Introduction to Parallel Computing
Before we get into the details of Parallel Computing, let’s first look at the history of computing software and the reasons it failed in the modern era.
Computer software was originally written for serial computing. An algorithm is a method of solving a problem by breaking it down into smaller instructions. These instructions are then executed one at a time on the Central Processing Unit (CPU) of a computer. Once one instruction has been completed, the next one will be started.
This is a real-life example. Imagine people waiting in line for tickets to a movie and there is no cashier. The cashier gives tickets one at a time to each person. This situation becomes more complicated when there are two queues and one cashier.
In short, Serial Computing can be described as:
- This allows you to break down a problem statement into separate instructions.
- The instructions will then be followed one by one.
- One instruction can be executed at a time.
Point 3. This caused a major problem in the computing industry because only one instruction could be executed at any given moment. This caused a significant waste of hardware resources, as only one component of the hardware would be running at any given instruction or time. The time it took to execute problem statements was increasing as they became more complicated and heavier. Pentium 3 or Pentium 4 are two examples of processors.
Let’s get back to the real-world problem. Complexity will decrease when there are 2 queues and 2 ticket agents giving tickets to 2 people simultaneously. Parallel Computing is illustrated in this example.
This is when multiple processing elements are used simultaneously to solve a problem. Each resource is applied simultaneously to solve the problem.
AdvantagesThese are the advantages of Parallel Computing over Serial Computing:
- As many resources work together, it saves time and money.
- It is possible to solve more complex problems using Serial Computing.
- When local resources are limited, it can use non-local resources.
- Parallel Computing is better than serial computing because it ‘wastes” the potential computing power.
Types of parallelism
- Bit-level parallelism –
This is parallel computing that is based on increasing the processor’s size. This reduces the number of instructions the system must execute to complete a task with large-sized data.
Example: A scenario in which an 8-bit processor has to compute the sum of two 16 bit integers is one example. The operation requires two instructions. It first must add up the 8 lower-order bits and then subtract the 8 higher-order ones. The operation can be performed by a 16-bit processor with one instruction.
- Instruction-level parallelism –
A processor can address only one instruction per clock cycle phase. These instructions can be re-ordered or grouped, which can later be executed simultaneously without affecting the program’s result. This is known as instruction-level parallelism.
- Task Parallelism –
Task parallelism is the process of breaking down a task into smaller tasks and allocating subtasks to be executed. Sub-task execution is performed concurrently by processors.
4. Data-level parallelism (DLP) –
Instructions from one stream can be used concurrently to access multiple data. This is limited by non-regular data manipulation patterns, memory bandwidth, and other limitations.
Parallel computing: Why?
- Dynamic nature is the basis of all real-world. Many things can happen simultaneously at the same time, but not at the same place at the same time. These data are extremely large and difficult to manage.
- Parallel computing is essential for real-world data. It allows for more dynamic simulations and modeling.
- Parallel computing allows for concurrency, which saves money and time.
- Large, complex data sets can only be managed using parallel computing.
- This ensures efficient use of resources. It is possible to use the hardware effectively, whereas serial computation uses only a portion of the hardware and leaves the rest idle.
- It is also impossible to implement real-time systems with serial computing.
- Databases and data mining
- Simulation of real-time systems.
- Science and Engineering
- Advanced graphics, augmented reality, and virtual reality.
Limitations of Parallel Computing
- It addresses communication and synchronization among multiple sub-tasks or processes, which can be difficult to achieve.
- It is important to manage the algorithms in a way that allows them to be used in parallel.
- Programs and algorithms must be low in coupling and have high cohesion. It is difficult to create such programs.
- Expert programmers and programmers with more technical skills can code parallelism-based programs well.
The Future of Parallel ComputingThe computational graph has seen a significant shift from serial computing towards parallel computing. Intel, a tech giant, has already made a move towards parallel computing with multicore processors. Parallel computing will change the way computers work in the future for the better. Parallel Computing plays a greater role in keeping the world connected than ever before. Parallel Computing is even more important with faster networks, distributed systems, multi-processor computers, and multi-processor machines.
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