That sequential pattern of technology development was parallelized with blockchain.
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These applications all need strong parallelized processing, which Nvidia specializes in.
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Many of the workloads they run are, after all, easily parallelized across hundreds or thousands of machines.
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GPUs are already massively parallelized, having to handle huge amounts of data under extremely strict time constraints, so they're a great match for supercomputing rigs.
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Unlike central processing units, however, graphics chips are less versatile in the tasks they can perform and are best suited to running a ton of parallelized workloads.
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Thousands of cell experiments can be parallelized and automated on their lab-on-a-chip system, increasing throughput, precision, economy, and insight that can lead to dramatic innovations in organogenesis, fermentation condition optimization and therapeutic production.
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In the human visual system, the eye itself does rudimentary processing before images are even sent to the brain, and when they do arrive, the task of breaking them down is split apart and parallelized in an amazingly effective manner.
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Intel invested nearly $30 billion last year in R&D with a focus on memory, 5G, and graphical processing units (GPUs), which are seen as the best option for artificial intelligence, machine learning, and any use case needing strong parallelized processing capabilities.
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But those results are achieved with the benefit of supercomputers and parallelized GPUs; who knows how long it takes a state of the art algorithm to look at an image and say, "there are six boats, two cars, a phone and a bush," as well as label their boundaries.
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LU reduction is a special parallelized version of an LU decomposition algorithm, an example can be found in (Guitart 2001). The parallelized version usually distributes the work for a matrix row to a single processor and synchronizes the result with the whole matrix (Escribano 2000).
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Chip-multiprocessors however were expected to be heavily used in all areas of computing such as with parallelized consumer applications.
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The divide-and-conquer algorithm is readily parallelized, and linear algebra computing packages such as LAPACK contain high-quality parallel implementations.
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The need is also non-trivial because large amount of legacy code written over the past few decades needs to be reused and parallelized.
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Each thread has an id attached to it which can be obtained using a function (called `omp_get_thread_num()`). The thread id is an integer, and the primary thread has an id of 0. After the execution of the parallelized code, the threads join back into the primary thread, which continues onward to the end of the program. By default, each thread executes the parallelized section of code independently.
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A new variant of parallelized collision searching using MPI was proposed by Anton Kuznetsov in 2014, which allowed to find a collision in 11 hours on a computing cluster.
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A harmonic- balance and linear microwave analysis tool, named Agile, for microwave circuits is available for download. A parallelized version was also developed, but this version is not available.
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Like CBC mode, changes in the plaintext propagate forever in the ciphertext, and encryption cannot be parallelized. Also like CBC, decryption can be parallelized. CFB, OFB and CTR shares two advantages over CBC mode: the block cipher is only ever used in the encrypting direction, and the message does not need to be padded to a multiple of the cipher block size (though ciphertext stealing can also be used to make padding unnecessary).
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Then Shijie Zhong (U. of Colorado, Boulder) successfully parallelized CitCom using message passing routines on a limited release Intel supercomputer. Zhong then created a spherical version of the code which he named CitComS.
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GCM requires one block cipher operation and one 128-bit multiplication in the Galois field per each block (128 bit) of encrypted and authenticated data. The block cipher operations are easily pipelined or parallelized; the multiplication operations are easily pipelined and can be parallelized with some modest effort (either by parallelizing the actual operation, by adapting Horner's method per the original NIST submission, or both). Intel has added the PCLMULQDQ instruction, highlighting its use for GCM. In 2011, SPARC added the XMULX and XMULXHI instructions, which also perform 64 × 64 bit carry-less multiplication.
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Blelloch, Guy; Gu, Yan; Shun, Julian; and Sun, Yihan. Parallelism in Randomized Incremental Algorithms . SPAA 2016. doi:10.1145/2935764.2935766. proposed another version of incremental algorithm based on rip-and-tent, which is practical and highly parallelized with polylogarithmic span.
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Rockbox also provides support for multicore and asymmetric multiprocessor systems based on ARM, ColdFire, MIPS and SH. Several codecs can be parallelized across 2 CPU cores for increased power efficiency, and the HWCODEC interface allows for dedicated audio decoder DSPs.
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GPGPU is fundamentally a software concept, not a hardware concept; it is a type of algorithm, not a piece of equipment. Specialized equipment designs may, however, even further enhance the efficiency of GPGPU pipelines, which traditionally perform relatively few algorithms on very large amounts of data. Massively parallelized, gigantic-data-level tasks thus may be parallelized even further via specialized setups such as rack computing (many similar, highly tailored machines built into a rack), which adds a third layer many computing units each using many CPUs to correspond to many GPUs. Some Bitcoin "miners" used such setups for high-quantity processing.
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TEBD also offers the possibility of straightforward parallelization due to the factorization of the exponential time-evolution operator using the Suzuki-Trotter expansion. A parallel-TEBD has the same mathematics as its non- parallelized counterpart, the only difference is in the numerical implementation.
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The Lagrangian particle dispersion model FLEXPART- WRF version 3.1 The code was also parallelized with compile-time options for OpenMP and MPI added to the previous default serial mode. Furthermore, an option to use the NetCDF standard format for output was added.
