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2 example of anti dependence 3 3 example of output dependence 3 4 example of input dependence 4 dependence in loops toggle dependence in loops subsection 4 1 loop carried vs loop independent dependence 4 2 loop carried dependence graph 5 types toggle types subsection 5 1 distributed loop 5 2 doall parallelism 5 3 doacross parallelism 5 4 dopipe parallelism 6 see also 7 references toggle the table of contents loop level parallelism 1 language русский 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 loop level parallelism is a form of parallelism in software programming that is concerned with extracting parallel tasks from loops the opportunity for loop level parallelism often arises in computing programs where data is stored in random access data structures where a sequential program will iterate over the data structure and operate on indices one at a time a program exploiting loop level parallelism will use multiple threads or processes which operate on some or all of the indices at the same time such parallelism provides a speedup to overall execution time of the program typically in line with amdahl s law description edit for simple loops where each iteration is independent of the others loop level parallelism can be embarrassingly parallel as parallelizing only requires assigning a process to handle each iteration however many algorithms are designed to run sequentially and fail when parallel processes race due to dependence within the code sequential algorithms are sometimes applicable to parallel contexts with slight modification usually though they require process synchronization synchronization can be either implicit via message passing or explicit via synchronization primitives like semaphores example edit consider the following code operating on a list l where l size n for int i 0 i n i l i 10 s1 each iteration of the loop takes the value from the current index of l and increments it by 10 if statement s1 takes t time to execute then the loop takes time n t to execute sequentially ignoring time taken by loop constructs now consider a system with p processors where p n if n threads run in parallel the time to execute all n steps is reduced to t less simple cases produce inconsistent i e non serializable outcomes consider the following loop operating on the same list l for int i 0 i n i l i l i 1 10 s1 each iteration sets the current index to be the value of the previous plus ten when run sequentially each iteration is guaranteed that the previous iteration will already have the correct value with multiple threads process scheduling and other considerations prevent the execution order from guaranteeing an iteration will execute only after its dependence is met it very well may happen before leading to unexpected results serializability can be restored by adding synchronization to preserve the dependence on previous iterations dependencies in code edit there are several types of dependences that can be found within code 1 2 type notation description true flow dependence s1 t s2 a true dependence between s1 and s2 means that s1 writes to a location later read from by s2 anti dependence s1 a s2 an anti dependence between s1 and s2 means that s1 reads from a location later written to by s2 output dependence s1 o s2 an output dependence between s1 and s2 means that s1 and s2 write to the same location input dependence s1 i s2 an input dependence between s1 and s2 means that s1 and s2 read from the same location in order to preserve the sequential behaviour of a loop when run in parallel true dependence must be preserved anti dependence and output dependence can be dealt with by giving each process its own copy of variables known as privatization 1 example of true dependence edit int a b s1 a 2 s2 b a 40 s3 s2 t s3 meaning that s2 has a true dependence on s3 because s2 writes to the variable a which s3 reads from example of anti dependence edit int a b 40 s1 a b 38 s2 b 1 s3 s2 a s3 meaning that s2 has an anti dependence on s3 because s2 reads from the variable b before s3 writes to it example of output dependence edit int a b 40 s1 a b 38 s2 a 2 s3 s2 o s3 meaning that s2 has an output dependence on s3 because both write to the variable a example of input dependence edit int a b c 2 s1 a c 1 s2 b c 1 s3 s2 i s3 meaning that s2 has an input dependence on s3 because s2 and s3 both read from variable c dependence in loops edit loop carried vs loop independent dependence edit loops can have two types of dependence loop carried dependence loop independent dependence in loop independent dependence loops have inter iteration dependence but do not have dependence between iterations each iteration