classification, and regression trees, learning vector quantization, support vector machine, and/or backpropagation.

In an embodiment, a set of training data includes datasets and associated labels. The datasets are associated with input variables (e.g., static code segment characteristics, such as a type of process represented, a size of data used by a static code segment, and a number of variables accessed and/or modified by the static code segment) for the target model f. In one or more embodiments, the input variables for the static code segment includes measured or estimated performance characteristics. For example, a system may estimate or measure a run time of a set of machine code corresponding to a source code segment. In addition, or in the alternative, the system may estimate or measure a computing resource usage of the set of machine code corresponding to the source code segment. The associated labels are associated with the output variable (e.g., a performance improvement achieved by implementing parallel multi-thread processing, a number of threads with which to execute the compiled static code segment, and/or a multi-threading implementation category of the static code segment) of the target model f. The training data may be updated based on, for example, feedback on the accuracy of the current target model f. Updated training data is fed back into the machine learning algorithm that in turn updates the target model f.

A machine learning algorithm is an algorithm that can be iterated to train a target model f that best maps a set of input variables to an output variable, using a set of training data. The training data includes datasets and associated labels. The datasets are associated with input variables for the target model f. The associated labels are associated with the output variable of the target model f. The training data may be updated based on, for example, feedback on the predictions by the target model f and accuracy of the current target model f. Updated training data is fed back into the machine learning algorithm that in turn updates the target model f.

A machine learning algorithm 514 generates a target model f such that the target model f best fits the datasets of training data, including static source code segment characteristics, to the labels of the training data. Additionally, or alternatively, a machine learning algorithm 114 generates a target model f such that when the target model f is applied to the datasets of the training data, a maximum number of results determined by the target model f matches the labels of the training data. Different target models may be generated based on different machine learning algorithms and/or different sets of training data.

In an embodiment, a machine learning algorithm can be iterated to learn whether particular source code segments are candidates for implementing multi-threading processes. In an embodiment, a set of training data includes historical or synthetically-generated source code segments. The source code segments are associated with labels, indicating a classification of the source code segment as being a multi-threading process candidate or not. According to one example embodiment, the classification includes a number of threads. The number of threads may correspond to a historically identified optimal number of threads associated with executing a particular source code segment, for example.

A run-time analysis engine 512 analyzes performance of execution of class files 103 on the execution platform 112, as described in FIG. 1, to identify execution attributes of source code segments. For example, the run-time analysis engine 512 may determine that when a particular source code segment was executed as compiled machine code, it satisfied one or more multi-threading criteria. For example, the run-time analysis engine 512 may determine that a segment of compiled code (such as machine code) was associated with a high level of compute resource utilization or a long execute time, exceeding pre-defined threshold values. The run-time analysis engine 512 identifies characteristics associated with source code that may not be apparent from the static source code. For example, a set of static source code may include a particular function call to an external application. It may not be apparent from the static source code what effect the function call will have at execution time. Accordingly, the static code analysis engine 511 may not be able to determine whether the source code segment meets a set of parallel multi-thread processing criteria. However, the run-time analysis engine 512 may determine, based on measured run-time data of compiled machine code corresponding to the static source code, that the function call results in a level of computing-resource usage exceeding a threshold. The run-time analysis engine 512 may consequently classify the corresponding source code segment as a candidate for applying parallel multi-thread processing.

A source code modification engine 513 applies a set of source code modification rules 141 to generate a modified set of source code that implements parallel multi-thread processing. The modified set of source code may include, for example, a same set of variables to be used in a set of processes in the source code segment. For example, if a source code segment performs a complex calculation on a set of variable values, the source code modification engine 513 modifies the source code to perform the same complex calculation on the same set of variable values using parallel multi-thread processing. The modifications to the source code may include (a) modifications to the set of source code that specify two or more threads for performing the process, (b) modifications to the set of source code to format the code in such a way that a compiler automatically compiles the source code to generate compiled code that utilizes parallel multi-thread processing, and (c) insertion of a compiler directive preceding the source code segment to direct a compiler to implement the source code segment using parallel multi-thread processing. In one or more embodiments, the source code modification rules 141 include, for example, the following: a number of threads to be generated based on source code segment characteristics, naming conventions for the threads, rules for assigning priority values to the threads, and stack data associated with a stack for storing thread data.

