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Data-parallelism
Data parallelism is parallelization across multiple processors in parallel computing environments. It focuses on distributing the data across different nodes, which operate on the data in parallel. It can be applied on regular data structures like arrays and matrices by working on each element in parallel. It contrasts to task parallelism as another form of parallelism. A data parallel job on an array of ''n'' elements can be divided equally among all the processors. Let us assume we want to sum all the elements of the given array and the time for a single addition operation is Ta time units. In the case of sequential execution, the time taken by the process will be ''n''×Ta time units as it sums up all the elements of an array. On the other hand, if we execute this job as a data parallel job on 4 processors the time taken would reduce to (''n''/4)×Ta + merging overhead time units. Parallel execution results in a speedup of 4 over sequential execution. One important thing to not ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Task Parallelism
Task parallelism (also known as function parallelism and control parallelism) is a form of parallelization of computer code across multiple processors in parallel computing environments. Task parallelism focuses on distributing tasks—concurrently performed by processes or threads—across different processors. In contrast to data parallelism which involves running the same task on different components of data, task parallelism is distinguished by running many different tasks at the same time on the same data. A common type of task parallelism is pipelining, which consists of moving a single set of data through a series of separate tasks where each task can execute independently of the others. Description In a multiprocessor system, task parallelism is achieved when each processor executes a different thread (or process) on the same or different data. The threads may execute the same or different code. In the general case, different execution threads communicate with one ano ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Graphics Processing Unit
A graphics processing unit (GPU) is a specialized electronic circuit designed to manipulate and alter memory to accelerate the creation of images in a frame buffer intended for output to a display device. GPUs are used in embedded systems, mobile phones, personal computers, workstations, and game consoles. Modern GPUs are efficient at manipulating computer graphics and image processing. Their parallel structure makes them more efficient than general-purpose central processing units (CPUs) for algorithms that process large blocks of data in parallel. In a personal computer, a GPU can be present on a video card or embedded on the motherboard. In some CPUs, they are embedded on the CPU die. In the 1970s, the term "GPU" originally stood for ''graphics processor unit'' and described a programmable processing unit independently working from the CPU and responsible for graphics manipulation and output. Later, in 1994, Sony used the term (now standing for ''graphics processing ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Sequential Vs
In mathematics, a sequence is an enumerated collection of mathematical object, objects in which repetitions are allowed and order theory, order matters. Like a Set (mathematics), set, it contains Element (mathematics), members (also called ''elements'', or ''terms''). The number of elements (possibly infinite number, infinite) is called the ''length'' of the sequence. Unlike a set, the same elements can appear multiple times at different positions in a sequence, and unlike a set, the order does matter. Formally, a sequence can be defined as a function (mathematics), function from natural numbers (the positions of elements in the sequence) to the elements at each position. The notion of a sequence can be generalized to an indexed family, defined as a function from an ''arbitrary'' index set. For example, (M, A, R, Y) is a sequence of letters with the letter 'M' first and 'Y' last. This sequence differs from (A, R, M, Y). Also, the sequence (1, 1, 2, 3, 5, 8), which contains the numb ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Load Balancing (computing)
In computing, load balancing is the process of distributing a set of tasks over a set of resources (computing units), with the aim of making their overall processing more efficient. Load balancing can optimize the response time and avoid unevenly overloading some compute nodes while other compute nodes are left idle. Load balancing is the subject of research in the field of parallel computers. Two main approaches exist: static algorithms, which do not take into account the state of the different machines, and dynamic algorithms, which are usually more general and more efficient but require exchanges of information between the different computing units, at the risk of a loss of efficiency. Problem overview A load-balancing algorithm always tries to answer a specific problem. Among other things, the nature of the tasks, the algorithmic complexity, the hardware architecture on which the algorithms will run as well as required error tolerance, must be taken into account. Therefor ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Parallel Programming Model
In computing, a parallel programming model is an abstraction of parallel computer architecture, with which it is convenient to express algorithms and their composition in programs. The value of a programming model can be judged on its ''generality'': how well a range of different problems can be expressed for a variety of different architectures, and its ''performance'': how efficiently the compiled programs can execute. The implementation of a parallel programming model can take the form of a library invoked from a sequential language, as an extension to an existing language, or as an entirely new language. Consensus around a particular programming model is important because it leads to different parallel computers being built with support for the model, thereby facilitating portability of software. In this sense, programming models are referred to as '' bridging'' between hardware and software.Leslie G. Valiant, "A bridging model for parallel computation", Communications of the A ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Scalable Parallelism
