Classes of Parallel Computers

Parallel computers are classified based on their structure and the way they handle tasks. Here are the main types:

Multi-Core Computing

One of the most common forms of parallel computing is multi-core computing. This involves a single computing component with two or more independent processing units, known as cores. Each core can execute instructions independently of the others.

Multi-core processors have become the norm in personal computers and servers, as they increase performance and energy efficiency. They are particularly useful in multitasking environments where several programs run concurrently.

Symmetric Multiprocessing

Symmetric multiprocessing (SMP) is a class of parallel computing architecture where two or more identical processors are connected to a single shared main memory. Most SMP systems use a uniform memory access (UMA) architecture, in which all processors share the physical memory.

SMP systems are highly efficient when running multiple tasks that require frequent inter-processor communication. They are commonly used in servers, where many tasks need to be executed simultaneously. The primary advantage of SMP systems is their ability to increase computational speed while maintaining the simplicity of a single processor system.

Distributed Computing

In distributed computing, a single task is divided into many smaller subtasks that are distributed across multiple computers. These computers may be located in the same physical location, or they may be spread out across different geographical locations.

Distributed computing systems are highly scalable, as more computers can be added to the network to increase computational power. They are used for tasks that require massive amounts of data and computational resources, such as processing of large databases, scientific simulations, and large-scale web applications.

Cluster Computing

Cluster computing is a type of parallel computing where a group of computers are linked together to form a single, unified computing resource. These computers, known as nodes, work together to execute tasks more quickly than a single computer could.

Cluster computing is useful for tasks that require high performance, reliability, and availability. By distributing tasks across multiple nodes, cluster computing reduces the risk of system failure, as even if one node fails, the remaining nodes can continue processing.

Massively Parallel Computing

Massively parallel computing is a type of parallel computing where hundreds or thousands of processors are used to perform a set of coordinated computations simultaneously. This type of computing is used for tasks that require high computational power, such as genetic sequencing, climate modeling, and fluid dynamics simulations.

Massively parallel computers use a distributed memory architecture, where each processor has its own private memory. Communication between processors is achieved through a variety of methods, including messaging systems and shared memory.

Grid Computing

Grid computing is a form of distributed computing where a virtual supercomputer is composed of networked, loosely coupled computers, which are used to perform large tasks.

Grid computing is used for tasks that require a large amount of computational resources that can't be fulfilled by a single computer but don't require the high performance of a supercomputer. It's commonly used in scientific, mathematical, and academic research, as well as in large enterprises for resource-intensive tasks.

Related content: Read our guide to parallel computing with Python

Types of Parallel Computing Architectures

Shared Memory Systems

In shared memory systems, multiple processors access the same physical memory. This allows for efficient communication between processors because they directly read from and write to a common memory space. Shared memory systems are typically easier to program than distributed memory systems due to the simplicity of memory access.

However, shared memory systems can face challenges with scalability and memory contention. As the number of processors increases, the demand for memory access can lead to bottlenecks, where processors are waiting for access to shared memory. 

Common examples of shared memory systems include symmetric multiprocessors (SMP) and multicore processors found in modern desktop computers and servers. These systems are well-suited for applications that require tight coupling and frequent communication between processors, such as real-time data processing and complex simulations.

Distributed Memory Systems

Distributed memory systems consist of multiple processors, each with its own private memory. Processors communicate by passing messages over a network. This design can scale more effectively than shared memory systems, as each processor operates independently, and the network can handle communication between them.

The primary challenge in distributed memory systems is the complexity of communication and synchronization. Programmers need to explicitly manage data distribution and message passing, often using libraries such as MPI (Message Passing Interface). The latency and bandwidth of the network can also impact performance.

Distributed memory systems are commonly used in high-performance computing (HPC) environments, such as supercomputers and large-scale clusters. They are suitable for applications that can be decomposed into independent tasks with minimal inter-process communication, like large-scale simulations and data analysis.

Hybrid Systems

Hybrid systems combine elements of shared and distributed memory architectures. They typically feature nodes that use shared memory, interconnected by a distributed memory network. Each node operates as a shared memory system, while communication between nodes follows a distributed memory model.

Within a node, tasks can communicate quickly using shared memory, while inter-node communication uses message passing. 

One common use case for hybrid systems is in large-scale scientific computing, where computations are divided into smaller tasks within nodes and coordinated across a larger network. Hybrid systems can efficiently handle complex workloads that require both high-speed local computation and distributed processing across a large number of processors.

Parallel Computing Techniques

Here are the primary techniques used to parallelize tasks on computing systems:

Bit-Level Parallelism

Bit-level parallelism is a type of parallel computing that seeks to increase the number of bits processed in a single instruction. This form of parallelism dates back to the era of early computers, where it was discovered that using larger word sizes could significantly speed up computation.