In one or more embodiments, interface 516 refers to hardware and/or software configured to facilitate communications between a user and a multi-threading implementation engine 510. Interface 516 renders user interface elements and receives input via user interface elements. Examples of interfaces include a graphical user interface (GUI), a command line interface (CLI), a haptic interface, and a voice command interface. Examples of user interface elements include checkboxes, radio buttons, dropdown lists, list boxes, buttons, toggles, text fields, date and time selectors, command lines, sliders, pages, and forms.

In an embodiment, different components of interface 516 are specified in different languages. The behavior of user interface elements is specified in a dynamic programming language, such as JavaScript. The content of user interface elements is specified in a markup language, such as hypertext markup language (HTML) or XML User Interface Language (XUL). The layout of user interface elements is specified in a style sheet language, such as Cascading Style Sheets (CSS). Alternatively, interface 516 is specified in one or more other languages, such as Java, C, or C++.

In one or more embodiments, the interface 516 renders a GUI to allow a user to specify one or more source code modification rules 141. For example, a user may set configuration values associated with a number of threads to be generated and/or used to implement processes. The user may set rules that cause a computer to automatically create a number of threads to execute a set of code based on factors such as the following: a type of process being performed (e.g., a loop-type function, mathematical calculations, a branch-type function), a number of total processing cores available for executing parallel threads, a priority value assigned to a particular source code segment, or a process implemented by the source code segment, estimated compute resources utilized by the source code segment, and estimated run-time associated with the source code segment. Based on the user input, the system may store a set source code modification rules 141.

A multi-threading implementation engine 510 communicates with the computing architecture 100 to generate recommendations and modify to source code to implement parallel multi-thread processing. In one or more embodiments, multi-threading implementation engine 510 refers to hardware and/or software configured to perform operations described herein for generating recommendations and modify to source code to implement parallel multi-thread processing. Examples of operations for generating recommendations and modifications to source code to implement parallel multi-thread processing are described below with reference to FIG. 6.

4. Modifying Source Code to Implement Parallel Multi-Thread Processing

FIG. 6 illustrates a flowchart showing techniques for generating recommendations and modifying source code to implement parallel multi-thread processing. One or more operations illustrated in FIG. 6 may be modified, rearranged, or omitted. Accordingly, the particular sequence of operations illustrated in FIG. 6 should not be construed as limiting the scope of one or more embodiments.

A system accesses a set of source code (Operation 602). The source code may be written in one or more programming languages, such as PYTHON, C++, or JAVA. The source code includes one or more segments that are compiled and executed to perform one or more functions. The segments may be characterized by start and end sets of parentheses, brackets, or carrots, for example.

The system selects a particular source code segment for analysis (Operation 604). For example, the system may sequentially select each segment separated by start/end punctuation in the source code. The system may analyze source code segments according to hierarchies. For example, if a source code segment includes one set of operations with a corresponding set of start/end punctuation nested within another set of operations corresponding to another set of start/end punctuation, the system may select each set of operations as a separate source code segment. The system may analyze the entire source code file for particular types of operations. The system may select the source code corresponding to the particular types of operations as source code segments. The system may refrain from selecting, for analysis, other portions of the source code that exclude the particular types of operations.

