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multiple instruction single data wikipedia jump to content main menu main menu move to sidebar hide navigation main page contents current events random article 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 systolic arrays toggle systolic arrays subsection 1 1 classification controversy 2 footnotes toggle the table of contents multiple instruction single data 16 languages العربية català deutsch español فارسی suomi français italiano 日本語 한국어 polski português русский 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 parallel computing architecture this article is written like a personal reflection personal essay or argumentative essay that states a wikipedia editor s personal feelings or presents an original argument about a topic please help improve it by rewriting it in an encyclopedic style april 2017 learn how and when to remove this message this article may require cleanup to meet wikipedia s quality standards the specific problem is subsubsection classification controversy contains near duplicate content of systolic array classification controversy that may need consolidation please help improve this article if you can july 2025 learn how and when to remove this message flynn s taxonomy single data stream sisd misd multiple data streams simd mimd simd subcategories 1 array processing simt pipelined processing packed simd associative processing predicated masked simd see also spmd mpmd in computing multiple instruction single data misd is a type of parallel computing architecture where many functional units perform different operations on the same data pipeline architectures belong to this type although they arguably differ in that the data is different after processing by each stage in the pipeline fault tolerance executing the same instructions redundantly in order to detect and mask errors in a manner known as task replication may be considered to belong to this type applications for this architecture are much less common than mimd and simd as the latter two are often more appropriate for common data parallel techniques specifically they allow better scaling and use of computational resources however one prominent example of misd in computing are the space shuttle flight control computers 2 systolic arrays edit systolic arrays wavefront processors first described by h t kung and charles e leiserson are an example of misd architecture in a typical systolic array parallel input data flows through a network of hard wired processor nodes resembling the human brain which combine process merge or sort the input data into a derived result systolic arrays are often hard wired for a specific operation such as multiply and accumulate to perform massively parallel integration convolution correlation matrix multiplication or data sorting tasks a systolic array typically consists of a large monolithic network of primitive computing nodes which can be hardwired or software configured for a specific application the nodes are usually fixed and identical while the interconnect is programmable more general wavefront processors by contrast employ sophisticated and individually programmable nodes which may or may not be monolithic depending on the array size and design parameters because the wave like propagation of data through a systolic array resembles the pulse of the human circulatory system the name systolic was coined from medical terminology a significant benefit of systolic arrays is that all operand data and partial results are contained within passing through the processor array there is no need to access external buses main memory or internal caches during each operation as with standard sequential machines the sequential limits on parallel performance dictated by amdahl s law also do not apply in the same way because data dependencies are implicitly handled by the programmable node interconnect therefore systolic arrays are extremely good at artificial intelligence image processing pattern recognition computer vision and other tasks that animal brains do exceptionally well wavefront processors in general can also be very good at machine learning by implementing self configuring neural nets in hardware classification controversy edit while systolic arrays are officially classified as misd their classification is somewhat problematic because the input is typically a vector of independent values the systolic array is not sisd since these input values are merged and combined into the result s and do not maintain their independence as they would in a simd vector processing unit the array cannot be classified as such consequently the array cannot be classified as a mimd either since mimd can be viewed as a mere collection of smaller sisd and simd machines finally because the data swarm is transformed as it passes through the array from node to node the multiple nodes are not operating on the same data which makes the misd classification a misnomer the other reason why a systolic array should not qualify as a misd is the same as the one which disqualifies it from the sisd category the input data is typically a vector not a single data value although one could argue that any given input vector is a single dataset the above notwithstanding systolic arrays are often offered as a classic example of misd architecture in textbooks on parallel computing and in the engineering class if the