In bit-level parallelism, the focus is primarily on the size of the processor's registers. These registers hold the data being processed. By increasing the register size, more bits can be handled simultaneously, thus increasing computational speed. The shift from 32-bit to 64-bit computing in the early 2000s is a prime example of bit-level parallelism.

While the implementation of bit-level parallelism is largely hardware-based, it's crucial to understand its implications. For programmers, understanding bit-level parallelism can help design more efficient algorithms, especially for tasks that involve heavy numerical computation

Instruction-Level Parallelism

Instruction-level parallelism (ILP) is another form of parallel computing that focuses on executing multiple instructions simultaneously. Unlike bit-level parallelism, which focuses on data, ILP is all about instructions.

The idea behind ILP is simple: instead of waiting for one instruction to complete before the next starts, a system can start executing the next instruction even before the first one has completed. This approach, known as pipelining, allows for the simultaneous execution of instructions and thus increases the speed of computation.

However, not all instructions can be effectively pipelined. Dependencies between instructions can limit the effectiveness of ILP. For instance, if one instruction depends on the result of another, it cannot be started until the first instruction completes.

Superword Level Parallelism

Superword Level Parallelism (SLP) is a type of parallel computing that focuses on vectorizing operations on data stored in short vector registers. It is a form of data parallelism that operates on arrays or vectors of data.

In superword level parallelism, single instruction, multiple data (SIMD) operations are performed, where one instruction is applied to multiple pieces of data simultaneously. This technique is particularly effective in applications that require the same operation to be performed on large datasets, such as in image and signal processing.

SLP requires both hardware support in the form of vector registers and compiler support to identify opportunities for vectorization. As such, effectively leveraging SLP can be challenging, but the potential performance gains make it a valuable tool in the parallel computing toolbox.

Task Parallelism

While bit-level and instruction-level parallelism focus on data and instructions, in task parallelism, the focus is on distributing tasks across different processors.

A task, in this context, is a unit of work performed by a process. It could be anything from a simple arithmetic operation to a complex computational procedure. The key idea behind task parallelism is that by distributing tasks among multiple processors, we can get more work done in less time.

This form of parallelism requires careful planning and coordination. Tasks need to be divided in such a way that they can be executed independently. Furthermore, tasks may need to communicate with each other, which requires additional coordination.

Parallel Computing vs Parallel Processing

Parallel computing is a broad term encompassing the entire field of executing multiple computations simultaneously. It includes various architectures, techniques, and models used to achieve concurrent execution of tasks.

Parallel processing refers to the act of performing multiple operations at the same time. It is a subset of parallel computing focused on the execution aspect. Parallel processing can occur at different levels, such as bit-level, instruction-level, data-level, and task-level parallelism. Each level addresses different aspects of computation to improve performance.

While parallel computing involves the design and implementation of systems that can perform parallel processing, parallel processing is concerned with the actual execution of operations in a concurrent manner.

Parallel Computing vs Sequential Computing

Sequential computing processes tasks one at a time, in a linear fashion. Each task must complete before the next one begins, which can lead to inefficiencies, especially for large or complex problems. Sequential computing is simple to implement and debug but is limited in performance by the speed of a single processor.

Parallel computing divides tasks into smaller sub-tasks that can be executed simultaneously across multiple processors. This significantly reduces the time required to complete large computations. Parallel computing is more complex to design and implement due to the need for coordination and synchronization between tasks.

Parallel Processing Examples and Use Cases

Parallel computing has practical applications in various fields. Here are a few real world examples:

Supercomputers for Use in Astronomy

In astronomy, supercomputers equipped with parallel processing capabilities are used to process vast amounts of data generated by telescopes and other observational instruments.

These supercomputers can perform complex calculations in a fraction of the time it would take a single-processor computer. This allows astronomers to create detailed simulations of celestial bodies, analyze light spectra from distant stars, and search for patterns in vast quantities of data that may indicate the presence of exoplanets.

For example, the Pleiades supercomputer at NASA's Ames Research Center uses parallel processing to support some of the agency's most complex simulations, including those related to the study of dark matter and the evolution of galaxies.

Making Predictions in Agriculture

In agriculture, parallel computing is used to analyze data and make predictions that can improve crop yields and efficiency. For instance, by analyzing weather data, soil conditions, and other factors, farmers can make informed decisions about when to plant, irrigate, and harvest crops.

Parallel computing makes it possible to process this data quickly and accurately. For example, a supercomputer could analyze data from thousands of weather stations, satellite images, and soil samples to predict the optimal planting time for a particular crop.

Video Post-Production Effects

Parallel computing plays a significant role in the field of video post-production effects. These effects, which include 3D animation, color grading, and visual effects (VFX), require a high level of computational power. Sequential computing, which processes one task at a time, is often inadequate for these tasks due to their complexity.

