Parallel Computation

    

Parallel computing is a form of computation in which many calculations are carried out simultaneously, operating on the principle that large problems can often be divided into smaller ones, which are then solved concurrently ("in parallel"). There are several different forms of parallel computing: bit-level, instruction level, data, and task parallelism. Parallelism has been employed for many years, mainly in high-performance computing, but interest in it has grown lately due to the physical constraints preventing frequency scaling.As power consumption (and consequently heat generation) by computers has become a concern in recent years, parallel computing has become the dominant paradigm in computer architecture, mainly in the form of multi-core processors. Parallel computers can be roughly classified according to the level at which the hardware supports parallelism, with multi-core and multi-processor computers having multiple processing elements within a single machine, while clusters, MPPs, and grids use multiple computers to work on the same task. Specialized parallel computer architectures are sometimes used alongside traditional processors, for accelerating specific tasks. Parallel computer programs are more difficult to write than sequential ones,because concurrency introduces several new classes of potential software bugs, of which race conditions are the most common.Communication and synchronization between the different subtasks are typically some of the greatest obstacles to getting good parallel program performance. The maximum possible speed-up of a single program as a result of parallelization is known as Amdahl's law.

Distributed processing is a phrase used to refer to a variety of computer systems that use more than one computer (or processor) to run an application. This includes parallel processing in which a single computer uses more than one CPU to execute programs. More often, however, distributed processing refers to local-area networks (LANs) designed so that a single program can run simultaneously at various sites. Most distributed processing systems contain sophisticated software that detects idle CPUs on the network and parcels out programs to utilize them. Another form of distributed processing involves distributed databases. This is databases in which the data is stored across two or more computer systems. The database system keeps track of where the data is so that the distributed nature of the database is not apparent to users.

Architectural Parallel Computer

von Neumann Architecture

• Named after the Hungarian mathematician John von Neumann who first authored the general requirements for an electronic computer in his 1945 papers.
• Since then, virtually all computers have followed this basic design, differing from earlier computers which were programmed through "hard wiring".

Comprised of four main components: Memory Control Unit Arithmetic Logic Unit Input/Output Read/write, random access memory is used to store both program instructions and data Program instructions are coded data which tell the computer to do something Data is simply information to be used by the program Control unit fetches instructions/data from memory, decodes the instructions and then sequentially coordinates operations to accomplish the programmed task. Aritmetic Unit performs basic arithmetic operations Input/Output is the interface to the human operator

Flynn's Classical Taxonomy

• There are different ways to classify parallel computers. 
• One of the more widely used classifications, in use since 1966, is called Flynn's Taxonomy.
• Flynn's taxonomy distinguishes multi-processor computer architectures according to how they can be classified along the two independent dimensions of Instruction Stream and Data Stream. Each of these dimensions can have only one of two possible states: Single or Multiple.
• The matrix below defines the 4 possible classifications according to Flynn:

Single Instruction, Single Data (SISD): A serial (non-parallel) computer Single Instruction: Only one instruction stream is being acted on by the CPU during any one clock cycle Single Data: Only one data stream is being used as input during any one clock cycle Deterministic execution This is the oldest type of computer Examples: older generation mainframes, minicomputers, workstations and single processor/core PCs.

Single Instruction, Multiple Data (SIMD): A type of parallel computer Single Instruction: All processing units execute the same instruction at any given clock cycle Multiple Data: Each processing unit can operate on a different data element Best suited for specialized problems characterized by a high degree of regularity, such as graphics/image processing. Synchronous (lockstep) and deterministic execution Two varieties: Processor Arrays and Vector Pipelines

Multiple Instruction, Single Data (MISD): A type of parallel computer Multiple Instruction: Each processing unit operates on the data independently via separate instruction streams. Single Data: A single data stream is fed into multiple processing units. Few (if any) actual examples of this class of parallel computer have ever existed. Some conceivable uses might be: multiple frequency filters operating on a single signal stream multiple cryptography algorithms attempting to crack a single coded message.