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Further speed-up is gained by the auto- management of the memory of large array objects. Numerical operations are parallelized on multicore systems. Linear algebra routines rely on processor specific optimized versions of LAPACK and BLAS. ILNumerics arrays utilize the unmanaged heap for storing data.
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Chapman & Hall, 2008. and Batcher's bitonic merge sort has an algorithmic complexity of O(log2(n)), all of which have a lower algorithmic time complexity to radix sort on a CREW-PRAM. The fastest known PRAM sorts were described in 1991 by David Powers with a parallelized quicksort that can operate in O(log(n)) time on a CRCW-PRAM with n processors by performing partitioning implicitly, as well as a radixsort that operates using the same trick in O(k), where k is the maximum keylength.David M. W. Powers, Parallelized Quicksort and Radixsort with Optimal Speedup, Proceedings of International Conference on Parallel Computing Technologies. Novosibirsk. 1991.
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Till then FLACS was developed for Unix and Linux platforms. In 2008, however, FLACS v9.0 was released for Microsoft Windows platform. FLACS v9.1 and FLACS-Wind was developed in 2010. A fully parallelized FLACSv10.0 (using OpenMP) with a new solver for incompressible flows was released in 2012.
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In addition, since the sequencing process is not parallelized across regions of the genome, data could be collected and analyzed in real time. These advantages of third generation sequencing may be well-suited in hospital settings where quick and on-site data collection and analysis is demanded.
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Thus an attacker could use an implementation that doesn't require many resources (and can therefore be massively parallelized with limited expense) but runs very slowly, or use an implementation that runs more quickly but has very large memory requirements and is therefore more expensive to parallelize.
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In the classical domain, the Hadamard transform can be computed in n \log n operations (n = 2^m), using the fast Hadamard transform algorithm. In the quantum domain, the Hadamard transform can be computed in O(1) time, as it is a quantum logic gate that can be parallelized.
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Communication between replication agents is via an efficient stream-oriented protocol built on top of regular TCP/IP connections. The replication agent is multi-threaded and in the 11.2.2 release, TimesTen supports parallel replication for increased throughput. The replication workload is automatically parallelized to maximize throughput while still maintaining correctness.
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Test execution can be parameterized and parallelized using profiles. Remote execution in Katalon Studio can be triggered by CI systems via Docker container or command line interface (CLI). From version 7.4.0, users are able to execute test cases from Selenium projects, along with the previous migration from TestNG and JUnit to Katalon Studio.
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It requires a fast interconnect between nodes, so Postgres-XL is not suited to geographically distributed clusters. Larger queries can be split and parallelized between multiple nodes. Individual database tables can be chosen to be fully replicated across the cluster (usually for smaller tables) or sharded between separate nodes (for write scalability).
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It gives an alternative to edge flipping for computing the Delaunay triangles containing a newly inserted vertex. Unfortunately the flipping-based algorithms are generally hard to be parallelized, since adding some certain point (e.g. the center point of a wagon wheel) can lead to up to O(n) consecutive flips. Blelloch et al.
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CBC has been the most commonly used mode of operation. Its main drawbacks are that encryption is sequential (i.e., it cannot be parallelized), and that the message must be padded to a multiple of the cipher block size. One way to handle this last issue is through the method known as ciphertext stealing.
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The Karp–Flatt metric is a measure of parallelization of code in parallel processor systems. This metric exists in addition to Amdahl's law and Gustafson's law as an indication of the extent to which a particular computer code is parallelized. It was proposed by Alan H. Karp and Horace P. Flatt in 1990.
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In 2008 AWR acquired Simulation Technology and Applied Research (STAAR), in Mequon, Wisconsin. STAAR developed proprietary parallelized 3D FEM EM simulation and analysis capability, marketed as Analyst software. AWR was acquired by National Instruments in 2011 for about $58 million. In January 2020 Cadence Design Systems, Inc completed acquisition of AWR Corporation from National Instruments.
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A Givens rotation procedure is used instead which does the equivalent of the sparse Givens matrix multiplication, without the extra work of handling the sparse elements. The Givens rotation procedure is useful in situations where only a relatively few off diagonal elements need to be zeroed, and is more easily parallelized than Householder transformations.
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Hydra is a parallelized network logon cracker. Hydra works by using different approaches of generating possible passwords, such as wordlist attacks, brute- force attacks and others. Hydra is commonly used by penetration testers together with a program named crunch, which is used to generate wordlists. Hydra is then used to test the attacks using the wordlists that crunch created.
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Furthermore, these methods result in a two step manufacturing process. Silica structures are a much more effective method of packing material because they are etched into the channel during its fabrication and is thus the result of a one step manufacturing processes via soft lithography. Silica structures are therefore easier to use in highly parallelized designs than beads or resins.
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VeraCrypt supports parallelized encryption for multi-core systems and, under Microsoft Windows, pipelined read and write operations (a form of asynchronous processing) to reduce the performance hit of encryption and decryption. On processors supporting the AES-NI instruction set, VeraCrypt supports hardware- accelerated AES to further improve performance. On 64-bit CPUs VeraCrypt uses optimized assembly implementation of Twofish and Camellia.
