Before taking a toll on Parallel Computing, first let’s take a look at the background of computations of a computer software and why it failed for the modern era.
Computer software were written conventionally for serial computing. This meant that to solve a problem, an algorithm divides the problem into smaller instructions. These discrete instructions are then executed on Central Processing Unit of a computer one by one. Only after one instruction is finished, next one starts.
Real life example of this would be people standing in a queue waiting for movie ticket and there is only cashier.Cashier is giving ticket one by one to the persons. Complexity of this situation increases when there are 2 queues and only one cashier.
So, in short Serial Computing is following:
- In this, a problem statement is broken into discrete instructions.
- Then the instructions are executed one by one.
- Only one instruction is executed at any moment of time.
Look at point 3. This was causing a huge problem in computing industry as only one instruction was getting executed at any moment of time. This was a huge waste of hardware resources as only one part of the hardware will be running for a particular instruction and of time. As problem statements were getting heavier and bulkier, so does the amount of time in execution of those statements. Example of processors are Pentium 3 and Pentium 4.
Now let’s come back to our real life problem. We could definitely say that complexity will decrease when there are 2 queues and 2 cashier giving tickets to 2 persons simultaneously. This is an example of Parallel Computing.
Parallel Computing –
It is the use of multiple processing elements simultaneously for solving any problem. Problems are broken down into instructions and are solved concurrently as each resource which has been applied to work is working at the same time.
Advantages of Parallel Computing over Serial Computing are as follows:
- It saves time and money as many resources working together will reduce the time and cut potential costs.
- It can be impractical to solve larger problems on Serial Computing.
- It can take advantage of non-local resources when the local resources are finite.
- Serial Computing ‘wastes’ the potential computing power, thus Parallel Computing makes better work of hardware.
Types of Parallelism:
- Bit-level parallelism: It is the form of parallel computing which is based on the increasing processor’s size. It reduces the number of instructions that the system must execute in order to perform a task on large-sized data.
Example: Consider a scenario where an 8-bit processor must compute the sum of two 16-bit integers. It must first sum up the 8 lower-order bits, then add the 8 higher-order bits, thus requiring two instructions to perform the operation. A 16-bit processor can perform the operation with just one instruction.
- Instruction-level parallelism: A processor can only address less than one instruction for each clock cycle phase. These instructions can be re-ordered and grouped which are later on executed concurrently without affecting the result of the program. This is called instruction-level parallelism.
- Task Parallelism: Task parallelism employs the decomposition of a task into subtasks and then allocating each of the subtasks for execution. The processors perform execution of sub tasks concurrently.
Why parallel computing?
- The whole real world runs in dynamic nature i.e. many things happen at a certain time but at different places concurrently. This data is extensively huge to manage.
- Real world data needs more dynamic simulation and modeling, and for achieving the same, parallel computing is the key.
- Parallel computing provides concurrency and saves time and money.
- Complex, large datasets, and their management can be organized only and only using parallel computing’s approach.
- Ensures the effective utilization of the resources. The hardware is guaranteed to be used effectively whereas in serial computation only some part of hardware was used and the rest rendered idle.
- Also, it is impractical to implement real-time systems using serial computing.
Applications of Parallel Computing:
- Data bases and Data mining.
- Real time simulation of systems.
- Science and Engineering.
- Advanced graphics, augmented reality and virtual reality.
Limitations of Parallel Computing:
- It addresses such as communication and synchronization between multiple sub-tasks and processes which is difficult to achieve.
- The algorithms must be managed in such a way that they can be handled in the parallel mechanism.
- The algorithms or program must have low coupling and high cohesion. But it’s difficult to create such programs.
- More technically skilled and expert programmers can code a parallelism based program well.
Future of Parallel Computing: The computational graph has undergone a great transition from serial computing to parallel computing. Tech giant such as Intel has already taken a step towards parallel computing by employing multicore processors. Parallel computation will revolutionize the way computers work in the future, for the better good. With all the world connecting to each other even more than before, Parallel Computing does a better role in helping us stay that way. With faster networks, distributed systems, and multi-processor computers, it becomes even more necessary.
Attention reader! Don’t stop learning now. Get hold of all the important CS Theory concepts for SDE interviews with the CS Theory Course at a student-friendly price and become industry ready.