Multiple Instruction, Multiple Data (MIMD): A type of parallel computer Multiple Instruction: Every processor may be executing a different instruction stream Multiple Data: Every processor may be working with a different data stream Execution can be synchronous or asynchronous, deterministic or non-deterministic Currently, the most common type of parallel computer - most modern supercomputers fall into this category. Examples: most current supercomputers, networked parallel computer clusters and "grids", multi-processor SMP computers, multi-core PCs. Note: many MIMD architectures also include SIMD execution sub-components Threaded Programming For many years, maximum computer performance was limited largely by the speed of a single microprocessor at the heart of the computer. As the speed of individual processors started reaching their practical limits, however, chip makers switched to multicore designs, giving the computer the opportunity to perform multiple tasks simultaneously. And although OS X takes advantage of these cores whenever it can to perform system-related tasks, your own applications can also take advantage of them through threads. What Are Threads? Threads are a relatively lightweight way to implement multiple paths of execution inside of an application. At the system level, programs run side by side, with the system doling out execution time to each program based on its needs and the needs of other programs. Inside each program, however, exists one or more threads of execution, which can be used to perform different tasks simultaneously or in a nearly simultaneous manner. The system itself actually manages these threads of execution, scheduling them to run on the available cores and preemptively interrupting them as needed to allow other threads to run. From a technical standpoint, a thread is a combination of the kernel-level and application-level data structures needed to manage the execution of code. The kernel-level structures coordinate the dispatching of events to the thread and the preemptive scheduling of the thread on one of the available cores. The application-level structures include the call stack for storing function calls and the structures the application needs to manage and manipulate the thread’s attributes and state.  In a non-concurrent application, there is only one thread of execution. That thread starts and ends with your application’s main routine and branches one-by-one to different methods or functions to implement the application’s overall behavior. By contrast, an application that supports concurrency starts with one thread and adds more as needed to create additional execution paths. Each new path has its own custom start routine that runs independently of the code in the application’s main routine. Having multiple threads in an application provides two very important potential advantages: Multiple threads can improve an application’s perceived responsiveness. Multiple threads can improve an application’s real-time performance on multicore systems. Threading Terminology Before getting too far into discussions about threads and their supporting technologies, it is necessary to define some basic terminology. If you are familiar with UNIX systems, you may find that the term “task” is used differently by this document. On UNIX systems, the term “task” is used at times to refer to a running process. This document adopts the following terminology: The term thread is used to refer to a separate path of execution for code. The term process is used to refer to a running executable, which can encompass multiple threads. The term task is used to refer to the abstract concept of work that needs to be performed. In November 2006, NVIDIA introduced CUDA™, a general purpose parallel computing platform and programming model that leverages the parallel compute engine in NVIDIA GPUs to solve many complex computational problems in a more efficient way than on a CPU. CUDA comes with a software environment that allows developers to use C as a high-level programming language. GPU Computing Applications. CUDA is designed to support various languages and application programming interfaces. 1. A Scalable Programming Model The advent of multicore CPUs and manycore GPUs means that mainstream processor chips are now parallel systems. Furthermore, their parallelism continues to scale with Moore's law. The challenge is to develop application software that transparently scales its parallelism to leverage the increasing number of processor cores, much as 3D graphics applications transparently scale their parallelism to manycore GPUs with widely varying numbers of cores. The CUDA parallel programming model is designed to overcome this challenge while maintaining a low learning curve for programmers familiar with standard programming languages such as C. At its core are three key abstractions - a hierarchy of thread groups, shared memories, and barrier synchronization - that are simply exposed to the programmer as a minimal set of language extensions. These abstractions provide fine-grained data parallelism and thread parallelism, nested within coarse-grained data parallelism and task parallelism. They guide the programmer to partition the problem into coarse sub-problems that can be solved independently in parallel by blocks of threads, and each sub-problem into finer pieces that can be solved cooperatively in parallel by all threads within the block.  Note: A GPU is built around an array of Streaming Multiprocessors (SMs)  A multithreaded program is partitioned into blocks of threads that execute independently from each other, so that a GPU with more multiprocessors will automatically execute the program in less time than a GPU with fewer multiprocessors