The system analyzes the source code to identify source code segments that meet parallel multi-thread processing criteria (Operation 606). For example, the system may identify types of methods, functions, and operations that may be multi-threading candidates. According to one embodiment, the system identifies particular code structures as matching predefined candidate structures. For example, the system may determine that the particular structure of source code embodied in a method would, when executed as compiled code, perform an iterative operation or a loop operation. According to an embodiment, the system parses the source code to identify the different components, including method names, function names, variable names, variable values, data type names, explanatory notations, initialization operations, operands, calls, interrupts, punctuation or syntax, and any other components that make up the source code. The system may tokenize the parsed source code to identify different components described above. For example, the terms “public,” “class,” “Main,” “void,” “static,” and the symbols “(”, “)”, “{”, “//”, and “:” may all be identified as separate tokens. The system searches the parsed and/or tokenized source code content to identify patterns matching multi-threading candidate patterns. An example pattern may include a token representing a particular function type, such as a counting function or a summing function, applied to a variable representing a particular data type, such as a matrix of values.

The system may trace components through multiple different source code segments. For example, if a source code segment specifies a particular variable, the system may search other source code segments for initialization of the variable and other functions that use the variable. Similarly, if a source code segment identifies a particular memory address, the system may search other source code segments for other functions that access data at the same memory address. Examples of types of functions and operations include matrix summing, matrix sorting, matrix multiplication, matrix searching, data parsing, data filtering, data analysis (e.g., identifying particular values within a set of data), visual graphics processing, network requests, disk input/output operations, web server request handling, query request handling, reading data from a file, and writing data to two or more files.

According to an example embodiment, a system may identify loop-type functions and iterative functions within source code as types of functions that may be multi-threading candidates. The system may determine that a particular pattern of tokens, including a method name corresponding to a particular method, in combination with a particular set of variable values for a corresponding set of variables corresponds to a loop-type function. The system may determine whether an identified loop-type function executes a number of times exceeding a threshold. For example, the system may be configured to consider a loop-type function for parallel multi-thread processing if the loop executes more than 10,000 times. The system may further identify branches and conditional statements that can execute concurrently.

Identifying segments of source code as multi-threading candidates may include estimating a run-time of the source code segment. For example, one loop-type function may repeat a simple calculation 10,000 times. Accordingly, the loop-type function may have an estimated run-time less than a threshold. Another loop-type function may repeat a complex function call 10,000 times. Accordingly, the latter loop-type function may have an estimated run time greater than the threshold. The system may identify the latter loop-type function as a candidate for multi-threading and the former loop-type function as not being a candidate for multi-threading.

In addition, or in the alternative, identifying a segment of source code as a multi-threading candidate may include estimating computing resources that would be used by the compiled source code during execution. The system may estimate compute resources based on a set of factors including the following: a number and complexity of functions and calculations performed within a source code segment, a number and type of variables generated and/or accessed within the source code segment, a number of iterations or loops executed by an iterative process or a loop-type function, a number of branches or conditional statements within a source code segment, and a length of the source code segment. A compute-intensive task includes tasks that include, for example, a number of branches, a number of mathematical algorithms or computations, or a number of modifications to variables that exceed respective thresholds.

In one or more embodiments, the system determines the run time of a source code segment by compiling the source code and executing the compiled code. The system may monitor the execution of the code to identify the run time of the compiled code that corresponds to the source code segment. In one or more embodiments, the system determines the computing resources required to execute a source code segment by compiling the source code and executing the compiled code. The system may monitor the execution of the code to identify the computing resources required to execute a portion of the compiled code that corresponds to the source code segment.

In one or more embodiments, identifying a source code segment as a candidate for parallel multi-thread processing includes determining whether a source code segment includes an operational dependency on any other source code segment. For example, if one source code segment utilizes a particular variable that is manipulated by another source code segment, the former source code segment may not be a candidate for parallel multi-thread processing. On the other hand, if two source code segments read a value of the variable without altering the value, the source code segments may be candidates for parallel multi-thread processing. According to one example embodiment, the system identifies tokens representing variables and data sets used by and generated by particular source code segments. In an example in which the system identifies a set of source code as corresponding to a loop-type operation, the system analyzes the source code to determine whether the variables and data sets are used in multiple operations of the loop. In an example in which the system identifies a set of source code as corresponding to an iterative operation, the system analyzes the source code to determine whether the variables and data sets are used in multiple iterations of the iterative operation. The system further traces the generation and manipulation of variables and data sets among multiple different source code segments within a set of source code. For example, one source code segment may write a value to a location in memory. Another source code segment may access the value from the location in memory and increment it using a loop-type operation. In its analysis of source-code segment corresponding to the loop-type operation, the system identifies the dependency from the loop-type operation to the source code segment that writes the value to the location in memory.