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PLINQ, or Parallel LINQ, parallelizing the execution of queries on objects (LINQ to Objects) and XML data (LINQ to XML). PLINQ is intended for exposing data parallelism by use of queries. Any computation on objects that has been implemented as queries can be parallelized by PLINQ. However, the objects need to implement the `IParallelEnumerable` interface, which is defined by PLINQ itself.
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LIO Architecture LIO implements a modular and extensible architecture around a versatile and highly efficient, parallelized SCSI command processing engine. The SCSI target engine implements the semantics of a SCSI target. The LIO SCSI target engine is independent of specific fabric modules or backstore types. Thus, LIO supports mixing and matching any number of fabrics and backstores at the same time.
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Tests may be serial (one after the other) or parallel (some or all at once) depending on the sophistication of the test environment. A significant goal for agile and other high-productivity software development practices is reducing the time from software design or specification to delivery in production. Highly automated and parallelized test environments are important contributors to rapid software development.
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In fact this theoretically optimal bound can never be reached, because some subtasks cannot be parallelized, and some processors may have to wait a result from another processor. The main complexity problem is thus to design algorithms such that the product of the computation time by the number of processors is as close as possible to the time needed for the same computation on a single processor.
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A harmonic-balance tool, named Agile, for microwave circuits is available for download. A parallelized version was also developed, but this version is not available. Sandia National Labs developed Xyce, a high performance parallel electronic simulator that can perform harmonic balance analysis. The harmonic balance method is also natively supported for general nonlinear multiphysics finite element simulations in the open source c++ FEM library Sparselizard.
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PostgreSQL server is process-based (not threaded), and uses one operating system process per database session. Multiple sessions are automatically spread across all available CPUs by the operating system. Starting with PostgreSQL 9.6, many types of queries can also be parallelized across multiple background worker processes, taking advantage of multiple CPUs or cores. Client applications can use threads and create multiple database connections from each thread.
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After this point, it follows the main line on the other site until a point south of Krymske at where it leaves the course of the main line in southeast direction toward the electrode. The electrode for the terminal at Volgograd is situated northwest of Kamennyy at . It is connected with it by a 24 kilometre long overhead line consisting of two parallelized 2450 mm2 ACSR conductors.
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Written in Scala, GeoMesa is capable of ingesting, indexing, and querying billions of geometry features using a highly parallelized index scheme. GeoMesa builds on top of open source geo (OSG) libraries. It implements the GeoTools DataStore interface providing standardized access to feature collections as well as implementing a GeoServer plugin. Google announced that GeoMesa supported the Google Cloud Bigtable hosted NoSQL service in their release blog post in May 2015.
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In the past some areas in information engineering such as signal processing used analog electronics, but nowadays most information engineering is done with digital computers. Many tasks in information engineering can be parallelized, and so nowadays information engineering is carried out using CPUs, GPUs, and AI accelerators. There has also been interest in using quantum computers for some subfields of information engineering such as machine learning and robotics.
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A black-box fuzzer treats the program as a black box and is unaware of internal program structure. For instance, a random testing tool that generates inputs at random is considered a blackbox fuzzer. Hence, a blackbox fuzzer can execute several hundred inputs per second, can be easily parallelized, and can scale to programs of arbitrary size. However, blackbox fuzzers may only scratch the surface and expose "shallow" bugs.
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Light is emitted from a source such as a vapor lamp. A slit selects a thin strip of light which passes through the collimator where it gets parallelized. The aligned light then passes through the prism in which it is refracted twice (once when entering and once when leaving). Due to the nature of a dispersive element the angle with which light is refracted depends on its wavelength.
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In computer science, join-based tree algorithms are a class of algorithms for self-balancing binary search trees. This framework aims at designing highly- parallelized algorithms for various balanced binary search trees. The algorithmic framework is based on a single operation join. Under this framework, the join operation captures all balancing criteria of different balancing schemes, and all other functions join have generic implementation across different balancing schemes.
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In cryptography and number theory, TWIRL (The Weizmann Institute Relation Locator) is a hypothetical hardware device designed to speed up the sieving step of the general number field sieve integer factorization algorithm. During the sieving step, the algorithm searches for numbers with a certain mathematical relationship. In distributed factoring projects, this is the step that is parallelized to a large number of processors. TWIRL is still a hypothetical device — no implementation has been publicly reported.
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Single-molecule real-time (SMRT) sequencing is a parallelized single molecule DNA sequencing method. Single-molecule real-time sequencing utilizes a zero- mode waveguide (ZMW). A single DNA polymerase enzyme is affixed at the bottom of a ZMW with a single molecule of DNA as a template. The ZMW is a structure that creates an illuminated observation volume that is small enough to observe only a single nucleotide of DNA being incorporated by DNA polymerase.