Quantum Computation

Quantum computing is the area of study focused on developing computer technology based on the principles of quantum theory, which explains the nature and behavior of energy and matter on the quantum (atomic and subatomic) level. Development of a quantum computer, if practical, would mark a leap forward in computing capability far greater than that from the abacus to a modern day supercomputer, with performance gains in the billion-fold realm and beyond. The quantum computer, following the laws of quantum physics, would gain enormous processing power through the ability to be in multiple states, and to perform tasks using all possible permutations simultaneously. Current centers of research in quantum computing include MIT, IBM, Oxford University, and the Los Alamos National Laboratory.

Quantum entanglement is a quantum mechanical phenomenon in which the quantum states of two or more objects have to be described with reference to each other, even though the individual objects may be spatially separated. This leads to correlations between observable physical properties of the systems. For example, it is possible to prepare two particles in a single quantum state such that when one is observed to be spin-up, the other one will always be observed to be spin-down and vice versa, this despite the fact that it is impossible to predict, according to quantum mechanics, which set of measurements will be observed. As a result, measurements performed on one system seem to be instantaneously influencing other systems entangled with it. But quantum entanglement does not enable the transmission of classical information faster than the speed of light. Quantum entanglement has applications in the emerging technologies of quantum computing and quantum cryptography, and has been used to realize quantum teleportation experimentally.

A qubit is a quantum bit , the counterpart in quantum computing to the binary digit or bit of classical computing. Just as a bit is the basic unit of information in a classical computer, a qubit is the basic unit of information in a quantum computer .In a quantum computer, a number of elemental particles such as electrons or photons can be used (in practice, success has also been achieved with ions), with either their charge or polarization acting as a representation of 0 and/or 1. Each of these particles is known as a qubit; the nature and behavior of these particles (as expressed in quantum theory ) form the basis of quantum computing. The two most relevant aspects of quantum physics are the principles of superposition and entanglement .

A quantum gate or quantum logic gate is a rudimentary quantum circuit operating on a small number of qubits. They are the analogues for quantum computers to classical logic gates for conventional digital computers. Quantum logic gates are reversible, unlike many classical logic gates. Some universal classical logic gates, such as the Toffoli gate, provide reversibility and can be directly mapped onto quantum logic gates. Quantum logic gates are represented by unitary matrices. The most common quantum gates operate on spaces of one or two qubits. This means that as matrices, quantum gates can be described by 2 x 2 or 4 x 4 matrices with orthonormal rows.

Shor's algorithm is a quantum algorithm for factoring a number N in O((log N)3) time and O(log N) space, named after Peter Shor. The algorithm is significant because it implies that RSA, a popular public-key cryptography method, might be easily broken, given a sufficiently large quantum computer. RSA uses a public key N which is the product of two large prime numbers, One way to crack RSA encryption is by factoring N, but with classical algorithms, factoring becomes increasingly time-consuming as N grows large; more specifically. no classical algorithm is known that can factor in polynomial time. But Shor's algorithm can crack RSA in polynomial time.  Like many quantum computer algorithms, Shor's algorithm is probabilistic and It gives the correct answer with high probability, and the probability of failure can be decreased by repeating the algorithm. Shor's algorithm was discovered in 1994 by Peter Shor, but the classical part was known before. it is credited to G. L. Miller. Seven years later, in 2001. it was demonstrated by a group at IBM, which factored 15 into 3 and 5, using a quantum computer with 7 qubits.