According to one example embodiment, determining whether a source code segment is a candidate for parallel multi-threading operations includes determining that it does not access the same data set as another source code segment. According to another example, the system may determine that a source code segment is a candidate for parallel multi-threading operations based on determining that the source code segment may be performed before or after another source code segment accessing the same data set without altering a result of operations associated with the source code segments.

One or more embodiments identify candidate source code segments for applying parallel multi-thread processing based on a combination of one or more of the following: long-running tasks, compute-intensive tasks, whether tasks include data dependencies to other tasks, loop-type functions, iterative-type functions, and control flow code that may be executed in parallel. In other words, upon identifying a type of task that may be a candidate for multi-threading, such as an iterative-type task, the system further estimates computing resources used by the task and whether the task includes data dependencies. An iterative-type task with a low computing-resource usage may not be a candidate for multi-threading. Similarly, an iterative-type task with data dependencies to other tasks may not be a candidate for multi-threading. On the other hand, an iterative-type task associated with a high computing-resource-usage level and without data dependencies may be a candidate for implementing parallel multi-thread processing.

According to one embodiment, the system categorizes the source code segments based on a graded scale. The categories may include the following: (a) a source code segment is a candidate for parallel multi-thread processing, (b) the source code segment is not a candidate for parallel multi-thread processing, (c) the source code segment would be a candidate for parallel multi-thread processing if re-written, and (d) the system cannot determine if the source code segment is a candidate for multi-threading.

If the system cannot determine whether a source code segment is a candidate for parallel multi-thread processing, the system may either (a) treat the source code segment as a non-candidate for multi-threading or (b) flag the source code segment for human review.

According to one or more embodiments, the system applies a trained machine learning model to a set of source code to identify candidate source code segments for parallel multi-thread processing. For example, the system may train a neural network on a data set of source code segments. Each source code segment corresponds to a label. The label may specify one or more of (a) whether compiled code corresponding to the source code was, or could be, implemented using parallel multi-thread processing, (b) a number of threads that were, or may be, used to execute the compiled code, and (c) performance characteristics associated with the compiled code. In an example in which a source code segment corresponds to a historical (e.g., previously implemented and/or executed) set of source code, the number of parallel threads corresponds to the number of threads that were used to execute a set of compiled code corresponding to the source code segment. In another example, a source code segment may be synthetically generated to include a particular operation and a number of threads determined to be an optimal number for the particular operation. Upon being trained on the data set of source code segments, the machine learning model is configured to receive a target source code segment as input data and generate as output data a target number of threads for executing compiled code corresponding to the target source code segment. Accordingly, the trained machine learning model is a specialized computing device configured to perform a particular function of determining whether a source code segment should be a multi-thread processing candidate and how many threads should be used to execute the compiled code corresponding to the source code segment.

If the system determines that a source code segment is not a candidate for parallel multi-thread processing, the system ends the analysis of the source code segment (Operation 608). If the source code includes additional source code segments to be analyzed, the system may repeat the process of selecting a source code segment (Operation 604) and analyzing the source code segment (Operation 606).

If the system determines that the source code segment is a candidate for parallel multi-thread processing, the system generates a modified set of source code to implement parallel multi-thread processing (Operation 610). According to one example embodiment, the system applies a set of source-code-modification rules to modify the source code segment to implement parallel multi-thread processing. The source-code-modification rules may direct the system to specify (a) a particular number of threads to be generated for a particular operation type and (b) a naming convention to be applied to the threads. The system may generate source code that specifies the threads to be generated to execute the corresponding source code segment.