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Delphi distribution comes as a sequential as well as parallelized codes, runs on Linux, Mac OS X and Microsoft Windows systems and the source code is available in Fortran 95 and C++ programming languages. DELPHI is also implemented into an accessible web-server. DELPHI has also been utilized to build a server that predicts pKa's of biological macromolecules such as proteins, RNAs and DNAs which can be accessed via web. DelPhi v.
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Because variable x is always written to before being used, variable x can be privatized. //Sequential Code: //Swap Function //Assume the variables have already been initialized x = a; a = b; b = x; x = c; c = d; d = x; x = e; e = f; b = x; The block above is the sequential code. Notice that without privatizing the variable "x", the code could not be parallelized. The code below shows what is possible by parallelizing "x".
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As a consequence, decryption can be parallelized. Note that a one-bit change to the ciphertext causes complete corruption of the corresponding block of plaintext, and inverts the corresponding bit in the following block of plaintext, but the rest of the blocks remain intact. This peculiarity is exploited in different padding oracle attacks, such as POODLE. Explicit initialization vectors takes advantage of this property by prepending a single random block to the plaintext.
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In an electronic brainstorming, the group creates a shared list of ideas. In contrast to paper-based brainstorming or brain-writing methods, contributions are directly entered by the participants and immediately visible to all, typically in anonymous format. By overcoming social barriers with anonymity and process limitations with parallelized input, more ideas are generated and shared with less conformity than in a traditional brainstorming or brain- writing session. The benefits of electronic brainstorming increase with group size.
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Each one separately manages their candidate/solution and the results are returned to the master. • Move acceleration model: The quality of each move is evaluated in a parallel centralized way. That model is particularly interesting when the evaluation function can be itself parallelized as it is CPU time-consuming and/or I/O intensive. In that case, the function can be viewed as an aggregation of a certain number of partial functions that can be run in parallel.
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4 of: Thus, these are different from distributed computing problems that need communication between tasks, especially communication of intermediate results. They are easy to perform on server farms which lack the special infrastructure used in a true supercomputer cluster. They are thus well suited to large, Internet-based distributed platforms such as BOINC, and do not suffer from parallel slowdown. The opposite of embarrassingly parallel problems are inherently serial problems, which cannot be parallelized at all.
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Hans Lischka has published over 300 scientific papers, which have been cited about 20,000 times, with h-index 69 according to Google Scholar. Hans Lischka has pioneered on the development and implementation of highly-parallelized MRCI, with analytical energy gradients and analytical nonadiabatic couplings. These methods were later fundamental for the implementation of the Newton-X program. In 2010, Lischka's group published the first ab initio mapping of the deactivation mechanism of UV-excited canonical nucleobases.
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As AC-filters, four resonance circuits are installed on both sides of the plant. Each of the filters consist of a series-connection of a two-microfarad capacitator with a coil to which a 615 ohm resistor is parallelized. One filter on each side uses a 41 mH air- core coil, while the other has a 29 mH air-core coil. On each power exit, there is also a bank of capacitors for reactive power compensation.
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Gravity Pipe (abbreviated GRAPE) is a project which uses hardware acceleration to perform gravitational computations. Integrated with Beowulf-style commodity computers, the GRAPE system calculates the force of gravity that a given mass, such as a star, exerts on others. The project resides at Tokyo University. The GRAPE hardware acceleration component "pipes" the force computation to the general-purpose computer serving as a node in a parallelized cluster as the innermost loop of the gravitational model.
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DOACROSS parallelism is particularly useful when one statement depends on the values generated by another statement. In such a loop, DOALL parallelism can not be implemented in a straightforward manner. If the first statement blocks the execution of the second statement until the required value has been produced, then the two statements would be able to execute independent of each other (i.e.), each of the aforementioned statements would be parallelized for simultaneous execution using DOALL parallelism.
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Only suspension towers of extraordinary height are built as lattice towers. The electrode of Mikhailovskaya converter station is situated north of Smile at . It is connected with Mikhailovskaya converter station by a 32 kilometre long overhead line, which consists of two parallelized 2450 mm2 ACSR conductors. The electrode line from Mikhailovskaya converter station follows the main line on the left side seen from Mikhailovskaya converter station until a point northeast of Zolobok at where it undercrosses the main line.
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The first example is the original code written sequentially. This example includes a dependence which would normally prevent the code to be run in parallel. The second example, shows the code parallelized and the privatization technique used to remove the dependence. //Sequential Code: //Assume the variables have already been initialized do i = 10, N - 1 x = (b(i) + c(i))/2 b(i) = a(i + 1) + x enddo For each iteration of the loop above, x is assigned and then read.
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Additionally, it is difficult to parallelize the partitioning step efficiently in-place. The use of scratch space simplifies the partitioning step, but increases the algorithm's memory footprint and constant overheads. Other more sophisticated parallel sorting algorithms can achieve even better time bounds. For example, in 1991 David Powers described a parallelized quicksort (and a related radix sort) that can operate in time on a CRCW (concurrent read and concurrent write) PRAM (parallel random-access machine) with processors by performing partitioning implicitly.
