Parallel computing in the context of Carnegie Mellon School of Computer Science

Mozilla Firefox, or simply Firefox, is a free and open-source web browser developed by the Mozilla Foundation and its subsidiary, the Mozilla Corporation. It uses the Gecko rendering engine to display web pages, which implements current and anticipated web standards. Firefox is available for Windows 10 or later versions of Windows, macOS, and Linux. Its unofficial ports are available for various Unix and Unix-like operating systems, including FreeBSD, OpenBSD, NetBSD, and other operating systems, such as ReactOS. It is the default, pre-installed browser on Debian, Ubuntu, and other Linux distros. Firefox is also available for Android and iOS. However, as with all other iOS web browsers, the iOS version uses the WebKit layout engine instead of Gecko due to platform requirements. An optimized version was also available on the Amazon Fire TV as one of the two main browsers available with Amazon's Silk Browser, until April 30, 2021, when Firefox would be discontinued on that platform. Firefox is the spiritual successor of Netscape Navigator, as the Mozilla community was created by Netscape in 1998, before its acquisition by AOL. Firefox was created in 2002 under the codename "Phoenix" by members of the Mozilla community who desired a standalone browser rather than the Mozilla Application Suite bundle. During its beta phase, it proved to be popular with its testers and was praised for its speed, security, and add-ons compared to Microsoft's then-dominant Internet Explorer 6. It was released on November 9, 2004, and challenged Internet Explorer's dominance with 60 million downloads within nine months. In November 2017, Firefox began incorporating new technology under the code name "Quantum" to promote parallelism and a more intuitive user interface.

Firefox usage share grew to a peak of 32.21% in November 2009, with Firefox 3.5 overtaking Internet Explorer 7, although not all versions of Internet Explorer as a whole; its usage then declined in competition with Google Chrome. As of February 2025, according to StatCounter, it had a 6.36% usage share on traditional PCs (i.e. as a desktop browser), making it the fourth-most popular PC web browser after Google Chrome (65%), Microsoft Edge (14%), and Safari (8.65%).

In computing, time-sharing is the concurrent sharing of a computing resource among many tasks or users by giving each task or user a small slice of processing time. This quick switch between tasks or users gives the illusion of simultaneous execution. It enables multi-tasking by a single user or enables multiple-user sessions.

Developed during the 1960s, its emergence as the prominent model of computing in the 1970s represented a major technological shift in the history of computing. By allowing many users to interact concurrently with a single computer, time-sharing dramatically lowered the cost of providing computing capability, made it possible for individuals and organizations to use a computer without owning one, and promoted the interactive use of computers and the development of new interactive applications.

A distributed database is a database in which data is stored across different physical locations. It may be stored in multiple computers located in the same physical location (e.g. a data centre); or maybe dispersed over a network of interconnected computers. Unlike parallel systems, in which the processors are tightly coupled and constitute a single database system, a distributed database system consists of loosely coupled sites that share no physical components.

System administrators can distribute collections of data (e.g. in a database) across multiple physical locations. A distributed database can reside on organised network servers or decentralised independent computers on the Internet, on corporate intranets or extranets, or on other organisation networks. Because distributed databases store data across multiple computers, distributed databases may improve performance at end-user worksites by allowing transactions to be processed on many machines, instead of being limited to one.

In theoretical computer science and mathematics, computational complexity theory focuses on classifying computational problems according to their resource usage, and explores the relationships between these classifications. A computational problem is a task solved by a computer. A computation problem is solvable by mechanical application of mathematical steps, such as an algorithm.

A problem is regarded as inherently difficult if its solution requires significant resources, whatever the algorithm used. The theory formalizes this intuition, by introducing mathematical models of computation to study these problems and quantifying their computational complexity, i.e., the amount of resources needed to solve them, such as time and storage. Other measures of complexity are also used, such as the amount of communication (used in communication complexity), the number of gates in a circuit (used in circuit complexity) and the number of processors (used in parallel computing). One of the roles of computational complexity theory is to determine the practical limits on what computers can and cannot do. The P versus NP problem, one of the seven Millennium Prize Problems, is part of the field of computational complexity.

Instruction-level parallelism (ILP) is the parallel or simultaneous execution of a sequence of instructions in a computer program. More specifically, ILP refers to the average number of instructions run per step of this parallel execution.

The instruction cycle (also known as the fetch–decode–execute cycle, or simply the fetch–execute cycle) is the cycle that the central processing unit (CPU) follows from boot-up until the computer has shut down in order to process instructions. It is composed of three main stages: the fetch stage, the decode stage, and the execute stage.

In simpler CPUs, the instruction cycle is executed sequentially, each instruction being processed before the next one is started. In most modern CPUs, the instruction cycles are instead executed concurrently, and often in parallel, through an instruction pipeline: the next instruction starts being processed before the previous instruction has finished, which is possible because the cycle is broken up into separate steps.

Gene Myron Amdahl (November 16, 1922 – November 10, 2015) was an American computer architect and high-tech entrepreneur, chiefly known for his work on mainframe computers at IBM and later his own companies, especially Amdahl Corporation. He formulated Amdahl's law, which states a fundamental limitation of parallel computing.

Myrmecology (/mɜːrmɪˈkɒlədʒi/; from Greek: μύρμηξ, myrmex, "ant" and λόγος, logos, "study") is a branch of entomology focusing on the study of ants. Ants continue to be a model of choice for the study of questions on the evolution of social systems because of their complex and varied forms of social organization. Their diversity and prominence in ecosystems also has made them important components in the study of biodiversity and conservation. In the 2000s, ant colonies began to be studied and modeled for their relevance in machine learning, complex interactive networks, stochasticity of encounter and interaction networks, parallel computing, and other computing fields.

