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Massively Parallel (computing)
Massively parallel is the term for using a large number of computer processors (or separate computers) to simultaneously perform a set of coordinated computations in parallel. GPUs are massively parallel architecture with tens of thousands of threads. One approach is grid computing, where the processing power of many computers in distributed, diverse administrative domains is opportunistically used whenever a computer is available.''Grid computing: experiment management, tool integration, and scientific workflows'' by Radu Prodan, Thomas Fahringer 2007 pages 1–4 An example is BOINC, a volunteer-based, opportunistic grid system, whereby the grid provides power only on a best effort basis.''Parallel and Distributed Computational Intelligence'' by Francisco Fernández de Vega 2010 pages 65–68 Another approach is grouping many processors in close proximity to each other, as in a computer cluster. In such a centralized system the speed and flexibility of the interconnect beco ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Processor (computing)
In computing and computer science, a processor or processing unit is an electrical component (circuit (computer science), digital circuit) that performs operations on an external data source, usually Memory (computing), memory or some other data stream. It typically takes the form of a microprocessor, which can be implemented on a single metal–oxide–semiconductor integrated circuit chip. In the past, processors were constructed using multiple individual vacuum tubes, multiple individual transistors, or multiple integrated circuits. Today, processors use built-in transistors. The term is frequently used to refer to the central processing unit (CPU) in a system. However, it can also refer to other coprocessors, such as a graphics processing unit (GPU). Traditional processors are typically based on silicon; however, researchers have developed experimental processors based on alternative materials such as carbon nanotubes, graphene, diamond, and alloys made of elements from group ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Data Warehouse Appliance
In computing, the term data warehouse appliance (DWA) was coined by Foster Hinshaw for a computer architecture for data warehouses (DW) specifically marketed for big data analysis and discovery that is simple to use (not a pre-configuration) and has a high performance for the workload. A DWA includes an integrated set of servers, storage, operating systems, and databases. In marketing, the term evolved to include pre-installed and pre-optimized hardware and software as well as similar software-only systems promoted as easy to install on specific recommended hardware configurations or preconfigured as a complete system. These are marketing uses of the term and do not reflect the technical definition. A DWA is designed specifically for high performance big data analytics and is delivered as an easy-to-use packaged system. DW appliances are marketed for data volumes in the terabyte to petabyte range. Technology The data warehouse appliance (DWA) has several characteristics which di ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Manycore Processor
Manycore processors are special kinds of multi-core processors designed for a high degree of parallel processing, containing numerous simpler, independent processor cores (from a few tens of cores to thousands or more). Manycore processors are used extensively in embedded computers and high-performance computing. Contrast with multicore architecture Manycore processors are distinct from multi-core processors in being optimized from the outset for a higher degree of explicit parallelism, and for higher throughput (or lower power consumption) at the expense of latency and lower single-thread performance. The broader category of multi-core processors, by contrast, are usually designed to efficiently run ''both'' parallel ''and'' serial code, and therefore place more emphasis on high single-thread performance (e.g. devoting more silicon to out of order execution, deeper pipelines, more superscalar execution units, and larger, more general caches), and shared memory. These techniqu ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
CUDA
CUDA (or Compute Unified Device Architecture) is a parallel computing platform and application programming interface (API) that allows software to use certain types of graphics processing units (GPUs) for general purpose processing, an approach called general-purpose computing on GPUs (GPGPU). CUDA is a software layer that gives direct access to the GPU's virtual instruction set and parallel computational elements, for the execution of compute kernels. CUDA is designed to work with programming languages such as C, C++, and Fortran. This accessibility makes it easier for specialists in parallel programming to use GPU resources, in contrast to prior APIs like Direct3D and OpenGL, which required advanced skills in graphics programming. CUDA-powered GPUs also support programming frameworks such as OpenMP, OpenACC and OpenCL; and HIP by compiling such code to CUDA. CUDA was created by Nvidia. When it was first introduced, the name was an acronym for Compute Unified Device Architectur ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Cellular Automaton
A cellular automaton (pl. cellular automata, abbrev. CA) is a discrete model of computation studied in automata theory. Cellular automata are also called cellular spaces, tessellation automata, homogeneous structures, cellular structures, tessellation structures, and iterative arrays. Cellular automata have found application in various areas, including physics, theoretical biology and microstructure modeling. A cellular automaton consists of a regular grid of ''cells'', each in one of a finite number of '' states'', such as ''on'' and ''off'' (in contrast to a coupled map lattice). The grid can be in any finite number of dimensions. For each cell, a set of cells called its ''neighborhood'' is defined relative to the specified cell. An initial state (time ''t'' = 0) is selected by assigning a state for each cell. A new ''generation'' is created (advancing ''t'' by 1), according to some fixed ''rule'' (generally, a mathematical function) that determines the new state of e ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Connection Machine