Cloud Computing

Cloud computing is the new definition for grid computing technologies which is used in the mid to late 1990s.  cloud computing began to emerge at the end of 2007, is used to move the services to the daily use of the Internet, not stored on the local computer again. Cloud computing is a new trend in the field of distributed computing in which the various parties can develop applications and services based on SOA (Service Oriented Architecture) on the Internet. With Cloud Computing you'd only have to load one application. That application would allow users to log into a Web-based service which hosts all the programs the user would need for his or her job. Remote machines is owned by another company that would run everything, from e-mail to word processing to complex data analysis programs.

            With Cloud computing you can get some advantage, first you can Reduce spending on technology infrastructure by Maintain easy access to your information with minimal upfront spending. Pay as you go (weekly, quarterly or yearly), based on demand., then the Streamline processes for Get more work done in less time with less people. Then you can Reduce capital costs because There’s no need to spend big money on hardware, software or licensing fee. And the most important thing you can get with cloud computing is You have access anytime, anywhere, that making your life so much easier!

            Cloud Based Services is the general term given to a variety of services that are accessed via the Internet or a proprietary network.   Broadly divided into three categories: Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS) and Software-as-a-Service (SaaS), Cloud Based Services allow users to store data, access software and access services and platforms from almost any device that can access the cloud via a broadband connection. The use of cloud services has greatly increased over the past decade.

            The following are the characteristics of cloud computing

• ‘On-Demand, Self-Service’ implies a customer can order service via the web or some other method at any point in time, 24×7, which becomes immediately available for his or her use.
• ‘Broad network access’ implies widespread, heterogeneous network accessibility for thin, thick, mobile and other commonly used compute mediums.
• ‘Shared resource pooling’ connotes the aggregation of physical compute resources into a logical ‘pool’ that is dynamically allocated in a multi-tenancy capacity across broad application service requirements.
• ‘Rapid, bi-directional elasticity’ simply means additional capacity remains available and accessible on an ‘as needed’ basis, and is recovered back to the pool when no longer needed for alternative allocation.
• ‘Metered service’ as noted above means that all variables of resource consumption are tracked in capacity that users can be automatically billed for their consumption.

Perhaps the biggest concerns about cloud computing are security and privacy. The idea of handing over important data to another company worries some people. Corporate executives might hesitate to take advantage of a cloud computing system because they can't keep their company's information under lock and key. The counterargument to this position is that the companies offering cloud computing services live and die by their reputations. It benefits these companies to have reliable security measures in place. Otherwise, the service would lose all its clients. It's in their interest to employ the most advanced techniques to protect their clients' data. Privacy is another matter. If a client can log in from any location to access data and applications, it's possible the client's privacy could be compromised. Cloud computing companies will need to find ways to protect client privacy. One way is to use authentication techniques such as user names and passwords. Another is to employ an authorization format -- each user can access only the data and applications relevant to his or her job.

When talking about a cloud computing system, it's helpful to divide it into two sections: the front end and the back end. They connect to each other through a network, usually the Internet. The front end is the side the computer user, or client, sees. The back end is the "cloud" section of the system. The front end includes the client's computer (or computer network) and the application required to access the cloud computing system. Not all cloud computing systems have the same user interface. Services like Web-based e-mail programs leverage existing Web browsers like Internet Explorer or Firefox. Other systems have unique applications that provide network access to clients. On the back end of the system are the various computers, servers and data storage systems that create the "cloud" of computing services. In theory, a cloud computing system could include practically any computer program you can imagine, from data processing to video games. Usually, each application will have its own dedicated server.

Objek Terdistribusi dan CORBA

Middleware adalah objek terdistribusi yang dirancang untuk menyediakan model pemrograman berdasarkan prinsip-prinsip berorientasi objek.

Emmerich [ 2000 ] melihat objek terdistribusi seperti evolusi alami dari tiga kegiatan :

• Pada sistem terdistribusi , middleware sebelumnya didasarkan pada model client-server dan ada keinginan untuk abstraksi pemrograman yang lebih canggih .