may be treated as a block and performed in parallel without other synchronization efforts in the following example code used for swapping the values of two array of length n there is a loop independent dependence of s1 t s3 for int i 1 i n i tmp a i s1 a i b i s2 b i tmp s3 in loop carried dependence statements in an iteration of a loop depend on statements in another iteration of the loop loop carried dependence uses a modified version of the dependence notation seen earlier example of loop carried dependence where s1 i t s1 i 1 where i indicates the current iteration and i 1 indicates the next iteration for int i 1 i n i a i a i 1 1 s1 loop carried dependence graph edit a loop carried dependence graph graphically shows the loop carried dependencies between iterations each iteration is listed as a node on the graph and directed edges show the true anti and output dependencies between each iteration types edit there are a variety of methodologies for parallelizing loops distributed loop doall parallelism doacross parallelism helix 3 dopipe parallelism each implementation varies slightly in how threads synchronize if at all in addition parallel tasks must somehow be mapped to a process these tasks can either be allocated statically or dynamically research has shown that load balancing can be better achieved through some dynamic allocation algorithms than when done statically 4 the process of parallelizing a sequential program can be broken down into the following discrete steps 1 each concrete loop parallelization below implicitly performs them 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 distributed loop edit when a loop has a loop carried dependence one way to parallelize it is to distribute the loop into several different loops statements that are not dependent on each other are separated so that these distributed loops can be executed in parallel for example consider the following code for int i 1 i n i a i a i 1 b i s1 c i d i s2 the loop has a loop carried dependence s1 i t s1 i 1 but s2 and s1 do not have a loop independent dependence so we can rewrite the code as follows loop1 for int i 1 i n i a i a i 1 b i s1 loop2 for int i 1 i n i c i d i s2 note that now loop1 and loop2 can be executed in parallel instead of single instruction being performed in parallel on different data as in data level parallelism here different loops perform different tasks on different data let s say the time of execution of s1 and s2 be t s 1 displaystyle t_ s_ 1 and t s 2 displaystyle t_ s_ 2 then the execution time for sequential form of above code is n t s 1 t s 2 displaystyle n cdot t_ s_ 1 t_ s_ 2 now because we split the two statements and put them in two different loops gives us an execution time of n t s 1 t s 2 displaystyle n cdot t_ s_ 1 t_ s_ 2 we call this type of parallelism either function or task parallelism doall parallelism edit doall parallelism exists when statements within a loop can be executed independently situations where there is no loop carried dependence 1 for example the following code does not read from the array a and does not update the arrays b c no iterations have a dependence on any other iteration for int i 0 i n i a i b i c i s1 let s say the time of one execution of s1 be t s 1 displaystyle t_ s_ 1 then the execution time for sequential form of above code is n t s 1 displaystyle n cdot t_ s_ 1 now because doall parallelism exists when all iterations are independent speed up may be achieved by executing all iterations in parallel which gives us an execution time of t s 1 displaystyle t_ s_ 1 which is the time taken for one iteration in sequential execution the following example using a simplified pseudo code shows how a loop might be parallelized to execute each iteration independently beginparallelism for int i 0 i n i a i b i c i s1 endparallelism block doacross parallelism edit doacross parallelism exists where iterations of a loop are parallelized by extracting calculations that can be performed independently and running them simultaneously 5 synchronization exists to enforce loop carried dependence consider the following synchronous loop with dependence s1 i t s1 i 1 for int i 1 i n i a i a i 1 b i 1 each loop iteration performs two actions calculate a i 1 b i 1 assign the value to a i calculating the value a i 1 b i 1 and then performing the assignment can be decomposed into two lines statements s1 and s2 int tmp b i 1 s1 a i a i 1 tmp s2 the first line int tmp b i 1 has no loop carried dependence the loop can then be parallelized by computing the temp value in parallel and then synchronizing the assignment to a i post 0 for int i 1 i n i int tmp b i 1 s1 wait i 1 a i a i 1 tmp s2 post i let s say the time of