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XFS file systems are internally partitioned into allocation groups, which are equally sized linear regions within the file system. Files and directories can span allocation groups. Each allocation group manages its own inodes and free space separately, providing scalability and parallelism so multiple threads and processes can perform I/O operations on the same file system simultaneously. This architecture helps to optimize parallel I/O performance on systems with multiple processors and/or cores, as metadata updates can also be parallelized.
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The runtime of the parallelized algorithm consists of two parts. The time for the computation and the part for communication and data transfer between the processes. As there is no additional computation in the algorithm and the computation is split equally among the p processes, we have a runtime of O(n^3 / p) for the computational part. In each iteration of the algorithm there is a one-to-all broadcast operation performed along the row and column of the processes.
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In order to identify the putative functions and annotation of the genes, MG-RAST builds clusters of proteins at 90% identity level using the UCLUST implementation in QIIME. The longest sequence of each cluster will be selected for a similarity analysis. The similarity analysis is computed through sBLAT (in which BLAT algorithm is parallelized using OpenMP). The search is computed against a protein database derived from the M5nr, which provides nonredundant integration of sequences from GenBank, SEED, IMG, UniProt, KEGG and eggNOGs databases.
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This ensures that message order is kept and prevents concurrency issues in modules. Yet the modules (worker threads) run concurrently, thus the global log processing flow is greatly parallelized. When an input module receives data, it creates an internal representation of the log message which is basically a structure containing the raw event data and any optional fields. This log message is then pushed to the queue of the next module in the route and an internal event is generated to signal the availability of the data.
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Intel Ct is a programming model developed by Intel to ease the exploitation of its future multicore chips, as demonstrated by the Tera-Scale research program. It is based on the exploitation of SIMD to produce automatically parallelized programs. On August 19, 2009, Intel"RapidMind + Intel", Intel Blog (2009-08-19) acquired RapidMind, a privately held company founded and headquartered in Waterloo, Ontario, Canada. RapidMind and Ct combined into a successor named Intel Array Building Blocks (ArBB)"Intel Flexes Parallel Programming Muscles", HPCwire (2010-09-02).
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In December 2004, Google Research published a paper on the MapReduce algorithm, which allows very large scale computations to be trivially parallelized across large clusters of servers. Cutting and Mike Cafarella, realizing the importance of this paper to extending Lucene into the realm of extremely large search problems, created the open-source Hadoop framework that allows applications based on the MapReduce paradigm to be run on large clusters of commodity hardware. Cutting was an employee of Yahoo!, where he led the Hadoop project full-time.
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DOACROSS parallelism is a parallelization technique used to perform Loop-level parallelism by utilizing synchronisation primitives between statements in a loop. This technique is used when a loop cannot be fully parallelized by DOALL parallelism due to data dependencies between loop iterations, typically loop- carried dependencies. The sections of the loop which contain loop-carried dependence are synchronized, while treating each section as a parallel task on its own. Therefore, DOACROSS parallelism can be used to complement DOALL parallelism to reduce loop execution times.
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Sample work at the University was primarily aimed at ways to efficiently fill the PEs with data, thus conducting the first "stress test" in computer development. In order to make this as easy as possible, several new computer languages were created; IVTRAN and TRANQUIL were parallelized versions of FORTRAN, and Glypnir was a similar conversion of ALGOL. Generally, these languages provided support for loading arrays of data "across" the PEs to be executed in parallel, and some even supported the unwinding of loops into array operations.
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As a simple example, if a system is running code on a 2-processor system (CPUs "a" & "b") in a parallel environment and we wish to do tasks "A" and "B", it is possible to tell CPU "a" to do task "A" and CPU "b" to do task "B" simultaneously, thereby reducing the run time of the execution. The tasks can be assigned using conditional statements as described below. Task parallelism emphasizes the distributed (parallelized) nature of the processing (i.e. threads), as opposed to the data (data parallelism).
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No other constant-space set data structure has this property, but the average access time of sparse hash tables can make them faster in practice than some Bloom filters. In a hardware implementation, however, the Bloom filter shines because its lookups are independent and can be parallelized. To understand its space efficiency, it is instructive to compare the general Bloom filter with its special case when = 1. If = 1, then in order to keep the false positive rate sufficiently low, a small fraction of bits should be set, which means the array must be very large and contain long runs of zeros.
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The parallelism comes from: (1) the reachability queries can be parallelized more easily (e.g. by a BFS, and it can be fast if the diameter of the graph is small); and (2) the independence between the subtasks in the divide-and-conquer process. This algorithm performs well on real-world graphs, but does not have theoretical guarantee on the parallelism (consider if a graph has no edges, the algorithm requires O(n) levels of recursions). Blelloch et al.. in 2016 shows that if the reachability queries are applied in a random order, the cost bound of O(n log n) still holds.
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Nearest neighbor memory access patterns appear in simulation, and are related to sequential or strided patterns. An algorithm may traverse a data structure using information from the nearest neighbors of a data element (in one or more dimensions) to perform a calculation. These are common in physics simulations operating on grids. Nearest neighbor can also refer to inter-node communication in a cluster; physics simulations which rely on such local access patterns can be parallelized with the data partitioned into cluster nodes, with purely nearest-neighbor communication between them, which may have advantages for latency and communication bandwidth.