• Dalam bahasa pemrograman , karya sebelumnya dalam bahasa berorientasi objek seperti Simula - 67 dan Smalltalk menyebabkan munculnya lebih utama dan sangat digunakan bahasa pemrograman seperti Java dan C + + ( bahasa yang digunakan secara ekstensif dalam sistem terdistribusi ) .

• Dalam rekayasa perangkat lunak , kemajuan yang signifikan dibuat dalam pengembangan metode desain berorientasi obyek , yang menyebabkan munculnya Unified Modelling Language ( UML ) sebagai notasi industri - standar untuk menentukan ( potensi terdistribusi ) pada sistem perangkat lunak berorientasi objek .

Dengan kata lain, melalui pendekatan berorientasi objek dalam sistem terdistribusi, pengembang tidak hanya disediakan dengan abstraksi pemrograman ( seperti pada bahasa seperti C + + dan Java  ) , tetapi juga dapat menggunakan object - oriented prinsip-prinsip desain , alat dan teknik ( termasuk UML ) dalam pengembangan software sistem terdistribusi . Ini merupakan langkah maju yang besar di daerah di mana , sebelumnya , teknik desain seperti itu tidak tersedia . 

Middleware pada objek terdistribusi menawarkan abstraksi pemrograman berbasis object oriented Contoh middleware objek terdistribusi adalah Java, RMI dan CORBA .

Java RMI dan CORBA berbagi banyak kesamaan ,namun ada satu perbedaan yang terpenting : yaitu penggunaan Java RMI dibatasi untuk pengembangan berbasis Java , sedangkan CORBA adalah solusi yang memungkinkan multi-bahasa berbasis objek untuk dapat ditulis dalam berbagai bahasa  .

CORBA

       Pada tahun 1989 object Management group (0MG) dibentuk melakukan standarisasi terhadap arsitektur aplikasi menggunakan obyek terdistribusi sehingga aplikasi yang dibuat oleh sebuah vendor dapat birenteraksi dengan vendor Iainnya dengan berbagai perarigkat jaringan dan sistem operasi. Standar yang dikePuarkan oleh 0MG disebut Common Object RequestBroker Architektur (CQRBA). Spesifikasi CORBA ini berisi sebuah spesifikasi infrastruktur yang disebut Object request Broker (ORB) yang memungkinkan aplikasi klien untuk dapat berkomunikasi dengan obyek secara remote, yang meliputi antarmuka program, protokol komunikasi don model obyek atau layanan yang memungkinkan untuk saling berkomunikasi dalam berbagai platform.

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CORBA menggunakan interface definition language (IDL) untuk menunjukkan interface atau antarmuka yang dapat digunakan oleh program atau obyek lain.

  Komponen CORBA yang terletak di sisi Server

1. Server Side ORB Interface
2. Static IDL Skeleton
3. Dynamic Skeleton Interface
4. Object Adapter
5. Server Side Implementation

Komponen CORBA pada sisi Client:
1.     Client Application
2.     Client IDL Stubs
3.     Dynamic Invocation Interface
4.     Interface Repository
5      Client Side ORB Interface

Komponen Object Request Broker (ORB)
          Inti dari CORBA adalah ORB, dimana ORB  bertanggungjawab untuk menjalankan semua mekanisme yang dibutuhkan, antara lain yaitu:

1.  menemukan implementasi obyek untuk memenuhi suatu request,
2.  menyiapkan implementasi obyek untuk menerima suatu request,
3.  melakukan komunkasi data untuk memenuhi suatu request.

          Sebuah permintaan (request) yang dikirimkan suatu client ke suatu object implementation akan melewati ORB. Dengan ORB, yang terdiri dari interface, suatu entitas dapat berkomunikasi dengan object implementation tanpa adanya batasan platform, topologi jaringan, bahasa pemrograman, dan letak obyek

Client

Secara umum, client adalah suatu program/proses yang melakukan request pada suatu objek. Terdapat pula client relative, yaitu suatu objek yang menjadi client dari objek lainnya.Client suatu objek harus mengakses OR (Object Reference) suatau objek tertentu untuk melakukan operasi pada suatu objek. Client hanya mengetahui struktur logika suatu objek melalui interface yang dimiliki objek tersebut dan behaviour yang dimiliki objek tersebut saat dipanggil.Secara umum, client mengakses objek dan ORB melalui language mapping.Client dapat bersifat portable dan seharusnya dapat berjalan tanpa harus mengubah kode pada ORB yang mendukung language mapping berbeda dengan objek instance yangmengimplementasikan interface berbeda.