In computer science, concurrency refers to the ability of a system to execute multiple tasks through simultaneous execution or time-sharing (context switching), sharing resources and managing interactions. Concurrency improves responsiveness, throughput, and scalability in modern computing, including:

• Operating systems and embedded systems
• Distributed systems, parallel computing, and high-performance computing
• Database systems, web applications, and cloud computing

In computing, multitasking is the concurrent execution of multiple tasks (also known as processes) over a certain period of time. New tasks can interrupt already started ones before they finish, instead of waiting for them to end. As a result, a computer executes segments of multiple tasks in an interleaved manner, while the tasks share common processing resources such as central processing units (CPUs) and main memory. Multitasking automatically interrupts the running program, saving its state (partial results, memory contents and computer register contents) and loading the saved state of another program and transferring control to it. This "context switch" may be initiated at fixed time intervals (pre-emptive multitasking), or the running program may be coded to signal to the supervisory software when it can be interrupted (cooperative multitasking).

Multitasking does not require parallel execution of multiple tasks at exactly the same time; instead, it allows more than one task to advance over a given period of time. Even on multiprocessor computers, multitasking allows many more tasks to be run than there are CPUs.

In concurrent computing, deadlock is any situation in which no member of some group of entities can proceed because each waits for another member, including itself, to take action, such as sending a message or, more commonly, releasing a lock. Deadlocks are a common problem in multiprocessing systems, parallel computing, and distributed systems, because in these contexts systems often use software or hardware locks to arbitrate shared resources and implement process synchronization.

In an operating system, a deadlock occurs when a process or thread enters a waiting state because a requested system resource is held by another waiting process, which in turn is waiting for another resource held by another waiting process. If a process remains indefinitely unable to change its state because resources requested by it are being used by another process that itself is waiting, then the system is said to be in a deadlock.

Grid computing is the use of widely distributed computer resources to reach a common goal. A computing grid can be thought of as a distributed system with non-interactive workloads that involve many files. Grid computing is distinguished from conventional high-performance computing systems such as cluster computing in that grid computers have each node set to perform a different task/application. Grid computers also tend to be more heterogeneous and geographically dispersed (thus not physically coupled) than cluster computers. Although a single grid can be dedicated to a particular application, commonly a grid is used for a variety of purposes. Grids are often constructed with general-purpose grid middleware software libraries. Grid sizes can be quite large.

Grids are a form of distributed computing composed of many networked loosely coupled computers acting together to perform large tasks. For certain applications, distributed or grid computing can be seen as a special type of parallel computing that relies on complete computers (with onboard CPUs, storage, power supplies, network interfaces, etc.) connected to a computer network (private or public) by a conventional network interface, such as Ethernet. This is in contrast to the traditional notion of a supercomputer, which has many processors connected by a local high-speed computer bus. This technology has been applied to computationally intensive scientific, mathematical, and academic problems through volunteer computing, and it is used in commercial enterprises for such diverse applications as drug discovery, economic forecasting, seismic analysis, and back office data processing in support for e-commerce and Web services.

OpenCL (Open Computing Language) is a framework for writing programs that execute across heterogeneous platforms consisting of central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), field-programmable gate arrays (FPGAs) and other processors or hardware accelerators. OpenCL specifies a programming language (based on C99) for programming these devices and application programming interfaces (APIs) to control the platform and execute programs on the compute devices. OpenCL provides a standard interface for parallel computing using task- and data-based parallelism.

OpenCL is an open standard maintained by the Khronos Group, a non-profit, open standards organisation. Conformant implementations (passed the Conformance Test Suite) are available from a range of companies including AMD, Arm, Cadence, Google, Imagination, Intel, Nvidia, Qualcomm, Samsung, SPI and Verisilicon.

CUDA (Compute Unified Device Architecture) is a proprietary parallel computing platform and application programming interface (API) that allows software to use certain types of graphics processing units (GPUs) for accelerated general-purpose processing, significantly broadening their utility in scientific and high-performance computing. CUDA was created by Nvidia starting in 2004 and was officially released in 2007. When it was first introduced, the name was an acronym for Compute Unified Device Architecture, but Nvidia later dropped the common use of the acronym and now rarely expands it.

CUDA is both a software layer that manages data, giving direct access to the GPU and CPU as necessary, and a library of APIs that enable parallel computation for various needs. In addition to drivers and runtime kernels, the CUDA platform includes compilers, libraries and developer tools to help programmers accelerate their applications.

General-purpose computing on graphics processing units (GPGPU, or less often GPGP) is the use of a graphics processing unit (GPU), which typically handles computation only for computer graphics, to perform computation in applications traditionally handled by the central processing unit (CPU). The use of multiple video cards in one computer, or large numbers of graphics chips, further parallelizes the already parallel nature of graphics processing.

Essentially, a GPGPU pipeline is a kind of parallel processing between one or more GPUs and CPUs, with special accelerated instructions for processing image or other graphic forms of data. While GPUs operate at lower frequencies, they typically have many times the number of Processing elements. Thus, GPUs can process far more pictures and other graphical data per second than a traditional CPU. Migrating data into parallel form and then using the GPU to process it can (theoretically) create a large speedup.