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The theoretical speedup of the latency of the execution of a program as a function of the number of processors executing it, according to Amdahl's law. The speedup is limited by the serial part of the program. For example, if 95% of the program can be parallelized, the theoretical maximum speedup using parallel computing would be 20 times. In computer architecture, Amdahl's law (or Amdahl's argument) is a formula which gives the theoretical speedup in latency of the execution of a task at fixed workload that can be expected of a system whose resources are improved.
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At first, performance was dismal, with most programs running at about 15 MFLOPS, about three times the average for the CDC 7600. Over time this improved, notably after Ames programmers wrote their own version of FORTRAN, CFD, and learned how to parallel I/O into the limited PEMs. On problems that could be parallelized the machine was still the fastest in the world, outperforming the CDC 7600 by two to six times, and it is generally credited as the fastest machine in the world until 1981. On 7 September 1981, after nearly 10 years of operation, the ILLIAC IV was turned off.
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BisQue has been used to manage and analyze 23.3 hours (884GB) of high definition video from dives in Bering Sea submarine canyons to evaluate the density of fishes, structure-forming corals and sponges and to document and describe fishing damage. Non-overlapping frames were extracted from each video transect at a constant frequency of 1 frame per 30s. An image processing algorithm developed in Matlab was used to detect laser dots projected onto the seafloor as a scale reference. BisQue's module system allows to wrap this Matlab code into an analysis module that can be parallelized across a compute cluster.
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A straightforward application of the Merkle–Damgård construction, where the size of hash output is equal to the internal state size (between each compression step), results in a narrow-pipe hash design. This design causes many inherent flaws, including length-extension, multicollisions, long message attacks, generate-and-paste attacks, and also cannot be parallelized. As a result, modern hash functions are built on wide- pipe constructions that have a larger internal state size – which range from tweaks of the Merkle–Damgård construction to new constructions such as the sponge construction and HAIFA construction. None of the entrants in the NIST hash function competition use a classical Merkle–Damgård construction.
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MDA products result in a length of about 12 kb and ranges up to around 100 kb, enabling its use in DNA sequencing. In 2017, a major improvement to this technique, called WGA-X, was introduced by taking advantage of a thermostable mutant of the phi29 polymerase, leading to better genome recovery from individual cells, in particular those with high G+C content. MDA has also been implemented in a microfluidic droplet-based system to achieve a highly parallelized single-cell whole genome amplification. By encapsulating single-cells in droplets for DNA capture and amplification, this method offers reduced bias and enhanced throughput compared to conventional MDA.
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Retrieved 2013-09-18. The high-end scalability of geographically dispersed grids is generally favorable, due to the low need for connectivity between nodes relative to the capacity of the public Internet. There are also some differences in programming and MC. It can be costly and difficult to write programs that can run in the environment of a supercomputer, which may have a custom operating system, or require the program to address concurrency issues. If a problem can be adequately parallelized, a “thin” layer of “grid” infrastructure can allow conventional, standalone programs, given a different part of the same problem, to run on multiple machines.
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A critical option was the implementation of the first practical gauge-invariant atomic orbital (GIAO) NMR program by Wolinski, who additionally included a highly efficient integral package. Bofill executed an unhindered natural orbital-complete active space (UNO-CAS) program including analytical gradients; this is a minimal-cost alternative to MC-SCF and works just as well in most cases. TEXAS was initially parallelized in 1995–1996 on a cluster of 10 IBM RS6000 workstations. In 1996, Baker joined Pulay and, around the same time, Intel brought out the Pentium Pro, a processor for PC that was competitive with low- end workstations and less costly by around an order of magnitude.
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Civilian AR-15 target sights have an aperture between . The aperture on AR-15 military sights have a day aperture of approximately , and also a night setting with a larger , and as such the military sight is not strictly a diopter sight in either setting. The "diopter sight effect" is achieved when looking through an aperture opening of approximately or less, and happens due to an optical phenomenon (edge effects) resulting in the light passing through being parallelized similar to how a collimated lens would. Because of this optical effect the front sight will also appear more steady, even though the shooter moves the head in a way such that the sighting eye moves sideways relative to the rear sight.
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In 1999, a Working Party on Litigation was set up by member states of the European Patent Organisation to propose an optional agreement on the creation of such a central judicial system. At its fifth meeting on 19 and 20 November 2003, the Working Party came up with a draft agreement and a draft statute for the European Patent Court. The EPO level proposal parallelized a similar EU level proposal for a Luxembourg European Patent Court by the European Commission and Council in conjunction with the community patent. In 2006, the European Commission launched a public consultation on future patent policy in Europe,European Commission, DG Internal Market and Services, Consultation and public hearing on future patent policy in Europe, Retrieved on September 6, 2006.
