Parallel computing means breaking down complex tasks into smaller, separate parts that can be solved simultaneously by multiple processors. These processors communicate via shared memory, and their results are combined to solve the overall problem faster. The main goal is to increase computation power for quicker application processing and problem-solving.
Parallel Computing Infrastructure: In a data center, multiple processors are placed in server racks. The application server distributes computation requests in small chunks to be executed at the same time on each server.
Types of Parallel Computing
Bit-level parallelism: Increases processor word size to perform operations on variables larger than the word length.
Instruction-level parallelism: Executes instructions in parallel, decided by the processor (dynamic parallelism) or compiler (static parallelism).
Task parallelism: Runs different tasks simultaneously on the same data.
Superword-level parallelism: Exploits parallelism in inline code.
Parallel Application Types
Parallel applications are categorized as fine-grained (subtasks communicate frequently), coarse-grained (subtasks communicate less), and embarrassing parallelism (subtasks rarely communicate). Mapping in parallel computing solves embarrassing parallel problems by applying a simple operation to all elements without communication.
Evolution of Parallel Computing
Parallel computing evolved due to the power wall from increasing processor frequency. Instead, power-efficient processors with multiple cores were designed to address power consumption and overheating issues.
Importance of Parallel Computing
Parallel computing is crucial with the rise of multicore processors and GPUs. GPUs work alongside CPUs to increase data throughput and concurrent calculations. Parallelism enables GPUs to complete more work than CPUs in the same time.
Parallel Computer Architecture
Parallel computer architecture exists in various types, including multi-core computing, symmetric multiprocessing, distributed computing, and massively parallel computing. Different architectures utilize parallel hardware effectively.
Parallel Computing Software Solutions
Concurrent programming languages, APIs, libraries, and programming models aid parallel computing. Techniques like application checkpointing and automatic parallelization improve fault tolerance and multi-threaded code creation.
Difference Between Parallel Computing and Cloud Computing
Cloud computing delivers scalable services over the internet, while parallel computing uses multiple processors to solve tasks. Cloud computing provides parallel processing power and storage to a broader audience.
Difference Between Parallel Processing and Parallel Computing
Parallel processing divides tasks among multiple CPUs, while parallel computing involves optimizing software for parallel processing. The terms are often used interchangeably but have distinct focuses.
Difference Between Sequential and Parallel Computing
Sequential computing uses one processor for sequential execution, limiting speed. Parallel computing uses multiple processors, increasing problem-solving speed.
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