execution of s1 and s2 be t s 1 displaystyle t_ s_ 1 and t s 2 displaystyle t_ s_ 2 then the execution time for sequential form of above code is n t s 1 t s 2 displaystyle n cdot t_ s_ 1 t_ s_ 2 now because doacross parallelism exists speed up may be achieved by executing iterations in a pipelined fashion which gives us an execution time of t s 1 n t s 2 displaystyle t_ s_ 1 n cdot t_ s_ 2 dopipe parallelism edit dopipe parallelism implements pipelined parallelism for loop carried dependence where a loop iteration is distributed over multiple synchronized loops 1 the goal of dopipe is to act like an assembly line where one stage is started as soon as there is sufficient data available for it from the previous stage 6 consider the following synchronous code with dependence s1 i t s1 i 1 for int i 1 i n i a i a i 1 b i s1 c i a i s2 s1 must be executed sequentially but s2 has no loop carried dependence s2 could be executed in parallel using doall parallelism after performing all calculations needed by s1 in series however the speedup is limited if this is done a better approach is to parallelize such that the s2 corresponding to each s1 executes when said s1 is finished implementing pipelined parallelism results in the following set of loops where the second loop may execute for an index as soon as the first loop has finished its corresponding index for int i 1 i n i a i a i 1 b i s1 post i for int i 1 i n i wait i c i a i s2 let s say the time of execution of s1 and s2 be t s 1 displaystyle t_ s_ 1 and t s 2 displaystyle t_ s_ 2 then the execution time for sequential form of above code is n t s 1 t s 2 displaystyle n cdot t_ s_ 1 t_ s_ 2 now because dopipe parallelism exists speed up may be achieved by executing iterations in a pipelined fashion which gives us an execution time of n t s 1 n p t s 2 displaystyle n cdot t_ s_ 1 frac n p cdot t_ s_ 2 where p is the number of processor in parallel see also edit data parallelism doacross parallelism task parallelism parallelism using different types of memory models like shared and distributed and message passing references edit 1 2 3 4 5 solihin yan 2016 fundamentals of parallel architecture boca raton fl crc press isbn 978 1 4822 1118 4 goff gina 1991 practical dependence testing proceedings of the acm sigplan 1991 conference on programming language design and implementation pldi 91 pp 15 29 doi 10 1145 113445 113448 isbn 0897914287 s2cid 2357293 murphy niall discovering and exploiting parallelism in doacross loops pdf university of cambridge retrieved 10 september 2016 kavi krishna parallelization of doall and doacross loops a survey cite journal cite journal requires journal help unnikrishnan priya 2012 a practical approach to doacross parallelization euro par 2012 parallel processing lecture notes in computer science vol 7484 pp 219 231 doi 10 1007 978 3 642 32820 6_23 isbn 978 3 642 32819 0 s2cid 18571258 dopipe an effective approach to parallelize simulation pdf intel retrieved 13 september 2016 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 heterogenous speculative spmt preemptive cooperative clustered multi thread cmt hardware scout theory pram model pem model analysis of parallel algorithms amdahl s law gustafson s law cost efficiency karp flatt metric slowdown speedup elements process thread fiber instruction window array coordination multiprocessing memory coherence cache coherence cache invalidation barrier synchronization application checkpointing programming stream processing dataflow programming models implicit parallelism explicit parallelism concurrency non blocking algorithm hardware flynn s taxonomy sisd simd array processing simt pipelined processing associative processing misd mimd dataflow architecture pipelined processor superscalar processor vector processor multiprocessor symmetric asymmetric memory shared distributed distributed shared uma numa coma massively parallel computer computer cluster beowulf cluster grid computer hardware acceleration apis ateji px boost chapel hpx charm cilk coarray fortran cuda dryad c amp global arrays gpuopen mpi openmp opencl openhmpp openacc parallel extensions pvm pthreads raftlib rocm upc tbb zpl problems automatic parallelization cache stampede deadlock deterministic algorithm embarrassingly parallel parallel slowdown race condition software lockout scalability starvation category parallel computing retrieved from https en wikipedia org w index php title loop level_parallelism oldid 1318035675 category parallel computing hidden categories cs1 errors missing periodical articles with example pseudocode this page was last edited on 21 october 2025 at 14 55 utc page was rendered with 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