Untuk membuat suatu request, client dapat menggunakan :

1. DII (Dynamic Invocation Interface) yaitu suatu interface yang tidak tergantung pada

inteface objek yang dituju

2. IDL Stub, yang tergantung pada interface objek yang dituju.

(cth: Untuk fungsi-fungsi tertentu, client dapat berinteraksi secara langsung dengan ORB)

Object Implementation (OI)

Suatu Object Implementation (OI) menyediakan semantik dari objek, yangumumnya dilakukan dengan mendefiniskan data untuk object instance dan kode untuk method-method objek tersebut. Seringkali kita menggunakan objek lain atau menggunakan software tambahan untuk mengimplementasikan sifat suatu objek.

Object Implementation (OI) menerima suatu request melalui

1. IDL Skeleton

2. Dynamic Skeleton Interface(DSI)

Object Implementation (OI) dapat memanggil Object Adapter (OA) dan ORB pada saat memproses sebuah request.

Interface

Inteface suatu objek dapat didefinisikan dengan cara statis, yaitu menggunakan IDL (Interface Definition Languange). IDL mendefiniskan tipe suatu objek berdasarkan operasi-operasi (yang mungkin dijalankan pada objek tersebut) dan parameter operasi tersebut.

Interface dapat pula ditambahkan ke dalam suatu IRS (Interface Repository Service) yang menggambarkan komponen-komponen dari interface suatu objek. Client dapat mengakses komponen-komponen ini saat runtime.Client meminta suatu request dengan melakukan akses ke OR (Object Reference)suatu objek yang dituju dan mengetahui tipe dari objek dan operasi-operasi yang dapat dilakukan pada objek tersebut. Client menginisialisasi request dengan memanggil rutinrutin suatu stub yang sesuai dengan objek atau membangun request secara dinamik.

Interface dinamik dan interface stub harus memeiliki semantic request yabng msa dalam pemanggilan suatu request.ORB mencari implementation code yang dituju, mengirimkan parameterparameter dan mentransfer kontrol pada Object Implementation memalui IDL Sekeleton atau Dynamic Skeleton. Secara spesifik, skeleton berupa interface dan OA (ObjectAdapter). Dalam mengolah suatu request, Object Implementation memberikan service pada ORB melalui OA (Object Adapter). Saat suatu request selesai dijalankan, kontrol dan nilai keluaran dikembalikan ke client. OI dapat memilih OA yang akan digunakan.Keputusan pemilihan OA ditentukan oleh jenis service yang dibutuhkan oleh OI tersebut.Infomasi tentang OI diberikan pada saat instalasi dan disimpan dalam IR(Implementation Repository) yang digunakan selama pengiriman hasil request.Dalam arsitekturnya, ORB tidak perlu dimplementasikan dalam sebuah komponen tunggal namun, ORB didefinisikan menggunakan interface-interface yang dimilikinya. Interface-interface tersebut dikelompokan menjadi:

1. operasi yang sama untuk semua implementasi ORB

2. operasi khusus untuk tipe objek tertentu

3. operasi khusus untuk style OI tertentu

Object Reference (OR)

Object Reference (OR) merupakan informasi yang dibutuhkan untuk menentukan sebuah objek dalam ORB. Client dan Object Implementation (OI) memiliki bagaian yang tertutup dari OR dengan language mapping, yang kemudian disekat dari representasi aktualnya. Dua implementasi ORB dapat memiliki representasi OR yang berbeda.Representasi OR pada sisi client hanya valid selama masa hidup client tersebut.Semua ORB harus menyediakan language mapping yang sama untuk sebuah OR(umumnya disebut objek) untuk sebuah bahasa pemrograman tertentu. Hal ini memungkinkan sebuah program ditulis dalam bahasa apapun untuk mengakses OR secara independen terhadap ORB terntentu.

