In computers, parallel computing is closely related to parallel processing (or concurrent computing). It is the form of computation in which concomitant (“in parallel”) use of multiple CPUs that is carried out simultaneously with shared-memory systems to solving a super computing computational problem. Parallelism is the process of large computations, which can be broken down into multiple processors that can process independently and whose results combined upon completion. Parallelism has long employed in high-performance super computing.
Parallel processing generally implemented in the broad spectrum of applications that need massive amounts of calculations. The primary goal of parallel computing is to increase the computational power available to your essential applications. Typically, This infrastructure is where the set of processors are present on a server, or separate servers are connected to each other to solve a computational problem.
In the earliest computer software, that executes a single instruction (having a single Central Processing Unit (CPU)) at a time that has written for serial computation. A Problem is broken down into multiple series of instructions, and that Instructions executed one after another. Only one of computational instruction complete at a time.
Main Reasons to use Parallel Computing is that:
1. Save time and money.
2. Solve larger problems.
3. Provide concurrency.
4. Multiple execution units
Types of parallel computing
Bit-level parallelism
In the Bit-level parallelism every task is running on the processor level and depends on processor word size (32-bit, 64-bit, etc.) and we need to divide the maximum size of instruction into multiple series of instructions in the tasks. For Example, if we want to do an operation on 16-bit numbers in the 8-bit processor, then we would require dividing the process into two 8 bit operations.
Instruction-level parallelism (ILP)
Instruction-level parallelism (ILP) is running on the hardware level (dynamic parallelism), and it includes how many instructions executed simultaneously in single CPU clock cycle.
Data Parallelism
The multiprocessor system can execute a single set of instructions (SIMD), data parallelism achieved when several processors simultaneously perform the same task on the separate section of the distributed data.
Task Parallelism
Task parallelism is the parallelism in which tasks are splitting up between the processors to perform at once.
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