According to another example embodiment, the system modifies the source code to comply with a format that causes a compiler to utilize parallel multi-thread processing. For example, a set of source code that is initially written to be implemented as a “for” loop may be rewritten to generate a dynamic thread that automatically utilizes parallel multi-thread processing to execute a corresponding set of compiled code.

An example source code segment is provided below:

- function processData (dataset) {
- // perform some initial processing
- let results=[ ];
- for (let i=0; i<dataset.length; i++) {
- // perform some calculations on the dataset
- let value=dataset [i];
- //store the results
- results.push (value);
- }
- // perform some final processing
- return results;
- }

The system identifies the “for” loop in the above source code segment as a multi-threading candidate based on the function type (e.g., “for”) and the lack of data dependencies with other source code segments. The system re-writes the source code segment to generate a dynamic thread to run calculations in parallel. The system re-rewrites the source code as follows:

- function processData (dataset) {
- // perform some initial processing
- // create a new dynamic thread to perform the calculations
- let worker=new Worker (‘calculation.js’);
- //pass the dataset to the worker thread
- worker.postMessage (dataset);
- //listen for messages from the worker thread
- worker.onmessage=function (event) {
- // store the results
- results.push (event.data);
- // check if all results have been received
- if (results.length===dataset.length) {
- // perform some final processing
- return results;
- }}}

According to yet another example, the system modifies the source code by inserting a compiler directive into the source code to direct a compiler to implement parallel multi-thread processing for executing a particular segment of source code being compiled. The compiler directive may specify a number of threads to be generated to execute the compiled code, for example.

The system presents the modified set of source code to a user as a recommendation for modifying the source code (Operation 612). Upon a user confirmation, the system replaces the original source code with the modified source code. According to an alternative embodiment, the system may automatically, without user intervention, apply a set of source-code-modification rules to modify a set of source code to utilize parallel multi-thread processing. The system may, without user confirmation or intervention, replace a set of original source code with the modified source code.

5. Training a Machine Learning Model

FIG. 7 illustrates an example set of operations for training a machine learning model to predict a number of threads to generate for implementing parallel multi-thread processing on a particular segment of source code in accordance with one or more embodiments. One or more operations illustrated in FIG. 7 may be modified, rearranged, or omitted. Accordingly, the particular sequence of operations illustrated in FIG. 3 should not be construed as limiting the scope of one or more embodiments.

In some embodiments, a system (e.g., one or more components of architecture 100 illustrated in FIG. 1) accesses historical source code data (Operation 702). Obtaining the historical source code data may include obtaining source code segments for programs and applications previously generated to be executed on a particular computing system. According to one or more embodiments, obtaining the historical source code data includes obtaining segments of source code such as sections bracketed by symbols representing the beginning of a function or method to be implemented and the end of the function or method. In one example, the symbols representing the start/end of a function in source code are open brackets and closed brackets. In one example, a system pre-processes a set of source code to identify source code segments bounded by the start/end symbols. For example, a set of source code may include hundreds of static source code segments bounded by start/end symbols, including nested source code segments bounded by start/end symbols. The system may pre-process the source code to identify source code segments bounded by start/end symbols at a particular hierarchal level. The system may identify source code segments that perform particular types of operations, functions, or tasks. The system may separate each portion of source code bounded by start/end symbols as a separate code segment. The system may further generate code segments that include one or more nested segments bracketed by start/end symbols.

In one or more embodiments, the system obtains synthetically-generated source code data in addition to, or instead of, the historical source code data. Synthetically-generated source code data may be generated to have characteristics for training the machine learning model. The synthetically-generated source code data may not be source code that has historically been implemented or developed for execution in a system. For example, the system may generate a set of synthetic source code data including a particular branch function that may be implemented with multi-threading techniques to train the machine learning model to predict multiple threads for implementing the particular branch function.

