Research Scope in Parallel Computing And Parallel Programming
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Parallel computing involves performing multiple calculations simultaneously using large problems that can be divided into smaller sub-problems. There are different forms of parallelism at the bit-level, instruction-level, data-level, and task-level. Parallel programs can be challenging to write due to issues with communication and synchronization between subtasks. Software solutions for parallel programming include shared memory languages, distributed memory languages using message passing, and automatic parallelization tools.
Research Scope in Parallel Computing And Parallel Programming
- 1. Research Scope in Parallel Programming and Parallel computing By Shitalkumar R. Sukhdeve (www.Balloys.com) 1By Shitalkumar R . Sukhdeve
- 2. Parallel computing • 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 at the same time. 2By Shitalkumar R Sukhdeve
- 3. Parallel computing • Different forms of parallel computing: 1. bit-level, 2. instruction level, 3. data, and 4. task parallelism. By Shitalkumar R Sukhdeve 3
- 4. Parallel computing • Parallel computing is closely related to concurrent computing . • But the two are distinct: • it is possible to have parallelism without concurrency (such as bit-level parallelism), and concurrency without parallelism (such as multitasking by time-sharing on a single-core CPU). By Shitalkumar R Sukhdeve 4
- 5. Parallel computing • 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. By Shitalkumar R Sukhdeve 5
- 6. Parallel computing and Implementation issues • In some cases parallelism is transparent to the programmer, such as in bit-level or instruction-level parallelism, • but explicitly parallel algorithms, particularly those that use concurrency, 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. By Shitalkumar R Sukhdeve 6
- 7. Software Solutions 1. Programming parallel computers. • Concurrent programming languages, • libraries, • APIs, and • parallel programming models (such as Algorithmic Skeletons) . By Shitalkumar R Sukhdeve 7
- 8. Software Solutions Classification • Above solutions can be divided into classes based on the assumptions they make about the underlying memory architecture— 1. shared memory, 2. distributed memory, or 3. shared distributed memory. By Shitalkumar R Sukhdeve 8
- 9. Software Solutions Classification • Shared memory programming languages communicate by manipulating shared memory variables. • Distributed memory uses message passing. POSIX Threads and OpenMP are two of most widely used shared memory APIs, whereas Message Passing Interface (MPI) is the most widely used message-passing system API. By Shitalkumar R Sukhdeve 9
- 10. Software Solutions Classification • The ”future concept” is also useful while implementing parallel programming. • In Future concept ,one part of a program promises to deliver a required datum to another part of a program at some future time. By Shitalkumar R Sukhdeve 10
- 11. Software Solutions Classification 2. Automatic parallelization • Automatic parallelization of a sequential program by a compiler. • Despite decades of work by compiler researchers, automatic parallelization has had only limited success. • Mainstream parallel programming languages remain either explicitly parallel or (at best) partially implicit, in which a programmer gives the compiler directives for parallelization. By Shitalkumar R Sukhdeve 11
- 12. Software Solutions Classification • A few fully implicit parallel programming languages exist— 1. SISAL, 2. Parallel Haskell, 3. System C (for FPGAs), 4. Mitrion-C, 5. VHDL, and 6. Verilog. By Shitalkumar R Sukhdeve 12
- 13. Software Solutions Classification 3. Application checkpointing • Is a technique whereby the computer system takes a "snapshot" of the application — a record of all current resource allocations and variable states, akin to a core dump; this information can be used to restore the program if the computer should fail. • Application checkpointing means that the program has to restart from only its last checkpoint rather than the beginning. By Shitalkumar R Sukhdeve 13
- 14. Algorithmic Methods As parallel computers become larger and faster, it becomes feasible to solve problems that previously took too long to run. Common types of problems found in parallel computing applications are:[ • Dense linear algebra • Sparse linear algebra • Spectral methods (such as Cooley–Tukey fast Fourier transform) • n-body problems (such as Barnes–Hut simulation) • Structured grid problems (such as Lattice Boltzmann methods) • Unstructured grid problems (such as found in finite element analysis) By Shitalkumar R Sukhdeve 14
- 15. Algorithmic Methods • Monte Carlo simulation • Combinational logic (such as brute-force cryptographic techniques) • Graph traversal (such as sorting algorithms) • Dynamic programming • Branch and bound methods • Graphical models (such as detecting hidden Markov models and constructing Bayesian networks) • Finite-state machine simulation By Shitalkumar R Sukhdeve 15
- 16. Conclusion • As based on the above literature survey, parallel computing and parallel programming has immense scope for research in the area of implementation and performance analysis. By Shitalkumar R Sukhdeve 16
- 18. Thank you By Shitalkumar R Sukhdeve 18