Parallel programming model, language and compiler in ACA.
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This document discusses parallel programming models and their key aspects. It describes five common parallel programming models: shared-variable, message-passing, data parallel, object-oriented, and functional/logic. The main types of inter-process communication are shared variables and message passing. Synchronous and asynchronous message passing are introduced. The document also covers language features that enable parallel programming such as optimization, availability, synchronization/communication, control of parallelism, data parallelism, and process management.
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- 2. A programming model is a collection of program abstraction providing a programmer a simplified and transparent view of computer H/W and S/W. Parallel programming model is designed for vector computers. Fundamental issues in parallel programming. Creation, suspension, reactivation, termination.
- 3. Five model are designed that exploits parallelism-: Shared-variable model. Message-passing model. Data parallel model. Object oriented model. Functional and logic model.
- 4. In shared variable model parallelism depends on how IPC is implemented. IPC implemented in parallel programming by two ways. IPC using shared variable. IPC using message passing.
- 5. IPC with shared variable IPC with message passing
- 6. Critical section. Memory consistency. Atomicity with memory operation. Fast synchronization. Shared data structure.
- 7. Two process communicate with each other by passing message through a network. Delay caused by message passing is much longer than shared variable model in a same memory. Two message passing approach are introduced here.
- 8. Synchronous message passing-: Its synchronizes the sender and receiver process with time and space just like telephone call. No shared memory. No need of mutual exclusion. No buffer are used in communication channel. It can be blocked by channel being busy.
- 9. Asynchronous message passing-: Does not need to synchronize the sender and receiver in time and space. Non blocking can be achieved. Buffer are used to hold the message along the path of connecting channel. Message passing programming is gradually changing, once the virtual memory from all nodes are combined.
- 10. It require the use of pre-distributed data set. Interconnected data structure are also needed to facilitate data exchange operation. It emphasizes local computation and data routing operation such as permutation, replication, reduction and parallel prefix. It can be implemented on either SIMD or SPMD multicomputer, depending on the grain size of program.
- 11. Object are created and manipulated dynamically. Processing is performed using object. Concurrent programming model are built up from low level object such as processes, queue and semaphore. C-OOP achieve parallelism using three methods.
- 12. Pipeline concurrency. Divide and conquer concurrency. Co-operating problem solving.
- 13. Two language-oriented programming for parallel processing are purposed. Functional programming model such as LISP, SISAL, Strand 88. Logic programming model as prolog. Based on predicate logic, logic programming is suitable for solving large database queries.
- 14. Language feature for parallel programming into six categories according to functionality. Optimization features Used for program restructuring and compilation directives. Sequentially coded program into parallel code. Automated parallelization. Semi-automated parallelization.
- 15. Availability feature Its use to enhance the user- friendliness. Make language portable to large class of parallel computers. Scalability. Compatibility. Portability.
- 16. Synchronization/ communication feature Shared variable for IPC. Single assignment language. Send/receive for message passing. Logical shared memory such as the row space in Linda. Remote procedure call. Data flow languages such as id. .
- 17. Control of parallelism Coarse, medium or fine grain. Explicit versus implicit parallelism. Loop parallelism in iteration. Shared task queue. Divide and conquer paradigm. Shared abstract data type.
- 18. Data parallelism feature It specified how data are accessed and distributed Runtime automatic decomposition. Mapping specification. Virtual processor support. Direct access to shared data.
- 19. Process management features These feature are needed to support the efficient creation of parallel processes. Implementation of multithreading or multitasking. Dynamic process creation at runtime. Automatic load balancing. Light weight processes.
- 20. Special language construct and data array expression for exploiting parallelism in program. First is FORTRAN 90 array notation. Parallel flow control is achieve using do across and do all type of keyword which is use in the FORTRAN 90. Same we also use FORK and JOIN method.
- 21. The role of compiler to remove the burden of program optimization and code generation. A parallelizing compiler consist of the three major phases. Flow analysis. Optimization. Code generation.
- 22. “Compilation phases in parallel code generation”
- 23. THANK YOU