By dividing these tasks into smaller sub-tasks and processing them simultaneously, parallel computing drastically reduces the time required for rendering and processing video effects. Film studios use supercomputers and render farms (networks of computers) to quickly create stunning visual effects and animation sequences. Without parallel computing, the impressive visual effects we see in blockbuster movies and high-quality video games would be nearly impossible to achieve in practical timeframes.

Accurate Medical Imaging

Another field where parallel computing has made a profound impact is in the field of medical imaging. Techniques such as Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scans generate a large amount of data that needs to be processed quickly and accurately.

Parallel computing allows for faster image processing, enhancing the accuracy and efficiency of these imaging techniques. The simultaneous processing of image data enables radiologists to obtain high-resolution 3D images in real-time, aiding in more accurate diagnosis and treatment. Parallel computing also powers advanced imaging techniques like functional MRI (fMRI), which captures and processes dynamic data about the brain's functioning.

By improving the speed and accuracy of medical imaging, parallel computing plays a crucial role in advancing healthcare outcomes, enabling clinicians to detect and treat illnesses more effectively.

Challenges in Parallel Computing

There are several challenges associated with implementing parallel computing.

Synchronization and Coordination

]Synchronization mechanisms, such as locks, semaphores, and barriers, are used to manage access to shared resources and ensure data consistency. Without proper synchronization, race conditions can occur, leading to incorrect results or system crashes. Coordination involves managing the sequence and timing of tasks to optimize performance and avoid bottlenecks. 

Load Balancing

Load balancing is the process of distributing tasks evenly across multiple processors to ensure that no single processor is overwhelmed while others are idle. Poor load balancing can lead to suboptimal performance and increased computation time. Static load balancing assigns tasks to processors before execution begins, while dynamic load balancing redistributes tasks during execution based on the current workload.

Communication Overhead

In parallel computing, processors often need to communicate with each other to exchange data and coordinate tasks. This communication can introduce overhead, which can impact performance, especially in distributed memory systems where data must be transferred over a network. 

Debugging and Profiling

In parallel computing, issues such as race conditions, deadlocks, and non-deterministic behavior can arise, making it difficult to identify and fix bugs. Profiling tools are used to analyze the performance of parallel applications, identifying bottlenecks and inefficiencies. These tools must handle the complexity of concurrent execution and provide insights into how tasks interact.

3 Ways to Achieve Parallelization in Software Engineering

Even with a parallel computing system in place, software engineers need to use specialized techniques to manage parallelization of tasks and instructions. Here are three common techniques.

Application Checkpointing

Application checkpointing involves periodically saving the state of an application during its execution. In case of a failure, the application can resume from the last saved state, reducing the loss of computation and time.

Application checkpointing prevents the loss of all the computation done so far in case of system failure or shutdown, making it a critical component of distributed computing systems. It makes it possible to arbitrarily shut down instances of a parallel computing system and move workloads to other instances.

Automatic Parallelization

Automatic parallelization is a technique where a compiler identifies portions of a program that can be executed in parallel. This reduces the need for programmers to manually identify and code for parallel execution, simplifying the development process and ensuring more efficient use of computing resources.

While automatic parallelization is not always perfect and may not achieve the same level of efficiency as manual parallelization, it is a powerful tool in the hands of developers. It allows them to leverage the benefits of parallel computing without needing extensive knowledge about parallel programming and hardware architectures.

Parallel Programming Languages

Parallel programming languages are designed to simplify the process of writing parallel programs. These languages include constructs for expressing parallelism, allowing developers to specify parallel tasks without worrying about the low-level details of task scheduling, synchronization, and inter-process communication.

Examples of parallel programming languages include OpenMP, MPI, and CUDA. These languages provide diverse models of parallelism, from shared-memory parallelism (OpenMP) to message-passing parallelism (MPI) and data parallelism (CUDA). By using these languages, developers can make the most of parallel computing systems, developing applications that solve complex problems faster and more efficiently.

Parallel Computing Optimization with Run:ai

Run:ai automates resource management and orchestration for parallelized machine learning infrastructure. With Run:ai, you can automatically run as many compute intensive experiments as needed.

Here are some of the capabilities you gain when using Run:ai:

• Advanced visibility—create an efficient pipeline of resource sharing by pooling GPU compute resources.
• No more bottlenecks—you can set up guaranteed quotas of GPU resources, to avoid bottlenecks and optimize billing.
• A higher level of control—Run:ai enables you to dynamically change resource allocation, ensuring each job gets the resources it needs at any given time.
  

Run:ai simplifies machine learning infrastructure pipelines, helping data scientists accelerate their productivity and the quality of their models.

Learn more about the Run:ai GPU virtualization platform.