The system uses the historical source code data to generate a set of training data (Operation 704). The set of training data includes at least one classification label for a set of source code segment characteristics. For example, the system may identify in the historical source code data a particular function, a set of variables utilized by the function, a set of data accessed by the function, and a length of a source code segment that includes the function. The system may further identify in the historical source code data information specifying a number of threads used to execute the source code segment. The system generates one or more labels corresponding to the source code segment. One label may identify the source code segment as a candidate for implementation using multi-threading techniques. Another label may specify a number of threads associated with the source code segment. Another label may specify a type of function represented in the source code. Yet another label may specify a measured or estimated run time to execute compiled code corresponding to the source code.

According to one embodiment, the system obtains the historical data and the training data set from a data repository storing labeled data sets. The training data set may be generated and updated by an enterprise that generates source code. Alternatively, the training data set may be generated and maintained by a third party.

In some embodiments, generating the training data set includes generating a set of feature vectors for the labeled examples. A feature vector for an example may be n-dimensional, where n represents the number of features in the vector. The number of features that are selected may vary depending on the particular implementation. The features may be curated in a supervised approach or automatically selected from extracted attributes during model training and/or tuning. Example features include the following: information about an entity that generated the source code, a function that is specified in the source code such that executing the compiled source code would result in performance of the function, and a language or format of the source code and/or compiled code. Other example features include a number of variables utilized by a source code segment, a data set associated with the source code, an estimated or measured run time for the source code, and a number of threads utilized to execute compiled code corresponding to the source code. In some embodiments, a system generates one or more estimates or predictions that are used as features for source code. For example, the system may identify a run time of a function in the source code. The run time may be used as a feature. Additionally, or alternatively, the system may estimate the run time of the function. The system may generate the run-time estimate based on a type of function, a number of loops, iterations, and/or branches included in the function, a number of calculations performed by the function, and the types of calculations performed by the function. In some embodiments, a feature within a feature vector is represented numerically by one or more bits. The system may convert categorical attributes to numerical representations using an encoding scheme, such as one-hot encoding, label encoding, and binary encoding. One-hot encoding creates a unique binary feature for each possible category in an original feature. In one-hot encoding, when one feature has a value of 1, the remaining features have a value of 0. For example, if a type of function represented in source code has 10 different possible conditions, the system may generate ten different features of an input data set. When one condition is present (e.g., value “1”), the remaining features are assigned a value “0.” According to another example, the system may perform label encoding by assigning a unique numerical value to each condition. According to yet another example, the system performs binary encoding by converting numerical values to binary digits and creating a new feature for each digit.

The system applies a machine learning algorithm to the training data set to train the machine learning model (Operation 706). For example, the machine learning algorithm may analyze the training data set to train neurons of a neural network with particular weights and offsets to associate particular source code segments with particular multi-thread prediction value labels. Generating the multi-thread prediction value labels may include identifying particular features in the source code segments and particular numbers of threads that correspond to the particular combinations of features.

In some embodiments, the system iteratively performs the following: apply the machine learning algorithm to a set of input data including a source code segment to generate an output set of labels including a value representing a number of threads, compare the generated labels to pre-generated labels associated with the input data, adjusts weights and offsets of the algorithm (e.g., of each neuron in a neural network) based on an error (e.g., a difference between the algorithm-generated label and the pre-generated label), and apply the algorithm to another set of input data including another source code segment. Additionally, or alternatively, the system may generate and train other candidate models, such as support vector machines, decision trees, Bayes classifiers, and/or fuzzy logic models, as previously described.