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Car–Parrinello molecular dynamics or CPMD refers to either a method used in molecular dynamics (also known as the Car–Parrinello method) or the computational chemistry software package used to implement this method. The CPMD method is related to the more common Born–Oppenheimer molecular dynamics (BOMD) method in that the quantum mechanical effect of the electrons is included in the calculation of energy and forces for the classical motion of the nuclei. However, whereas BOMD treats the electronic structure problem within the time-independent Schrödinger equation, CPMD explicitly includes the electrons as active degrees of freedom, via (fictitious) dynamical variables. The software is a parallelized plane wave / pseudopotential implementation of density functional theory, particularly designed for ab initio molecular dynamics.
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They can use these MSMs to reveal how proteins misfold and to quantitatively compare simulations with experiments. Between 2000 and 2010, the length of the proteins Folding@home has studied have increased by a factor of four, while its timescales for protein folding simulations have increased by six orders of magnitude. In 2002, Folding@home used Markov state models to complete approximately a million CPU days of simulations over the span of several months, and in 2011, MSMs parallelized another simulation that required an aggregate 10 million CPU hours of computing. In January 2010, Folding@home used MSMs to simulate the dynamics of the slow-folding 32-residue NTL9 protein out to 1.52 milliseconds, a timescale consistent with experimental folding rate predictions but a thousand times longer than formerly achieved.
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TrueCrypt supports parallelized encryption for multi-core systems and, under Microsoft Windows, pipelined read/write operations (a form of asynchronous processing) to reduce the performance hit of encryption and decryption. On newer processors supporting the AES-NI instruction set, TrueCrypt supports hardware- accelerated AES to further improve performance. The performance impact of disk encryption is especially noticeable on operations which would normally use direct memory access (DMA), as all data must pass through the CPU for decryption, rather than being copied directly from disk to RAM. In a test carried out by Tom's Hardware, although TrueCrypt is slower compared to an unencrypted disk, the overhead of real-time encryption was found to be similar regardless of whether mid-range or state-of-the-art hardware is in use, and this impact was "quite acceptable".
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Merge sort was one of the first sorting algorithms where optimal speed up was achieved, with Richard Cole using a clever subsampling algorithm to ensure O(1) merge. Other sophisticated parallel sorting algorithms can achieve the same or better time bounds with a lower constant. For example, in 1991 David Powers described a parallelized quicksort (and a related radix sort) that can operate in O(log n) time on a CRCW parallel random-access machine (PRAM) with n processors by performing partitioning implicitly. Powers further shows that a pipelined version of Batcher's Bitonic Mergesort at O((log n)2) time on a butterfly sorting network is in practice actually faster than his O(log n) sorts on a PRAM, and he provides detailed discussion of the hidden overheads in comparison, radix and parallel sorting.
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1998) GENESIS is intended to quantify the physical framework of the nervous system in a way that allows for easy understanding of the physical structure of the nerves in question. “At present only GENESIS allows parallelized modeling of single neurons and networks on multiple-instruction-multiple-data parallel computers.”(A consumer guide to neuronal modeling software TRENDS IN NEUROSCIENCES 15: 462-464, 1992 Copyright © Elsevier Science Publishers Ltd, (UK), 1992 E. De Schutter Division of Biology 216-76, California Institute of Technology, Pasadena, CA 91125, USA) Development of GENESIS software spread from its home at Caltech to labs at the University of Texas at San Antonio, the University of Antwerp, the National Centre for Biological Sciences in Bangalore, the University of Colorado, the Pittsburgh Supercomputing Center, the San Diego Supercomputer Center, and Emory University.
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The model has been optimized to be highly parallelized, in order to facilitate rapid computation of large, complex problems. ADCIRC is able to apply several different bottom friction formulations including Manning's n-based bottom drag due to changes in land coverage (such as forests, cities, and seafloor composition), as well as utilize atmospheric forcing data (wind stress and atmospheric pressure) from several sources, and further reduce the strength of the wind forcing due to surface roughness effects. The model is also able to incorporate effects such as time-varying topography and bathymetry, boundary fluxes from rivers or other sources, tidal potential, and sub-grid scale features like levees. ADCIRC is frequently coupled to a wind wave model such as STWAVE, SWAN, or WAVEWATCH III, especially in storm surge applications where wave radiation stress can have important effects on ocean circulation and vice versa.
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The algorithm attempts to set up a congruence of squares modulo n (the integer to be factorized), which often leads to a factorization of n. The algorithm works in two phases: the data collection phase, where it collects information that may lead to a congruence of squares; and the data processing phase, where it puts all the data it has collected into a matrix and solves it to obtain a congruence of squares. The data collection phase can be easily parallelized to many processors, but the data processing phase requires large amounts of memory, and is difficult to parallelize efficiently over many nodes or if the processing nodes do not each have enough memory to store the whole matrix. The block Wiedemann algorithm can be used in the case of a few systems each capable of holding the matrix.
