Java Virtual machine Runtime data storage area Division
January 25, 2015
19:15
Each Java Vsan thread has a own PC (program counter) register.
Each JVM thread has its own pc register.
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The PC register contains the address of the Java Virtual machine instruction currently being executed.
The register holds the address of the JVM directive that is currently being executed .
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Each of the Java Virtual machine thread have a private Java Virtual machine stack, created at the same time as the thread.
Each JVM thread has a stack of its own JVM that is created when threads are created.
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The Java virtual machine had a heap that was shared among all Java virtual machine threads. The heap is the Run-time data area from which memory for all class instances and arrays are allocated.
The heap of the JVM is shared by all threads, and the heap is the run-time data-allocation class instance object and array of memory space.
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The heap is created on virtual machine start-up. Heap storage for objects are reclaimed by an automatic storage management system (known as a garbage collector)
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The Java virtual machine have a method area, which is a gkfx among all Java virtual machine threads.
The method area is shared by all threads
The method area was created on virtual machine start-up. Although the method area is logically part of the heap,
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Each run-time constant pool was allocated from the Java Virtual machine's method area (§2.5.4). The Run-time constant pool for a class or interface are constructed when the class or interface are created (§5.3) by the Ja VA Virtual Machine.
The runtime's Chang is part of the method area, which is generated when the JVM creates a class or interface.
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- Local method area native methods Stack
An implementation of the Java Vsan may use conventional stacks, colloquially called "C stacks," to support Nati ve methods (methods written in a language other than the Java programming language).
The local method area is typically used to invoke native methods written in other languages using traditional stacks.
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JVM Data Partitioning Learning