In some embodiments, the system compares the labels estimated through the one or more iterations of the machine learning model algorithm with ground truth labels to determine an estimation error (Operation 708). In an example in which the test set of examples is a set of historical source code segments, the ground truth labels may include the number of threads that were historically used to execute the compiled source code segments. According to an alternative embodiment in which the test set of examples includes synthetically-generated examples, the ground truth labels may include a number of threads assigned to the examples by a system or code developer. The system may perform this comparison for a test set of examples that may be a subset of examples in the training dataset that were not used to generate and fit the candidate models. The total estimation error for a particular iteration of the machine learning algorithm may be computed as a function of the magnitude of the difference and/or the number of examples for which the estimated label was wrongly predicted.

In some embodiments, the system determines whether to adjust the weights and/or other model parameters based on the estimation error (Operation 710). Adjustments may be made until a candidate model that minimizes the estimation error or otherwise achieves a threshold level of estimation error is identified. The process may return to Operation 708 to adjust and continue training the machine learning model.

In some embodiments, the system selects machine learning model parameters based on the estimation error meeting a threshold accuracy level (Operation 712). For example, the system may select a set of parameter values for a machine learning model based on determining that the trained model has an accuracy level for predicting a number of threads to be used to execute compiled source code of at least 98%.

In some embodiments, the system trains a neural network using backpropagation. Backpropagation is a process of updating cell states in the neural network based on gradients determined as a function of the estimation error. With backpropagation, nodes are assigned a fraction of the estimated error based on the contribution to the output and adjusted based on the fraction.

Additionally, or alternatively, the system may train other types of machine learning models. For example, the system may adjust the boundaries of a hyperplane in a support vector machine or node weights within a decision tree model to minimize estimation error. Once trained, the machine learning model may be used to estimate labels for new examples of source code segments.

In embodiments in which the machine learning algorithm is a supervised machine learning algorithm, the system may optionally obtain feedback on the various aspects of the analysis described above (Operation 714). For example, the feedback may affirm or revise labels generated by the machine learning model. The machine learning model may indicate that a particular source code segment is associated with a label predicting two threads or that the compiled source code should be executed as two threads executing in parallel on two processing cores. The system may receive feedback indicating that the source code segment should instead be associated with a label to execute the compiled source code as four threads executing in parallel on four processing cores. According to another example, a machine learning model may predict a particular run time for a set of compiled code corresponding to a source code segment. The system may receive feedback modifying the predicted run time. Based on the feedback, the machine learning training set may be updated, thereby improving its analytical accuracy (Operation 716). Once updated, the system may further train the machine learning model by optionally applying the model to additional training data sets of source code segments.

One or more embodiments utilize one or more trained machine learning models to determine whether to modify source code to utilize parallel multi-thread processing and how many threads to use. While an example is provided above in which a machine learning model predicts a number of threads for executing compiled code corresponding to a source code segment, in one or more embodiments, the system trains the machine learning model to instead categorize the source code as being a candidate for multi-threading or not. For example, a machine learning model may be trained to classify a source code segment as follows: (a) a source code segment is a candidate for parallel multi-thread processing, (b) the source code segment is not a candidate for parallel multi-thread processing, (c) the source code segment would be a candidate for parallel multi-thread processing if re-written, and (d) the model cannot determine if the source code segment is a candidate for multi-threading. According to another example, the system trains a machine learning model to predict a run time of source code segments. Based on the predicted run times, the system determines an improvement between a run time of the source code segment when parallel multi-thread processing is utilized compared to when it is not. The system generates recommendations for modifying source code to implement parallel multi-thread processing based on the machine learning model run-time predictions.

6. Example Embodiment

A detailed example is described below for purposes of clarity. Components and/or operations described below should be understood as one specific example that may not be applicable to certain embodiments. Accordingly, components and/or operations described below should not be construed as limiting the scope of any of the claims.

FIG. 8A illustrates a set of source code 802. The set of source code 802 includes multiple different source code segments 803, 804, 805, 806, 807, and 808. Source code segments 803-808 may correspond to particular operations or sets of tasks.

A multi-thread implementation recommendation system analyzes the source code 802 to identify candidate source code segments for parallel multi-thread processing. The system may determine that source code segment A (