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Data Parallel Computation
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 note ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Sequential Vs
In mathematics, a sequence is an enumerated collection of objects in which repetitions are allowed and order matters. Like a set, it contains members (also called ''elements'', or ''terms''). The number of elements (possibly 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 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 number 1 at two different positions, is a valid sequence. Sequences can be ''finite'', as in these examples, or ''infinit ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
SPMD
In computing, single program, multiple data (SPMD) is a technique employed to achieve parallelism; it is a subcategory of MIMD. Tasks are split up and run simultaneously on multiple processors with different input in order to obtain results faster. SPMD is the most common style of parallel programming. It is also a prerequisite for research concepts such as active messages and distributed shared memory. SPMD vs SIMD In SPMD, multiple autonomous processors simultaneously execute the same program at independent points, rather than in the lockstep that SIMD or SIMT imposes on different data. With SPMD, tasks can be executed on general purpose CPUs; SIMD requires vector processors to manipulate data streams. Note that the two are not mutually exclusive. Distributed memory SPMD usually refers to message passing programming on distributed memory computer architectures. A distributed memory computer consists of a collection of independent computers, called nodes. Each node starts ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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 ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Thread Level 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 an ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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 paralleli ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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] |
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] |
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] |
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] |
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. A ... [...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] |
Shared Memory
In computer science, shared memory is memory that may be simultaneously accessed by multiple programs with an intent to provide communication among them or avoid redundant copies. Shared memory is an efficient means of passing data between programs. Depending on context, programs may run on a single processor or on multiple separate processors. Using memory for communication inside a single program, e.g. among its multiple threads, is also referred to as shared memory. In hardware In computer hardware, ''shared memory'' refers to a (typically large) block of random access memory (RAM) that can be accessed by several different central processing units (CPUs) in a multiprocessor computer system. Shared memory systems may use: * uniform memory access (UMA): all the processors share the physical memory uniformly; * non-uniform memory access (NUMA): memory access time depends on the memory location relative to a processor; * cache-only memory architecture (COMA): the local memo ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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