Java virtual machineA Java virtual machine (JVM) is a virtual machine that enables a computer to run Java programs as well as programs written in other languages that are also compiled to Java bytecode. The JVM is detailed by a specification that formally describes what is required in a JVM implementation. Having a specification ensures interoperability of Java programs across different implementations so that program authors using the Java Development Kit (JDK) need not worry about idiosyncrasies of the underlying hardware platform.
Instructions per cycleIn computer architecture, instructions per cycle (IPC), commonly called instructions per clock is one aspect of a processor's performance: the average number of instructions executed for each clock cycle. It is the multiplicative inverse of cycles per instruction. While early generations of CPUs carried out all the steps to execute an instruction sequentially, modern CPUs can do many things in parallel.
Java bytecodeIn computing, Java bytecode is the bytecode-structured instruction set of the Java virtual machine (JVM), a virtual machine that enables a computer to run programs written in the Java programming language and several other programming languages, see List of JVM languages. A Java programmer does not need to be aware of or understand Java bytecode at all. However, as suggested in the IBM developerWorks journal, "Understanding bytecode and what bytecode is likely to be generated by a Java compiler helps the Java programmer in the same way that knowledge of assembly helps the C or C++ programmer.
Hardware virtualizationHardware virtualization is the virtualization of computers as complete hardware platforms, certain logical abstractions of their componentry, or only the functionality required to run various operating systems. Virtualization hides the physical characteristics of a computing platform from the users, presenting instead an abstract computing platform. At its origins, the software that controlled virtualization was called a "control program", but the terms "hypervisor" or "virtual machine monitor" became preferred over time.
Empty setIn mathematics, the empty set is the unique set having no elements; its size or cardinality (count of elements in a set) is zero. Some axiomatic set theories ensure that the empty set exists by including an axiom of empty set, while in other theories, its existence can be deduced. Many possible properties of sets are vacuously true for the empty set. Any set other than the empty set is called non-empty. In some textbooks and popularizations, the empty set is referred to as the "null set".
Intersection (set theory)In set theory, the intersection of two sets and denoted by is the set containing all elements of that also belong to or equivalently, all elements of that also belong to Intersection is written using the symbol "" between the terms; that is, in infix notation. For example: The intersection of more than two sets (generalized intersection) can be written as: which is similar to capital-sigma notation. For an explanation of the symbols used in this article, refer to the table of mathematical symbols.
Granularity (parallel computing)In parallel computing, granularity (or grain size) of a task is a measure of the amount of work (or computation) which is performed by that task. Another definition of granularity takes into account the communication overhead between multiple processors or processing elements. It defines granularity as the ratio of computation time to communication time, wherein computation time is the time required to perform the computation of a task and communication time is the time required to exchange data between processors.
Parallel computingParallel computing is a type of computation in which many calculations or processes are carried out simultaneously. Large problems can often be divided into smaller ones, which can then be solved at the same time. There are several different forms of parallel computing: bit-level, instruction-level, data, and task parallelism. Parallelism has long been employed in high-performance computing, but has gained broader interest due to the physical constraints preventing frequency scaling.
Explicit parallelismIn computer programming, explicit parallelism is the representation of concurrent computations by means of primitives in the form of special-purpose directives or function calls. Most parallel primitives are related to process synchronization, communication or task partitioning. As they seldom contribute to actually carry out the intended computation of the program, their computational cost is often considered as parallelization overhead. The advantage of explicit parallel programming is the absolute programmer control over the parallel execution.
Thread (computing)In computer science, a thread of execution is the smallest sequence of programmed instructions that can be managed independently by a scheduler, which is typically a part of the operating system. The implementation of threads and processes differs between operating systems. In Modern Operating Systems, Tanenbaum shows that many distinct models of process organization are possible. In many cases, a thread is a component of a process.
Green threadIn computer programming, a green thread (virtual thread) is a thread that is scheduled by a runtime library or virtual machine (VM) instead of natively by the underlying operating system (OS). Green threads emulate multithreaded environments without relying on any native OS abilities, and they are managed in user space instead of kernel space, enabling them to work in environments that do not have native thread support. Green threads refers to the name of the original thread library for the programming language Java (that was released in version 1.
Set theorySet theory is the branch of mathematical logic that studies sets, which can be informally described as collections of objects. Although objects of any kind can be collected into a set, set theory, as a branch of mathematics, is mostly concerned with those that are relevant to mathematics as a whole. The modern study of set theory was initiated by the German mathematicians Richard Dedekind and Georg Cantor in the 1870s. In particular, Georg Cantor is commonly considered the founder of set theory.