Software is said to exhibit scalable parallelism if it can make use of additional processors to solve larger problems, i.e. this term refers to software for which Gustafson's law holds. Consider a program whose execution time is dominated by one or more loops, each of that updates every element of an array --- for example, the following finite difference heat equation stencil calculation: for t := 0 to T do for i := 1 to N-1 do new(i) := (A(i-1) + A(i) + A(i) + A(i+1)) * .25 // explicit forward-difference with R = 0.25 end for i := 1 to N-1 do A(i) := new(i) end end In the above code, we can execute all iterations of each "i" loop concurrently, i.e., turn each into a parallel loop. In such cases, it is often possible to make effective use of twice as many processors for a problem of array size 2N as for a problem of array size N. As in this example, scalable parallelism is typically a form of data parallelism. This form of parallel ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Instruction Level Parallelism
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. Discussion ILP must not be confused with concurrency. In ILP there is a single specific thread of execution of a process. On the other hand, concurrency involves the assignment of multiple threads to a CPU's core in a strict alternation, or in true parallelism if there are enough CPU cores, ideally one core for each runnable thread. There are two approaches to instruction-level parallelism: hardware and software. Hardware level works upon dynamic parallelism, whereas the software level works on static parallelism. Dynamic parallelism means the processor decides at run time which instructions to execute in parallel, whereas static parallelism means the compiler decides which instructions to execute in parallel. The Pentium processor wor ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Active Message
An Active message (in computing) is a messaging object capable of performing processing on its own. It is a lightweight messaging protocol used to optimize network communications with an emphasis on reducing latency by removing software overheads associated with buffering and providing applications with direct user-level access to the network hardware. This contrasts with traditional computer-based messaging systems in which messages are passive entities with no processing power. Distributed Memory Programming Active messages are communications primitive for exploiting the full performance and flexibility of modern computer interconnects. They are often classified as one of the three main types of distributed memory programming, the other two being data parallel and message passing. The view is that Active Messages are actually a lower-level mechanism that can be used to implement data parallel or message passing efficiently. The basic idea is that each message has a header con ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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RaftLib
RaftLib is a portable parallel processing system that aims to provide extreme performance while increasing programmer productivity. It enables a programmer to assemble a massively parallel program (both local and distributed) using simple iostream-like operators. RaftLib handles threading, memory allocation, memory placement, and auto-parallelization of compute kernels. It enables applications to be constructed from chains of compute kernels forming a task and pipeline parallel compute graph. Programs are authored in C++ (although other language bindings are planned). Example Here is a Hello World example for demonstration purposes: #include #include #include #include class hi : public raft::kernel ; int main( int argc, char **argv ) References External links The RaftLib Project PageRaftLib User WikiProject GitHub RepositoryCPPNow RaftLib Tutorial SessionParallel BZip2 Implementation Using RaftLib {{Parallel computing C++ programming language family ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Threading Building Blocks
oneAPI Threading Building Blocks (oneTBB; formerly Threading Building Blocks or TBB), is a C++ template library developed by Intel for parallel programming on multi-core processors. Using TBB, a computation is broken down into tasks that can run in parallel. The library manages and schedules threads to execute these tasks. Overview A oneTBB program creates, synchronizes, and destroys graphs of dependent tasks according to ''algorithms'', i.e. high-level parallel programming paradigms (a.k.a. Algorithmic Skeletons). Tasks are then executed respecting graph dependencies. This approach groups TBB in a family of techniques for parallel programming aiming to decouple the programming from the particulars of the underlying machine. oneTBB implements work stealing to balance a parallel workload across available processing cores in order to increase core utilization and therefore scaling. Initially, the workload is evenly divided among the available processor cores. If one core compl ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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OpenACC
OpenACC (for ''open accelerators'') is a programming standard for parallel computing developed by Cray, CAPS, Nvidia and PGI. The standard is designed to simplify parallel programming of heterogeneous CPU/ GPU systems. As in OpenMP, the programmer can annotate C, C++ and Fortran source code to identify the areas that should be accelerated using compiler directives and additional functions. Like OpenMP 4.0 and newer, OpenACC can target both the CPU and GPU architectures and launch computational code on them. OpenACC members have worked as members of the OpenMP standard group to merge into OpenMP specification to create a common specification which extends OpenMP to support accelerators in a future release of OpenMP. These efforts resulted in a technical report for comment and discussion timed to include the annual Supercomputing Conference (November 2012, Salt Lake City) and to address non-Nvidia accelerator support with input from hardware vendors who participate in OpenMP ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |