But this only takes a few days because we can parallelize the training.
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For large applications, the CI server can break up and ideally parallelize the functional tests to save time.
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Yarn is also able to parallelize some of its operations, which in turn speeds up the install process for new modules, too.
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Of course, you can parallelize a lot of that; if you are rendering on a high-end, 16-core gaming rig with a ton of RAM, you might be able to get that time down to just a century or so.
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While this approach may not work for all startups pursuing an ICO, those startups that can connect their fundraise to product growth have a unique opportunity to parallelize multiple objectives for the company — and that can accelerate success in a very competitive environment.
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However, if you try to parallelize the loops (each loop a different thread), then the wrong sum would be calculated. In this case, privatization does not work. You cannot privatize the sum because they each rely on each other. While there are techniques to still parallelize this code, simple privatization does not work.
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These tools use either compile time techniques or run-time techniques. These techniques are built-in in some parallelizing compilers but user needs to identify parallelize code and mark the code with special language constructs. The compiler identifies these language constructs and analyzes the marked code for parallelization. Some tools parallelize only special form of code like loops.
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CABAC is also difficult to parallelize and vectorize, so other forms of parallelism (such as spatial region parallelism) may be coupled with its use. In HEVC, CABAC is used in all profiles of the standard.
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For high-performance needs, BisQue can leverage the HTCondor high-throughput computing software framework for coarse-grained distributed parallelization. In the latter case, BisQue can automatically parallelize analysis over large image datasets and then collect the results in a single BisQue metadata document.
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A fortiori, the corresponding decision problem belongs to the class P of problems solvable in polynomial time. The GCD problem is not known to be in NC, and so there is no known way to parallelize it efficiently; nor is it known to be P-complete, which would imply that it is unlikely to be possible to efficiently parallelize GCD computation. Shallcross et al. showed that a related problem (EUGCD, determining the remainder sequence arising during the Euclidean algorithm) is NC-equivalent to the problem of integer linear programming with two variables; if either problem is in NC or is P-complete, the other is as well.
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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 compiler theory, dependence analysis produces execution-order constraints between statements/instructions. Broadly speaking, a statement S2 depends on S1 if S1 must be executed before S2. Broadly, there are two classes of dependencies--control dependencies and data dependencies. Dependence analysis determines whether it is safe to reorder or parallelize statements.
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Parallel SAT solvers come in three categories: Portfolio, Divide-and-conquer and parallel local search algorithms. With parallel portfolios, multiple different SAT solvers run concurrently. Each of them solves a copy of the SAT instance, whereas divide-and-conquer algorithms divide the problem between the processors. Different approaches exist to parallelize local search algorithms.
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Block sort does not exploit sorted ranges of data on as fine a level as some other algorithms, such as Timsort. It only checks for these sorted ranges at the two predefined levels: the A and B subarrays, and the A and B blocks. It is also harder to implement and parallelize compared to a merge sort.
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Past techniques provided solutions for languages like FORTRAN and C; however, these are not enough. These techniques dealt with parallelization sections with specific system in mind like loop or particular section of code. Identifying opportunities for parallelization is a critical step while generating multithreaded application. This need to parallelize applications is partially addressed by tools that analyze code to exploit parallelism.
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The Example 4 above makes it impossible to predict anything from that loop. Unless the functions themselves are trivial (constant), there is no way to know where the loop will start, stop and how much it'll increment each iteration. Those loops are not only hard to parallelize, but they also perform horribly. Each iteration, the loop will evaluate two functions (max() and increment()).
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Previous linear-time algorithms are based on depth-first search which is generally considered hard to parallelize. Fleischer et al. in 2000 proposed a divide-and-conquer approach based on reachability queries, and such algorithms are usually called reachability-based SCC algorithms. The idea of this approach is to pick a random pivot vertex and apply forward and backward reachability queries from this vertex.
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Applications are often classified according to how often their subtasks need to synchronize or communicate with each other. An application exhibits fine-grained parallelism if its subtasks must communicate many times per second; it exhibits coarse-grained parallelism if they do not communicate many times per second, and it exhibits embarrassing parallelism if they rarely or never have to communicate. Embarrassingly parallel applications are considered the easiest to parallelize.
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1\. The process starts with identifying code sections that the programmer feels have parallelism possibilities. Often this task is difficult since the programmer who wants to parallelize the code has not originally written the code under consideration. Another possibility is that the programmer is new to the application domain. Thus, though this first stage in the parallelization process and seems easy at first it may not be so. 2\.
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However, these dependencies must not cross splitpoints; they will generate compiler warnings . In order to parallelize this loop, a special 'Iterator' class may be used in place of a standard integer looping counter. It is safe for parallelization, and the programmer is free to create new Iterator classes at will . In addition to these Iterator classes, the programmer is free to implement classes called 'Accumulators' which are used to carry out reduction operations.
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The high demand for low-cost sequencing has driven the development of high-throughput sequencing technologies that parallelize the sequencing process, producing thousands or millions of sequences at once. High-throughput sequencing is intended to lower the cost of DNA sequencing beyond what is possible with standard dye-terminator methods. In ultra-high- throughput sequencing, as many as 500,000 sequencing-by-synthesis operations may be run in parallel. Illumina Genome Analyzer II System.
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In order to find global optimum, global optimization algorithms such as simulated annealing are required. Unfortunately, simulated annealing may be hard to parallelize on modern multicore computers. Given enough time, simulated annealing can be shown to find the global optimum with a probability approaching 1, but such convergence proof does not mean the required time is reasonably low. In 1998, it was found that genetic algorithms are robust and fast fitting methods for X-ray reflectivity.
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The general idea is to iteratively approach the posterior from the prior through a sequence of target distributions. An advantage of such methods, compared to ABC-MCMC, is that the samples from the resulting posterior are independent. In addition, with sequential methods the tolerance levels must not be specified prior to the analysis, but are adjusted adaptively. It is relatively straightforward to parallelize a number of steps in ABC algorithms based on rejection sampling and sequential Monte Carlo methods.
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Up to version 2.0, OpenMP primarily specified ways to parallelize highly regular loops, as they occur in matrix-oriented numerical programming, where the number of iterations of the loop is known at entry time. This was recognized as a limitation, and various task parallel extensions were added to implementations. In 2005, an effort to standardize task parallelism was formed, which published a proposal in 2007, taking inspiration from task parallelism features in Cilk, X10 and Chapel. Version 3.0 was released in May 2008.
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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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Parallel BLAST versions of split databases are implemented using MPI and Pthreads, and have been ported to various platforms including Windows, Linux, Solaris, Mac OS X, and AIX. Popular approaches to parallelize BLAST include query distribution, hash table segmentation, computation parallelization, and database segmentation (partition). Databases are split into equal sized pieces and stored locally on each node. Each query is run on all nodes in parallel and the resultant BLAST output files from all nodes merged to yield the final output.
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Some NP-problems are not known to be either NP-complete or in P. These problems (e.g. factoring, graph isomorphism, parity games) are suspected to be difficult. Similarly there are problems in P that are not known to be either P-complete or NC, but are thought to be difficult to parallelize. Examples include the decision problem forms of finding the greatest common divisor of two numbers, and determining what answer the extended Euclidean algorithm would return when given two numbers.
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Parareal can be derived as both a multigrid method in time method or as multiple shooting along the time axis. Both ideas, multigrid in time as well as adopting multiple shooting for time integration, go back to the 1980s and 1990s. Parareal is a widely studied method and has been used and modified for a range of different applications. Ideas to parallelize the solution of initial value problems go back even further: the first paper proposing a parallel-in-time integration method appeared in 1964.
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Parallelization can be used to speed up priority queues, but requires some changes to the priority queue interface. The reason for such changes is that a sequential update usually only has O(1) or O(\log n) cost, and there is no practical gain to parallelize such an operation. One possible change is to allow the concurrent access of multiple processors to the same priority queue. The second possible change is to allow batch operations that work on k elements, instead of just one element.
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The CAL Actor Language was developed in 2001 as part of the Ptolemy II project at University of California at Berkeley. CAL is a dataflow language geared towards a variety of application domains, such as multimedia processing, control systems, network processing etc. Another common reason for choosing dataflow is that the goal is an efficient parallel implementation which would be difficult or impossible to achieve using a sequential programming language. Sequential languages are notoriously difficult to parallelize in general, so efficient parallel implementations will usually require significant guidance from the user.
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Cosmology@Home uses an innovative way of using machine learning to effectively parallelize a large computational task that involves many inherently sequential calculations over a substantial number of distributed computers. For any given class of theoretically possible models of the Universe, Cosmology@Home generates tens of thousands of example Universes and packages the cosmological parameters describing these Universes as work units. Each work unit represents a single Universe. When the work unit is requested by a participating computer, this computer simulates this Universe from the Big Bang until today.
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NT is the semantic work-horse, being used to simplify and decompose structures, based on a dataflow-like execution strategy similar to GAMMA and NESL. The NT semantic achieves a goal similar to that of the Lämmel and Peyton-Jones’ boilerplate elimination. All other features of the language are definable from these two laws - including recursion, subscripting structures, function references, and evaluation of function bodies. Though it was not the original intent, these new approaches allowed the language to parallelize a large fraction of the operations it performed, transparently to the programmer.
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In the 1985–1987, the virtual synchrony model was proposed and emerged as a widely adopted standard (it was used in the Isis Toolkit, Horus, Transis, Ensemble, Totem, Spread, C-Ensemble, Phoenix and Quicksilver systems, and is the basis for the CORBA fault-tolerant computing standard). Virtual synchrony permits a multi-primary approach in which a group of processes cooperates to parallelize some aspects of request processing. The scheme can only be used for some forms of in-memory data, but can provide linear speedups in the size of the group. A number of modern products support similar schemes.
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In contrast to e.g. Runge-Kutta or multi-step methods, some of the computations in Parareal can be performed in parallel and Parareal is therefore one example of a parallel-in-time integration method. While historically most efforts to parallelize the numerical solution of partial differential equations focussed on the spatial discretization, in view of the challenges from exascale computing, parallel methods for temporal discretization have been identified as a possible way to increase concurrency in numerical software. Because Parareal computes the numerical solution for multiple time steps in parallel, it is categorized as a parallel across the steps method.
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Periodic boundary conditions can be simulated by wrapping the simulation box in an additional layer of cells (shaded orange) containing periodic copies of the boundary cells (blue particles). In the ghost cells approach, the simulation box is wrapped in an additional layer of cells. These cells contain periodically wrapped copies of the corresponding simulation cells inside the domain. Although the data—and usually also the computational cost—is doubled for interactions over the periodic boundary, this approach has the advantage of being straightforward to implement and very easy to parallelize, since cells will only interact with their geographical neighbours.
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On the other hand, many researchers use a pool of processors to speed up the execution of a sequential algorithm, just because independent runs can be made more rapidly by using several processors than by using a single one. In this case, no interaction at all exists between the independent runs. However, actually most parallel population-based techniques found in the literature utilize some kind of spatial disposition for the individuals, and then parallelize the resulting chunks in a pool of processors. Among the most widely known types of structured metaheuristics, the distributed (or coarse grain) and cellular (or fine grain) algorithms are very popular optimization procedures.
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In principle, simulations of very large systems, approaching a cubic micron for milliseconds, are possible using a parallel implementation of DPD running on multiple processors in a Beowulf-style cluster. Because the non-bonded forces are short-ranged in DPD, it is possible to parallelize a DPD code very efficiently using a spatial domain decomposition technique. In this scheme, the total simulation space is divided into a number of cuboidal regions each of which is assigned to a distinct processor in the cluster. Each processor is responsible for integrating the equations of motion of all beads whose centres of mass lie within its region of space.
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On a shared memory machine (a computer with several CPUs that access the same memory space), messages can be sent by depositing their contents in a shared memory area. This is often the most efficient way to program shared memory computers with large number of processors, especially on NUMA machines, where memory is local to processors and accessing memory of another processor takes longer. SPMD on a shared memory machine is usually implemented by standard (heavyweight) processes. Unlike SPMD, shared memory multiprocessing (both symmetric multiprocessing, SMP, and non-uniform memory access, NUMA) presents the programmer with a common memory space and the possibility to parallelize execution by having the program take different paths on different processors.
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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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Consequently, such code is much more difficult to debug than single-threaded code when it breaks. There has been a perceived lack of motivation for writing consumer-level threaded applications because of the relative rarity of consumer-level demand for maximum use of computer hardware. Also, serial tasks like decoding the entropy encoding algorithms used in video codecs are impossible to parallelize because each result generated is used to help create the next result of the entropy decoding algorithm. Given the increasing emphasis on multi-core chip design, stemming from the grave thermal and power consumption problems posed by any further significant increase in processor clock speeds, the extent to which software can be multithreaded to take advantage of these new chips is likely to be the single greatest constraint on computer performance in the future.