Interface Definition Language (IDL)

Objek-objek CORBA dispesifikasikan menggunakan interface, yang merupakan penghubung anatara client dan server. Interface Definition Language (IDL) digunakan untuk mendefinisikan interface tersebut.IDL menentukan tipe-tipe suatu objek dengan mendefinisikan interface-interface objek tersebut. Sebuah interface terdiri dari kumpulan operasi dan parameter operasi tersbut. IDL hanya mendeskripsikan interface, tidak mengimplementasikannya. Meskipun sintaks yang dimiliki oleh IDL menyerupai sintaks bahasa pemrograman C++ dan Java., perlu diingat, IDL bukan bahasa pemrograman.Melalui IDL, Object Implementation (OI) akan memberitahu client yang akan mengakses operasi apa saja dan method apa saja yang harus dipanggil client tersebut.Dari definisi IDL, objek-objek CORBA dipetakan ke bahasa pemrograman –C,C++, Java, dan lain-lain—yang memiliki IDL mapping.

Bahasa Pemrograman yang berbeda dapat mengakses objek-objek CORBA dalam bebagai cara yang berbeda. Pemetaan dari IDL ke bahasa pemrograman tertentu harus sama untuk semua implementasi ORB. Language Mapping ini menyertakan definisi tipe data untuk bahasa  pemrograman terntentu dan procedure interface untuk mengakses objek melalui ORB. Ini meliputi:

1. Struktur dari client stub interface (tidak dibutuhkan untuk bahasa OOP)

2. Dynamic Invocation Interface

3. Implementation Skeleton

4. Object Adapters

5. Direct ORB Interface

Language Mapping juga mendefinisikan interaksi antara pemanggilan objek dan langkah kontrol pada client dan implementasi. Pemetaan yang paling umum menyediakan syncrhonous call, dimana rutin mengembalikan nilai pada saat operasi suatu objek selesai dilakukan. Pemetaan tambahan memungkinkan sebuah call diisisiasi dan kontrol dikembalikan kepada program.

 Dynamic Invocation/Skeleton Interface

IDL interface yang digunakan oleh sebuah client ditentukan pada saat client dikompilasi. Hal tesebut mengakibatkan seorang programmer hanya dapat menggunakan server-server yang terdiri dari objek-objek yang mengimplementasikan interfaceinterface tersebut.

Bila suatu aplikasi membutuhkan interface-interface yang tak didefiniskan saat kompilasi, maka diperlukan DII (Dynamic Invocation Interface) atau pun DSI (Dynamic Skeleton Interface).DII memungkinkan suatu aplikasi/client memanggil operasi-operasi dari sembarang interface. DSI menyediakan suatu cara untuk mengirim request dari sebuah ORB ke sebuah Object Implementation (OI) tanpa harus mengetahui tipe dari objek pada saat kompilasi.

Dynamic Invocation Interface (DII)

CORBA mendukung DII dan SII. Operasi invocation dapat dilakukan menggunakan static interface ataupun dynamic interface. Static Invocation Interface (SII) ditentukan pad saat kompilasi dan dihubungkan dengan client mengunakan stub.Sedangkan Dynamic Invocation Interface (DII) memungkinkan apliaksi di sisi client untuk menggunakan server object tanpa perlu mengetahui tipe obek-objek tersebut saat kompilasi.

DII memungkinkan client untuk mendapatkan sebuah instance dari objek CORBA dan membuat invocation pada objek tersebut dengan menciptakan request yang sifatnya dinamis. DII  menggunakan Interface Repository (IR) untuk memvalidasi dan mengambil identifier operasi pada suatu request yang dibuat.Client menggunakan Interface Repository (IR) untuk mempelajari tentang interface-objek yang tidak diketahui dan client menggunakan DII untuk memanggil methods suatu objek.

Empat tahap yang diperlukan saat penggunaan Dynamic Invocation Interface (DII):

1. Mengidentifikasikan target objek yang akan dipanggil

2. Mendapatkan target interface dari objek tersebut

3. Membangun invocation

4. Mengirim request untuk mendapatkan respon

Aplikasi-aplikasi client yang menggunakan Dynamic Invocation Interface (DII) tidak lebih efisien dari yang menggunakan SII, tapi ada dua keuntungan menggunakan DII,yaitu:

-Aplikasi client dapat melakukan permintaan kepada setiap operasi meskipun tersebut tidak diketahui pada saat aplikasi dikompilasi.

-Apliaksi client tidak harus dikompilasi ulang untuk mengakses OI yang diaktivasi ulang.

 Dynamic Skeleton Interface (DSI)

Dynamic Skeleton Interface (DSI) menyerupai DII, namun tereletak di sisi server. DSI memungkinkan server ditulis tanpa harus mempunyai skeleton-skeleton atau informasi tentang waktu kompilasi, dan untuk objek mana server ini diimlementasikan. Fungsi utama Dynamic Skeleton Interface (DSI) adalah mendukung implementasi gateway antara ORB yang memiliki protocol komunikasi berbeda.

Object Adapter (OA)

Object Adapter (OA) merupakan cara utama bagi sebuah Object Implemetation (OI) untuk mengakses service yang disediakan oleh ORB. Tugas utamanya adalah melakukan masking (menutupi) perbedaan dalam implementasi objek untuk memperoleh portability yang lebih tinggi.

ORB Interface

ORB Interface Merupakan interface yang berhubungan langsung dengan ORB yang sama untuk semua ORB dan tidak tergantung pada interface suatu objek atau Objek Adapter (OA). Karena banyak fungsionalitas ORB yang disediakan melalui OA, stub,skeleton, maupun dynamic invocation; maka ada sedikit operasi yang umum bagi semua objek.

 Inteface Repository (IR)

Interface Repository (IR) merupakan online database yang berisi tentang meta informasi tentang tipe dari objek ORB. Meta ionformasi yang disimpan meliputi informasi tentang modul, interface, operasi, atribut, dan eksepsi dari objek.Interface Repository (IR) menyediakan cara lain untuk menentukan interface ke suatu objek. Interface ini dapat ditambahakan ke layanan IR. Dengan menggunakan IR,sebuah client akan mencari objek yang tidak diketahui pada saat kompilasi, menemukan informasi tentang interface objek tersebut dan implementasi suatu aktivasi dan deaktivasi.

ORB biasa menggunakan IR untuk:

1. menyediakan interoperability antar implementasi ORB yang berbeda

2. menyediakan type checking dari signature sebuah request yang melalui SII dan DII

3. Mengecek kebenaran grafik inheritance

4. Mengelola instalasi dan distribusi interface definition alam sebuah jaringan

5. Mengeizinkan designer apliaksi untuk memodifikasi interface definition

6. Mengizinkan language compiler untuk mengcompile stub dan skeleton dari IR

bahkan langsung dari file IDL.

Implementation Repository

Implementation Repository terdiri dari informasi yang memperbolehkan ORB untuk mencari dan mengaktivasi implementasi suatu objek. Meskipun untuk suatu ORB atau lingkungan operasi, Implementation Repository merupakan tempat yang konvensional untuk menyimpan suatu informasi.

 Internet Inter-ORB Protocol (IIOP)

CORBA mendefinisikan IIOP (Internet Inter-ORB Protocol) untuk mengatur bagaimana objek berkomunikasi melalui jaringan. IIOP merupakan open protocol yang berjalan diatas TCP/IP.