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about wikipedia contact us contribute help learn to edit community portal recent changes upload file special pages search search appearance donate create account log in personal tools donate create account log in contents move to sidebar hide top 1 history 2 description 3 example 4 steps to parallelization 5 data parallelism vs task parallelism 6 data parallelism vs model parallelism 7 mixed data and task parallelism 8 data parallel programming environments 9 applications toggle applications subsection 9 1 data intensive computing 10 see also 11 notes 12 references toggle the table of contents data parallelism 12 languages العربية català español français 日本語 한국어 română සිංහල simple english српски srpski українська 中文 edit links article talk english read edit view history tools tools move to sidebar hide actions read edit view history general what links here related changes upload file permanent link page information cite this page get shortened url switch to legacy parser print export download as pdf printable version in other projects wikidata item appearance move to sidebar hide from wikipedia the free encyclopedia parallelization across multiple processors in parallel computing environments sequential vs data parallel job execution 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 the locality of data references plays an important part in evaluating the performance of a data parallel programming model locality of data depends on the memory accesses performed by the program as well as the size of the cache history edit exploitation of the concept of data parallelism started in 1960s with the development of the solomon machine 1 the solomon machine also called a vector processor was developed to expedite the performance of mathematical operations by working on a large data array operating on multiple data in consecutive time steps concurrency of data operations was also exploited by operating on multiple data at the same time using a single instruction these processors were called array processors 2 in the 1980s the term was introduced 3 to describe this programming style which was widely used to program connection machines in data parallel languages like c today data parallelism is best exemplified in graphics processing units gpus which use both the techniques of operating on multiple data in space and time using a single instruction most data parallel hardware supports only a fixed number of parallel levels often only one this means that within a parallel operation it is not possible to launch more parallel operations recursively and means that programmers cannot make use of nested hardware parallelism the programming language nesl was an early effort at implementing a nested data parallel programming model on flat parallel machines and in particular introduced the flattening transformation that transforms nested data parallelism to flat data parallelism this work was continued by other languages such as data parallel haskell and futhark although arbitrary nested data parallelism is not widely available in current data parallel programming languages description edit in a multiprocessor system executing a single set of instructions simd data parallelism is achieved when each processor performs the same task on different distributed data in some situations a single execution thread controls operations on all the data in others different threads control the operation but they execute the same code for instance consider matrix multiplication and addition in a sequential manner as discussed in the example example edit below is the sequential pseudo code for multiplication and addition of two matrices where the result is stored in the matrix c the pseudo code for multiplication calculates the dot product of two matrices a b and stores the result into the output matrix c if the following programs were executed sequentially the time taken to calculate the result would be of the o n 3 displaystyle o n 3 assuming row lengths and column lengths of both matrices are n and o n displaystyle o n for multiplication and addition respectively matrix multiplication for int i 0 i a rowlength i for int k 0 k b columnlength k int sum 0 for int j 0 j a columnlength j sum a i j b j k c i k sum array addition for int i 0 i c size i c i a i b i we can exploit data parallelism in the preceding code to execute it faster as the arithmetic is loop independent parallelization of the matrix multiplication code is achieved by using openmp an openmp directive omp parallel for instructs the compiler to execute the code in the for loop in parallel for multiplication we can divide matrix a and b into blocks along rows and columns respectively this allows us to calculate every element in matrix c individually thereby making the task parallel for example a m x n dot b n x k can be finished in o n displaystyle o n instead of o m n k displaystyle o m n k when executed in parallel using m k processors data parallelism in matrix multiplication matrix multiplication in parallel pragma omp parallel for schedule dynamic 1 collapse 2 for int i 0 i a rowlength i for int k 0 k b columnlength k int sum 0 for int j 0 j a columnlength j sum a i j b j k c i k sum it can be observed from the example that a lot of processors will be required as the matrix sizes keep on increasing keeping the execution time low is the priority but as the matrix size increases we are faced with other constraints like complexity of such a system and its associated costs therefore constraining the number of processors in the system we can still apply the same principle and divide the data into bigger chunks to calculate the product of two matrices 4 for addition of arrays in a data parallel implementation let s assume a more modest system with two central processing units cpu a and b cpu a could add all elements from the top half of the arrays while cpu b could add all elements from the bottom half of the arrays since the two processors work in parallel the job of performing array addition would take one half the time of performing the same operation in serial using one cpu alone the program expressed in pseudocode below which applies some arbitrary operation foo on every element in the array d illustrates data parallelism nb 1 if cpu a then lower_limit 1 upper_limit round d length 2 else if cpu b then lower_limit round d length 2 1 upper_limit d length for i from lower_limit to upper_limit by 1 do foo d i in an spmd system executed on 2 processor system both cpus will execute the code data parallelism emphasizes the distributed parallel nature of the data as opposed to the processing task parallelism most real programs fall somewhere on a continuum between task parallelism and data parallelism steps to parallelization edit the process of parallelizing a sequential program can be broken down into four discrete steps 5 type description decomposition the program is broken down into tasks the smallest exploitable unit of concurrence assignment tasks are assigned to