A Connection Machine (CM) is a member of a series of massively parallel supercomputers that grew out of doctoral research on alternatives to the traditional von Neumann architecture of computers by Danny Hillis at Massachusetts Institute of Technology (MIT) in the early 1980s. Starting with CM-1, the machines were intended originally for applications in artificial intelligence (AI) and symbolic processing, but later versions found greater success in the field of computational science. Origin of idea Danny Hillis and Sheryl Handler founded Thinking Machines Corporation (TMC) in Waltham, Massachusetts, in 1983, moving in 1984 to Cambridge, MA. At TMC, Hillis assembled a team to develop what would become the CM-1 Connection Machine, a design for a massively parallel hypercube-based arrangement of thousands of microprocessors, springing from his PhD thesis work at MIT in Electrical Engineering and Computer Science (1985). The dissertation won the ACM Distinguished Dissertation prize ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Symmetric Multiprocessing
Symmetric multiprocessing or shared-memory multiprocessing (SMP) involves a multiprocessor computer hardware and software architecture where two or more identical processors are connected to a single, shared main memory, have full access to all input and output devices, and are controlled by a single operating system instance that treats all processors equally, reserving none for special purposes. Most multiprocessor systems today use an SMP architecture. In the case of multi-core processors, the SMP architecture applies to the cores, treating them as separate processors. Professor John D. Kubiatowicz considers traditionally SMP systems to contain processors without caches. Culler and Pal-Singh in their 1998 book "Parallel Computer Architecture: A Hardware/Software Approach" mention: "The term SMP is widely used but causes a bit of confusion. ..The more precise description of what is intended by SMP is a shared memory multiprocessor where the cost of accessing a memory location ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Shared-nothing Architecture
A shared-nothing architecture (SN) is a distributed computing architecture in which each update request is satisfied by a single node (processor/memory/storage unit) in a computer cluster. The intent is to eliminate contention among nodes. Nodes do not share (independently access) the same memory or storage. One alternative architecture is shared everything, in which requests are satisfied by arbitrary combinations of nodes. This may introduce contention, as multiple nodes may seek to update the same data at the same time. SN eliminates single points of failure, allowing the overall system to continue operating despite failures in individual nodes and allowing individual nodes to upgrade without a system-wide shutdown. A SN system can scale simply by adding nodes, since no central resource bottlenecks the system. In databases, a term for the part of a database on a single node is a ''shard''. A SN system typically partitions its data among many nodes. A refinement is to replicate ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Process-oriented Programming
Process-oriented programming is a programming paradigm that separates the concerns of data structures and the concurrent processes that act upon them. The data structures in this case are typically persistent, complex, and large scale - the subject of general purpose applications, as opposed to specialized processing of specialized data sets seen in high productivity applications (HPC). The model allows the creation of large scale applications that partially share common data sets. Programs are functionally decomposed into parallel processes that create and act upon logically shared data. The paradigm was originally invented for parallel computers in the 1980s, especially computers built with transputer microprocessors by INMOS, or similar architectures. Occam was an early process-oriented language developed for the Transputer. Some derivations have evolved from the message passing paradigm of Occam to enable uniform efficiency when porting applications between distributed memory ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Parallel Computing
Parallel computing is a type of computation in which many calculations or processes are carried out simultaneously. Large problems can often be divided into smaller ones, which can then be solved at the same time. There are several different forms of parallel computing: bit-level, instruction-level, data, and task parallelism. Parallelism has long been employed in high-performance computing, but has gained broader interest due to the physical constraints preventing frequency scaling.S.V. Adve ''et al.'' (November 2008)"Parallel Computing Research at Illinois: The UPCRC Agenda" (PDF). Parallel@Illinois, University of Illinois at Urbana-Champaign. "The main techniques for these performance benefits—increased clock frequency and smarter but increasingly complex architectures—are now hitting the so-called power wall. The computer industry has accepted that future performance increases must largely come from increasing the number of processors (or cores) on a die, rather than m ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Embarrassingly Parallel
In parallel computing, an embarrassingly parallel workload or problem (also called embarrassingly parallelizable, perfectly parallel, delightfully parallel or pleasingly parallel) is one where little or no effort is needed to separate the problem into a number of parallel tasks. This is often the case where there is little or no dependency or need for communication between those parallel tasks, or for results between them.Section 1.4.4 of: Thus, these are different from distributed computing problems that need communication between tasks, especially communication of intermediate results. They are easy to perform on server farms which lack the special infrastructure used in a true supercomputer cluster. They are thus well suited to large, Internet-based volunteer computing platforms such as BOINC, and do not suffer from parallel slowdown. The opposite of embarrassingly parallel problems are inherently serial problems, which cannot be parallelized at all. A common example of an emb ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Multiprocessing
Multiprocessing is the use of two or more central processing units (CPUs) within a single computer system. The term also refers to the ability of a system to support more than one processor or the ability to allocate tasks between them. There are many variations on this basic theme, and the definition of multiprocessing can vary with context, mostly as a function of how CPUs are defined ( multiple cores on one die, multiple dies in one package, multiple packages in one system unit, etc.). According to some on-line dictionaries, a multiprocessor is a computer system having two or more processing units (multiple processors) each sharing main memory and peripherals, in order to simultaneously process programs. A 2009 textbook defined multiprocessor system similarly, but noting that the processors may share "some or all of the system’s memory and I/O facilities"; it also gave tightly coupled system as a synonymous term. At the operating system level, ''multiprocessing'' is som ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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