⚡Parallel Processing

NEW

Also known as: Parallelization, Concurrent processing

Executing multiple computations simultaneously to reduce runtime or handle larger workloads efficiently.

DESCRIPTION

Parallel processing subdivides tasks to run concurrently across cores, processors, or machines. It improves throughput and reduces wall-clock time for compute-intensive workloads. Models include data parallelism, task parallelism, and pipeline parallelism. Frameworks such as OpenMP and MPI support shared-memory and distributed-memory paradigms. Modern systems leverage GPUs and vectorization for massive parallelism. Correctness requires attention to synchronization, race conditions, and memory consistency. Effective parallel design balances work, minimizes communication, and reduces contention.

ALTERNATIVE DEFINITIONS

Wikipedia

Parallel computing is a type of computation in which many calculations or processes are carried out simultaneously.

Canonical emphasis on simultaneous execution.

Source

Encyclopaedia Britannica

Parallel processing, the simultaneous use of more than one computer or processor to execute a program.

Highlights use of multiple processors or computers.

Source

RELATED TERMS

Distributed Computing Multithreading Concurrency GPU Computing Vectorization MapReduce Apache Spark OpenMP MPI Data Parallelism

COMPETING TERMS

Serial Processing

KNOWLEDGE AREAS

Data Engineering Infrastructure Real-time & Streaming Data Software Engineering

TARGET AUDIENCE

Data Engineer Software Developer MLOps Engineer Cloud Architect Data Architect

EXTERNAL SOURCES

• Wikipedia - Parallel computing
• OpenMP Architecture Review Board - OpenMP parallel programming specification
• NVIDIA - CUDA guide for GPU parallelism
• Apache Spark - Cluster parallel data processing documentation

TOP QUESTIONS

What problems benefit most from Parallel Processing?

Compute-bound tasks with independent subproblems, such as matrix operations, training models, and ETL over partitions, typically benefit most.

Category: Application

How is Parallel Processing different from Concurrency?

Parallelism executes multiple tasks simultaneously; concurrency structures programs to handle multiple tasks, which may or may not run in parallel.

Category: Misunderstanding

What are common pitfalls in Parallel Processing?

Contention, false sharing, load imbalance, excessive synchronization, and I/O bottlenecks can erase expected speedups.

Category: Mistakes

How do Amdahl’s and Gustafson’s laws affect Parallel Processing?

They describe limits and scalability: serial fractions cap speedup (Amdahl), while scaling problem size can improve parallel efficiency (Gustafson).

Category: Concept

How do I choose between threads, processes, or distributed systems?

Use threads for shared-memory CPU-bound tasks, processes for isolation, and distributed frameworks when data or computation exceed single-host limits.

Category: Requirements

What is data parallelism?

The same operation is applied across partitions of data, common in SIMD, GPUs, and batch ETL over shards.

Category: Definition

How do GPUs accelerate Parallel Processing?

GPUs provide thousands of cores optimized for SIMD workloads, accelerating linear algebra, deep learning, and graphics pipelines.

Category: Example

How can I measure speedup and efficiency?

Speedup equals serial time over parallel time; efficiency is speedup divided by number of processors.

Category: Testing

What is a good first step to parallelize code?

Profile to find hotspots, then apply a coarse-grained parallel strategy (e.g., map over partitions) before fine-grained tuning.

Category: BestPractice