processes orchestration data access communication and synchronization of processes mapping processes are bound to processors data parallelism vs task parallelism edit data parallelism task parallelism same operations are performed on different subsets of same data different operations are performed on the same or different data synchronous computation asynchronous computation speedup is more as there is only one execution thread operating on all sets of data speedup is less as each processor will execute a different thread or process on the same or different set of data amount of parallelization is proportional to the input data size amount of parallelization is proportional to the number of independent tasks to be performed designed for optimum load balance on multi processor system load balancing depends on the availability of the hardware and scheduling algorithms like static and dynamic scheduling data parallelism vs model parallelism edit data parallelism model parallelism same model is used for every thread but the data given to each of them is divided and shared same data is used for every thread and model is split among threads it is fast for small networks but very slow for large networks since large amounts of data needs to be transferred between processors all at once it is slow for small networks and fast for large networks data parallelism is ideally used in array and matrix computations and convolutional neural networks model parallelism finds its applications in deep learning 6 mixed data and task parallelism edit data and task parallelism can be simultaneously implemented by combining them together for the same application this is called mixed data and task parallelism mixed parallelism requires sophisticated scheduling algorithms and software support it is the best kind of parallelism when communication is slow and number of processors is large 7 mixed data and task parallelism has many applications it is particularly used in the following applications mixed data and task parallelism finds applications in the global climate modeling large data parallel computations are performed by creating grids of data representing earth s atmosphere and oceans and task parallelism is employed for simulating the function and model of the physical processes in timing based circuit simulation the data is divided among different sub circuits and parallelism is achieved with orchestration from the tasks data parallel programming environments edit a variety of data parallel programming environments are available today most widely used of which are message passing interface it is a cross platform message passing programming interface for parallel computers it defines the semantics of library functions to allow users to write portable message passing programs in c c and fortran openmp 8 it s an application programming interface api which supports shared memory programming models on multiple platforms of multiprocessor systems since version 4 5 openmp is also able to target devices other than typical cpus it can program fpgas dsps gpus and more it is not confined to gpus like openacc cuda and openacc cuda and openacc respectively are parallel computing api platforms designed to allow a software engineer to utilize gpus computational units for general purpose processing threading building blocks and raftlib both open source programming environments that enable mixed data task parallelism in c c environments across heterogeneous resources applications edit data parallelism finds its applications in a variety of fields ranging from physics chemistry biology material sciences to signal processing sciences imply data parallelism for simulating models like molecular dynamics 9 sequence analysis of genome data 10 and other physical phenomenon driving forces in signal processing for data parallelism are video encoding image and graphics processing wireless communications 11 to name a few data intensive computing edit this section is an excerpt from data intensive computing edit data intensive computing is a class of parallel computing applications which use a data parallel approach to process large volumes of data typically terabytes or petabytes in size and typically referred to as big data computing applications that devote most of their execution time to computational requirements are deemed compute intensive whereas applications are deemed data intensive if they require large volumes of data and devote most of their processing time to input output and manipulation of data 12 see also edit active message instruction level parallelism parallel programming model prefix sum scalable parallelism segmented scan thread level parallelism notes edit some input data e g when d length evaluates to 1 and round rounds towards zero this is just an example there are no requirements on what type of rounding is used will lead to lower_limit being greater than upper_limit it s assumed that the loop will exit immediately i e zero iterations will occur when this happens references edit the solomon computer simd vector gpu pdf retrieved 2016 09 07 hillis w daniel and steele guy l data parallel algorithms communications of the acm december 1986 barney blaise introduction to parallel computing computing llnl gov archived from the original on 2013 06 10 retrieved 2016 09 07 solihin yan 2016 fundamentals of parallel architecture boca raton fl crc press isbn 978 1 4822 1118 4 how to parallelize deep learning on gpus part 2 2 model parallelism tim dettmers 2014 11 09 retrieved 2016 09 13 the netlib pdf openmp org openmp org archived from the original on 2016 09 05 retrieved 2016 09 07 boyer l l pawley g s 1988 10 01 molecular dynamics of clusters of particles interacting with pairwise forces using a massively parallel computer journal of computational physics 78 2 405 423 bibcode 1988jcoph 78 405b doi 10 1016 0021 9991 88 90057 5 yap t k frieder o martino r l 1998 parallel computation in biological sequence analysis ieee transactions on parallel and distributed systems 9 3 283 294 bibcode 1998itpds 9 283y doi 10 1109 71 674320 singh h lee ming hau lu guangming kurdahi f j bagherzadeh n filho e m chaves 2000 06 01 morphosys an integrated reconfigurable system for data parallel and computation intensive applications ieee transactions on computers 49 5 465 481 bibcode 2000itcmp 49 465s doi 10 1109 12 859540 issn 0018 9340 handbook of cloud computing data intensive technologies for cloud computing by a m middleton handbook of cloud computing springer 2010 hillis w daniel and steele guy l data parallel algorithms communications of the acm december 1986 blelloch guy e vector models for data parallel computing mit press 1990 isbn 0 262 02313 x v t e parallel computing general distributed computing parallel computing parallel algorithm massively parallel cloud computing high performance computing multiprocessing manycore processor gpgpu computer network systolic array levels bit instruction thread task data memory loop pipeline multithreading temporal simultaneous smt simultaneous and heterogeno...