array is viewed from the outside as atomic it should perhaps be classified as sfmudmer single function multiple data merged result s 3 4 5 6 footnotes edit flynn michael j september 1972 some computer organizations and their effectiveness pdf ieee transactions on computers c 21 9 948 960 doi 10 1109 tc 1972 5009071 spector a gifford d september 1984 the space shuttle primary computer system communications of the acm 27 9 872 900 doi 10 1145 358234 358246 s2cid 39724471 michael j flynn kevin w rudd parallel architectures crc press 1996 quinn michael j parallel programming in c with mpi and openmp boston mcgraw hill 2004 ibaroudene djaffer parallel processing eg6370g chapter 1 motivation and history st mary s university san antonio tx spring 2008 null linda lobur julia 2006 the essentials of computer organization and architecture 468 jones and bartlett cite book cs1 maint location link v t e processor technologies models abstract machine stored program computer finite state machine with datapath hierarchical deterministic finite automaton queue automaton cellular automaton quantum cellular automaton turing machine alternating turing machine universal post turing quantum nondeterministic turing machine probabilistic turing machine hypercomputation zeno machine belt machine stack machine register machines counter pointer random access random access stored program architecture microarchitecture von neumann harvard modified dataflow transport triggered cellular endianness memory access numa huma load store register memory cache hierarchy memory hierarchy virtual memory secondary storage heterogeneous fabric multiprocessing cognitive neuromorphic instruction set architectures types orthogonal instruction set cisc risc application specific edge trips vliw epic misc oisc nisc zisc visc architecture quantum computing comparison addressing modes instruction sets motorola 68000 series vax pdp 11 x86 arm stanford mips mips mips x power power powerpc power isa clipper architecture sparc superh dec alpha etrax cris m32r unicore itanium openrisc risc v microblaze lmc system 3x0 s 360 s 370 s 390 z architecture tilera isa visc architecture epiphany architecture others execution instruction pipelining pipeline stall operand forwarding classic risc pipeline hazards data dependency structural control false sharing out of order scoreboarding tomasulo s algorithm reservation station re order buffer register renaming wide issue speculative branch prediction memory dependence prediction parallelism level bit bit serial word instruction pipelining scalar superscalar task thread process data vector memory distributed multithreading temporal simultaneous hyperthreading simultaneous and heterogenous speculative preemptive cooperative flynn s taxonomy sisd simd array processing simt pipelined processing associative processing swar misd mimd spmd processor performance transistor count instructions per cycle ipc cycles per instruction cpi instructions per second ips floating point operations per second flops transactions per second tps synaptic updates per second sups performance per watt ppw cache performance metrics computer performance by orders of magnitude types central processing unit cpu graphics processing unit gpu gpgpu vector barrel stream tile processor coprocessor pal asic fpga fpoa cpld multi chip module mcm system in a package sip package on a package pop by application embedded system microprocessor microcontroller mobile ultra low voltage asip soft microprocessor systems on chip system on a chip soc multiprocessor mpsoc cypress psoc network on a chip noc hardware accelerators coprocessor ai accelerator graphics processing unit gpu image processor vision processing unit vpu physics processing unit ppu digital signal processor dsp tensor processing unit tpu secure cryptoprocessor network processor baseband processor word size 1 bit 4 bit 8 bit 12 bit 15 bit 16 bit 24 bit 32 bit 48 bit 64 bit 128 bit 256 bit 512 bit bit slicing others variable core count single core multi core manycore heterogeneous architecture components core cache cpu cache scratchpad memory data cache instruction cache replacement policies coherence bus clock rate clock signal fifo functional units arithmetic logic unit alu address generation unit agu floating point unit fpu memory management unit mmu load store unit translation lookaside buffer tlb branch predictor branch target predictor integrated memory controller imc memory management unit instruction decoder logic combinational sequential glue logic gate quantum array registers processor register status register stack register register file memory buffer memory address register program counter control unit hardwired control unit instruction unit data buffer write buffer microcode rom counter datapath multiplexer demultiplexer adder multiplier cpu binary decoder address decoder sum addressed decoder barrel shifter circuitry integrated circuit 3d mixed signal power management boolean digital analog quantum switch power management pmu apm acpi dynamic frequency scaling dynamic voltage scaling clock gating performance per watt ppw related history of general purpose cpus microprocessor chronology processor design digital electronics hardware security module semiconductor device fabrication tick tock model pin grid array chip carrier 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 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