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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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SequenceL is a general purpose functional programming language and auto-parallelizing tool set, whose primary design objectives are performance on multi-core processor hardware, ease of programming, platform portability/optimization, and code clarity and readability. Its main advantage is that it can be used to write straightforward code that automatically takes full advantage of all the processing power available, without programmers needing to be concerned with identifying parallelisms, specifying vectorization, avoiding race conditions, and other challenges of manual directive-based programming approaches such as OpenMP. Programs written in SequenceL can be compiled to multithreaded code that runs in parallel, with no explicit indications from a programmer of how or what to parallelize. As of 2015, versions of the SequenceL compiler generate parallel code in C++ and OpenCL, which allows it to work with most popular programming languages, including C, C++, C#, Fortran, Java, and Python.
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The maximal independent set problem was originally thought to be non-trivial to parallelize due to the fact that the lexicographical maximal independent set proved to be P-Complete; however, it has been shown that a deterministic parallel solution could be given by an NC^1 reduction from either the maximum set packing or the maximal matching problem or by an NC^2 reduction from the 2-satisfiability problem. Typically, the structure of the algorithm given follows other parallel graph algorithms - that is they subdivide the graph into smaller local problems that are solvable in parallel by running an identical algorithm. Initial research into the maximal independent set problem started on the PRAM model and has since expanded to produce results for distributed algorithms on computer clusters. The many challenges of designing distributed parallel algorithms apply in equal to the maximum independent set problem.
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SequenceL is a general purpose functional programming language and auto- parallelizing (Parallel computing) compiler and tool set, whose primary design objectives are performance on multi-core processor hardware, ease of programming, platform portability/optimization, and code clarity and readability. Its main advantage is that it can be used to write straightforward code that automatically takes full advantage of all the processing power available, without programmers needing to be concerned with identifying parallelisms, specifying vectorization, avoiding race conditions, and other challenges of manual directive-based programming approaches such as OpenMP. Programs written in SequenceL can be compiled to multithreaded code that runs in parallel, with no explicit indications from a programmer of how or what to parallelize. , versions of the SequenceL compiler generate parallel code in C++ and OpenCL, which allows it to work with most popular programming languages, including C, C++, C#, Fortran, Java, and Python.
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To compare the constraint-based polyhedral model to prior approaches such as individual loop transformations and the unimodular approach, consider the question of whether we can parallelize (execute simultaneously) the iterations of following contrived but simple loop: for i := 0 to N do A(i) := (A(i) + A(N-i))/2 Approaches that cannot represent symbolic terms (such as the loop-invariant quantity N in the loop bound and subscript) cannot reason about dependencies in this loop. They will either conservatively refuse to run it in parallel, or in some cases speculatively run it completely in parallel, determine that this was invalid, and re-execute it sequentially. Approaches that handle symbolic terms but represent dependencies via direction vectors or distance vectors will determine that the i loop carries a dependence (of unknown distance), since for example when N=10 iteration 0 of the loop writes an array element (A(0)) that will be read in iteration 10 (as A(10-10)) and reads an array element (A(10-0)) that will later be overwritten in iteration 10 (as A(10)). If all we know is that the i loop carries a dependence, we once again cannot safely run it in parallel.
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Such machines run in polynomial time because they can have a polynomial number of configurations. It is suspected that L ≠ P; that is, that some problems that can be solved in polynomial time also require more than logarithmic space. Similarly to the use of NP-complete problems to analyze the P = NP question, the P-complete problems, viewed as the "probably not parallelizable" or "probably inherently sequential" problems, serves in a similar manner to study the NC = P question. Finding an efficient way to parallelize the solution to some P-complete problem would show that NC = P. It can also be thought of as the "problems requiring superlogarithmic space"; a log-space solution to a P-complete problem (using the definition based on log-space reductions) would imply L = P. The logic behind this is analogous to the logic that a polynomial-time solution to an NP-complete problem would prove P = NP: if we have a NC reduction from any problem in P to a problem A, and an NC solution for A, then NC = P. Similarly, if we have a log-space reduction from any problem in P to a problem A, and a log-space solution for A, then L = P.
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