Difference Between Implicit Parallelism and Explicit Parallelism in Parallel Computing

Implicit Parallelism is defined as a parallelism technique where parallelism is automatically exploited by the compiler or interpreter. The objective of implicit parallelism is the parallel execution of code in the runtime environment. In implicit parallelism, parallelism is being carried out without explicitly mentioning how the computations are parallelized. The compiler assigns the resources to target machines for performing parallel operations. Implicit parallelism requires less programming effort and has applications in shared memory multiprocessors. 

Examples of Implicit Parallelism 

• Pipelining: Pipelining is an example of implicit parallelism that is being used by the processors to execute multiple instructions simultaneously. In the process of pipelining each stage of execution is performed in parallel with other instructions. 
• Multithreading: Multithreading is defined as a process of executing multiple threads in a single process. Every process has its own instructions and can be executed independently of the other threads. Multithreading is commonly used in parallel processing applications. 
• Vectorization: Vectorization is the technique used for optimizing the code in order to execute it on the processors that supports Single Instruction and Multiple Data Instructions. It allows performing the same operation on multiple data elements at the same time. 
• Out-of-Order: Out-of-Order is a technique being used by the processors for the execution of instructions that maximizes parallelism. In this technique, instructions are executed as soon as the required operands are available instead of the order in which the operands appear in the program.  

Implicit Parallelism Programming Languages 

• Axum: Axum is one of the domain-specific and concurrent languages that is based on the Actor model.  
• BMDFM: BMDFM stands for Binary Modular Dataflow Machine. It allows an application to run on Shared Memory symmetric multiprocessing. 
• HPF: HPF stands for High-Performance Fortran.  HPF is an extension of HPF 90 with constraints that support parallel computing. 
• Id: Id stands for Irvain Dataflow. It is a general-purpose programming language that has functional programming and non-strict semantics. 
• LabView: LabView stands for Laboratory Virtual Instrument Engineering Workbench. It is a system design platform and a development environment for visual programming languages.
• SaC: SaC stands for Single Assignment C. It is a purely functional language that is focused on numerical applications. 
• SISAL: SISAL is a general purpose Single assignment programming language that has features of implicit parallelism, strict semantics, and an efficient array handling mechanism. 

What is Explicit Parallelism?

Explicit Parallelism is defined as a parallelism technique where concurrent operations are executed parallel with the help of primitives that are known as special purpose directives or function calls. In explicit parallelism. In Explicit parallelism, the compiler does not detect the parallelism for the allocation of resources. The programming efforts required in explicit parallelism are more as compared to implicit parallelism as the execution of concurrent tasks takes place manually depending upon the source code developed by the programmer. The resources are being utilized more efficiently in explicit parallelism and have applications in loosely coupled multiprocessors. 

Examples of Explicit Parallelism 

• Parallel loops: Parallel loops are defined as a construct that allows a loop to be executed in parallel. The sequence and condition of the loops are specified according to which they are executed. 
• Message passing: Message passing is a technique that is used to enable communication that takes place between processes and the threads that are running on different processors. 
• Data parallelism: Data parallelism is defined as a technique that is used to divide the present large datasets into smaller subsets. These divided subsets can then be processed in parallel. 

Explicit Parallelism Programming Languages 

• Occam: Occam is a concurrent programming language that builds on Communicating Sequential Processes algebra. 
• Erlang: Erlang is a general-purpose, high-level, and concurrent programming language and is one of the garbage-collected runtime systems. It is designed for fault-tolerant, soft- real-time, and distributed systems.  
• Parallel Virtual Machine: Parallel Virtual Machine is defined as a software tool used for parallel networking of computers. 
• Ada Programming Language: Ada is an imperative, statistically typed, structured high-level programming language. It is extended from Pascal and other programming languages. It provides support for explicit concurrency, tasks and synchronous message passing, Pascal, and strong typing. 
• Java Programming Language: Java is a high-level, object-oriented, class-based programming language. It allows the programmers to write the code once and execute it later anywhere. resource

Implicit Parallelism vs Explicit Parallelism 

Parameter	Implicit Parallelism	Explicit Parallelism
Definition	Implicit Parallelism is defined as characteristics of parallel programming that automatically allow the compiler or interpreter to exploit the parallelism.	Explicit Parallelism is defined as characteristics of parallel programming that execute the concurrent computations with the help of primitives that are in the form of special purpose directives.
Programming Languages used	Implicit Parallelism makes use of conventional programming languages such as C, C++, and Fortran for writing the source code.	Explicit Parallelism requires more programming efforts and makes use of programming languages such as C, C++, Fortran, and Pascal.
Compilation of source code	In Implicit Parallelism, the source program is coded sequentially and then translated into parallel object code by a parallelizing compiler.	In Explicit parallelism, parallelism is explicitly specified in the source code itself.
Resource Allocation	In Implicit Parallelism, parallelism is detected by the compiler and then assigns the resources to the target machine code.	In explicit parallelism, as parallelism is specified explicitly, there is no need for the compiler to detect parallelism, and resources are allocated explicitly.
Programming efforts	Implicit parallelism requires fewer programming efforts by the programmers as compared to explicit parallelism.	Explicit parallelism requires more programming efforts by the programmers as compared to implicit parallelism.
Resource utilization	In Implicit parallelism, resource utilization is less efficient because resource allocation is done by the compiler according to the need.	In Explicit parallelism, resource utilization is more efficient because resources are allocated explicitly and make used of resources more efficiently.
Scalability	Implicit parallelism is less scalable due to system control.	Explicit parallelism is more scalable as it has programmer control.
Applications	Implicit Parallelism is used in shared memory multiprocessors.	Explicit parallelism is used in loosely coupled multiprocessors.

Conclusion

Implicit Parallelism and Explicit Parallelism are the two ways of parallel programming. to achieve parallelism. The parallelism is carried out by the compiler or manually by coding. Resource utilization is more efficient in explicit parallelism as compared to implicit parallelism and has applications in shared memory and loosely coupled multiprocessors.