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Parallel computing
Parallel computing involves solving computational problems simultaneously using multiple processors. It breaks problems into discrete parts that can be solved concurrently rather than sequentially. Parallel computing provides benefits like reduced time/costs to solve large problems and ability to model complex real-world phenomena. Common forms include bit-level, instruction-level, data, and task parallelism. Parallel resources can include multiple cores/processors in a single computer or networks of computers.
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Parallel computing
- 1. Parallel Computing Engr .Saddam Zardari FA14-MS-0036 Muhammad Ali Jinnah University Karachi, Sindh , Pakistan
- 2. Serial Computing • Traditionally,software has been written for serial computation: A problem is broken into a discrete series of instructions • Instructions are executed sequentially one after another • Executed on a single processor • Only one instruction may execute at any moment in time
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- 4. Parallel Computing parallel computingis the simultaneous use of multiple compute resources to solve a computational problem. • A problem is broken into discrete parts that can be solved concurrently • Each part is further broken down to a series of instructions • Instructions from each part execute simultaneously on different processors • An overall control/coordination mechanism is employed
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- 6. Resources Type • Asingle computer with multiple processors/cores • An arbitrary number of such computers connected by a network
- 7. • Virtually allstand-alone computers today are parallel from a hardware perspective, Multiple functional units (L1 cache, L2 cache, branch, prefetch, decode, floating-point, graphics processing (GPU), integer, etc.) • Multiple execution units/cores • Multiple hardware threads.
- 8. Example IBM BG/Q ComputeChip with 18 cores (PU) and 16 L2 Cache units
- 9. Networks connect multiplestand-alone computers (nodes) to make larger parallel computer clusters
- 10. Why Use ParallelComputing? Compared to serial computing, parallel computing is much better suited for modeling, simulating and understanding complex, real world phenomena.
- 11. Modeling These typeof problems by Parallel Computing
- 12. Why Parallel Computing? SAVETIME AND/OR MONEY • In theory, throwing more resources at a task will shorten its time to completion, with potential cost savings. • Parallel computers can be built from cheap, commodity components. SOLVE LARGER / MORE COMPLEX PROBLEMS • Many problems are so large and/or complex that it is impractical or impossible to solve them on a single computer, especially given limited computer memory.
- 13. Why Parallel Computing? PROVIDECONCURRENCY • A single compute resource can only do one thing at a time. Multiple compute resources can do many things simultaneously.
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- 15. Forms of parallelcomputing • Bit-level form of parallel computing based on increasing processor word size • Instruction level Instruction-level parallelism (ILP) is a measure of how many of the operations in a computer program can be performed simultaneously • Data parallelism Data parallelism is a form of parallelization of computing across multiple processors in parallel computing environments. Data parallelism focuses on distributing the data across different parallel computing nodes. • Task parallelism Also known as function parallelism and control parallelism, is a form of parallelization of computer code across multiple processors in parallel computing environments. Task parallelism focuses on distributing execution processes (threads) across different parallel computing nodes
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