History

The concept of parallel processing dates back to the 1960s when supercomputers with multiple processors were first developed. As the cost of processors decreased over time, parallel processing expanded from supercomputing into mainstream computing. Today multi-core processors that support parallel processing are standard even in consumer devices like laptops and smartphones.

Benefits

Parallel processing offers significant benefits over sequential processing on a single processor:

• Speed: By using multiple CPUs, parallel processing can significantly reduce processing time for large computations. Many tasks can be sped up by orders of magnitude.
• Efficiency: Parallel algorithms can maximize utilization of available computing resources.
• Scalability: Parallel systems can easily be expanded by adding more processors.

Challenges

Effective parallel processing also comes with some key challenges:

• Synchronization: Ensuring threads have access to shared data without conflicts.
• Communication: Enabling communication between threads to share intermediate results.
• Load Balancing: Distributing work evenly across processors.
• Task Partitioning: Breaking up the problem to minimize dependencies.

Types of Parallel Processing

There are several types of parallel processing, categorized by how the work is distributed:

Data Parallelism

The same computation is performed across multiple processors on different subsets of the input data. Common in scientific simulations.

Task Parallelism

Different computations of the same larger problem are distributed to multiple processors. Useful for complex workflows.

Pipelining

Different stages of a sequential process are assigned to different processors analogous to an assembly line. Maximizes throughput.

Instruction-Level Parallelism

Parallelism is exploited at the CPU instruction level, e.g. through superscalar or VLIW architectures.

How Parallel Processing Works

Executing programs in parallel requires both specialized hardware and software:

Parallel Hardware

Various architectures exist for connecting multiple processors, such as shared memory machines or computer clusters. Specialized parallel processors like GPUs and hardware accelerators are also used.

Parallel Software and Algorithms

Software frameworks help developers implement parallel programs by handling underlying communication and synchronization:

Threading

Lightweight processes called threads represent tasks for available CPUs.

Message Passing

Threads running on different processors pass data through messages.

Divide and Conquer

Recursively break down problem into smaller independent sub-problems.

MapReduce

Big data processing model that splits computation across clusters.

Synchronization

Synchronization mechanisms like locks and barriers coordinate access to shared data to prevent race conditions.

Load Balancing

Strategies evenly distribute computational load across processors to maximize utilization. Work stealing algorithms dynamically shift load from overloaded to idle processors.

Parallel Processing Architectures

There are three main hardware architectures for connecting processors:

Shared Memory Architecture

All processors access a common central memory. Simplifies communication but limits scalability.

Distributed Memory Architecture

Nodes have their own private local memory and pass messages to communicate. More scalable but complex to program.

Hybrid Architecture

Combines both shared and distributed memory for the benefits of simplicity and scalability. Used in most supercomputers today.

Applications of Parallel Processing

Parallel processing unlocks performance improvements across a diverse range of computing applications:

Scientific Computing

Complex simulations of phenomena like weather, aerodynamics, nuclear reactions etc.

Artificial Intelligence

Training deep neural networks leverages massive parallelism of GPUs.

Computer Graphics

Rendering

Ray tracing and rasterization of high resolution images and video.

Animation

Near real-time rendering of CGI for films and video games.

Data Mining

Finding insights in huge datasets using Map reduce on compute clusters.

Financial Modeling

Valuation of complex derivatives and portfolios uses Monte Carlo simulation.