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VLIW was put forward by Fisher as a way to build general-purpose instruction-level parallel processors exploiting ILP to a degree that would have been impractical using what would later be called superscalar control hardware. Instead, the compiler could, in advance, arrange the ILP to be carried out nearly in lock-step by the hardware, commanded by long instructions or a similar mechanism. While there had previously been processors that achieved significant amounts of ILP, they had all relied upon code laboriously hand-parallelized by the user, or upon library routines, and thus were not general-purpose computers and did not fit the VLIW paradigm. The practicality of trace scheduling was demonstrated by a compiler built at Yale by Fisher and three of his graduate students, John Ruttenberg, Alexandru Nicolau, and especially John Ellis, whose doctoral dissertation on the compiler won the ACM Doctoral Dissertation Award in 1985.
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This reduces the overall build time, due to eliminating the duplication, but increases the incremental build time (the time required after making a change to any single source file that is included in the Single Compilation Unit), due to requiring a full rebuild of the entire unit if any single input file changes. Therefore, this technique is appropriate for a set of infrequently modified source files with significant overlap (many or expensive common headers or templates), or source files that frequently require recompilation together, such as due to all including a common header or template that changes frequently. Another disadvantage of SCU is that it is serial, compiling all included source files in sequence in one process, and thus cannot be parallelized, as can be done in separate compilation (via distcc or similar programs). Thus SCU requires explicit partitioning (manual partitioning or "sharding" into multiple units) to parallelize compilation.
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The parent of x in the Cartesian tree is either the left neighbor of x or the right neighbor of x, whichever exists and has a larger value. The left and right neighbors may also be constructed efficiently by parallel algorithms, so this formulation may be used to develop efficient parallel algorithms for Cartesian tree construction.. Another linear-time algorithm for Cartesian tree construction is based on divide-and-conquer. In particular, the algorithm recursively constructs the tree on each half of the input, and then merging the two trees by taking the right spine of the left tree and left spine of the right tree and performing a standard merging operation. The algorithm is also parallelizable since on each level of recursion, each of the two sub-problems can be computed in parallel, and the merging operation can be efficiently parallelized as well.
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Fungibility has been used to describe certain types of tasks that can be broken down into interchangeable pieces that are easily parallelized and are not interdependent on the other pieces. For example: If a worker can hand dig 1 meter of ditch in a day, and a 10-meter ditch needs to be dug, either that worker can be given 10 days to complete the entire project, or 9 additional workers can be hired for the project to be completed in a single day. Each worker can complete his piece of the project without interfering with the other workers, and more importantly, each worker is not dependent on the results of any of the other workers to complete his share of the total project (this would contrast with the digging of a 10-meter-deep hole). On the other hand, non-fungible tasks tend to be highly serial in nature and require the completion of earlier steps before later steps can even be started.
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PRAM algorithms cannot be parallelized with the combination of CPU and dynamic random-access memory (DRAM) because DRAM does not allow concurrent access; but they can be implemented in hardware or read/write to the internal static random-access memory (SRAM) blocks of a field-programmable gate array (FPGA), it can be done using a CRCW algorithm. However, the test for practical relevance of PRAM (or RAM) algorithms depends on whether their cost model provides an effective abstraction of some computer; the structure of that computer can be quite different than the abstract model. The knowledge of the layers of software and hardware that need to be inserted is beyond the scope of this article. But, articles such as demonstrate how a PRAM-like abstraction can be supported by the explicit multi-threading (XMT) paradigm and articles such as demonstrate that a PRAM algorithm for the maximum flow problem can provide strong speedups relative to the fastest serial program for the same problem.
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Kreck grew up as the son of the theologian Walter Kreck in Herborn and studied mathematics and physics from 1966 to 1970, and business administration at the Universities of Bonn, Berlin and Regensburg. In 1970 he submitted his diploma in Mathematics in Bonn and in 1972 he received his doctorate there under the supervision of Friedrich Hirzebruch, with a thesis titled An invariant for stably parallelized manifolds. From 1972 to 1976 he studied Protestant theology in Bonn: in a similar period he was also assistant from 1970 to 1976 to professor Hirzebruch. In 1977 he completed his habilitation in Bonn in Mathematics, titled Bordism groups of diffeomorphisms. In 1976 he became professor at the University of Wuppertal and in 1978 he moved to the University of Mainz. From 1994 to 2002 he was director of the Mathematical Research Institute of Oberwolfach. In 1999 he became professor at the University of Heidelberg. From 2007 until October 2011 he was the founding director of the Hausdorff Research Institute for Mathematics at the University of Bonn.
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For data in which the maximum key size is significantly smaller than the number of data items, counting sort may be parallelized by splitting the input into subarrays of approximately equal size, processing each subarray in parallel to generate a separate count array for each subarray, and then merging the count arrays. When used as part of a parallel radix sort algorithm, the key size (base of the radix representation) should be chosen to match the size of the split subarrays.. The simplicity of the counting sort algorithm and its use of the easily parallelizable prefix sum primitive also make it usable in more fine- grained parallel algorithms.. As described, counting sort is not an in-place algorithm; even disregarding the count array, it needs separate input and output arrays. It is possible to modify the algorithm so that it places the items into sorted order within the same array that was given to it as the input, using only the count array as auxiliary storage; however, the modified in-place version of counting sort is not stable.
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