Crowdsourcing also can increase velocity by parallelizing work, so a sales team researching prospects can do it in hours rather than weeks.
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A cyclic multi-threading parallelizing compiler tries to split up a loop so that each iteration can be executed on a separate processor concurrently.
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41, No. 4 (2001). and Apéry's constants. An additional advantage of the method FEE is the possibility of parallelizing the algorithms based on the FEE.
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Philipp Ciechanowicz, Philipp Kegel, Maraike Schellmann, Sergei Gorlatch, and Herbert Kuchen. "Parallelizing the LM OSEM Image Reconstruction on Multi-Core Clusters." Parallel Computing: From Multicores and GPU's to Petascale, 19: 169–176, 2010.Philipp Ciechanowicz and Herbert Kuchen.
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The underlying platform, called MapD, works by parallelizing processes across commodity GPU cards, achieving speedups over traditional databases by a factor of a million using inexpensive hardware. Among other applications, TweetMap can be used for tracking earthquakes and epidemics such as influenza in real time.
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Vivek Sarkar. The PTRAN Parallel Programming System. In Parallel Functional Programming Languages and Compilers, edited by B. Szymanski, ACM Press Frontier Series, pages 309–391, 1991. Her PTRAN team developed new parallelism detection schemes and created the concept of the program dependence graph, the primary structuring method used by most parallelizing compilers.
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Parallel programs can be divided into two general categories: explicitly and implicitly parallel. Using parallel language constructs defined for process creation, communication and synchronization make an application explicitly parallel. Using a tool or parallelizing compiler to convert a serial program into a parallel one, makes it implicitly parallel. Both categories are equally bug- prone.
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However, unlike Folding@home's shorter trajectories, which are more amenable to distributed computing and other parallelizing methods, longer trajectories do not require adaptive sampling to sufficiently sample the protein's phase space. Due to this, it is possible that a combination of Anton's and Folding@home's simulation methods would provide a more thorough sampling of this space.
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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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In 2006, a prototype auto-parallelizing compiler was developed at Texas Tech University. In 2009, Texas Tech licensed the intellectual property to Texas Multicore Technologies (TMT),Texas Multicore Technologies, Inc. for follow-on commercial development. In January 2017 TMT released v3, which includes a free Community Edition for download in addition to the commercial Professional Edition.
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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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Par4All is an automatic parallelizing and optimizing compiler (workbench) for C and Fortran sequential programs. The purpose of this source-to-source compiler is to adapt existing applications to various hardware targets such as multicore systems, high performance computers and GPUs. It creates a new source code and thus allows the original source code of the application to remain unchanged.
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The basic parallelizing techniques Cetus currently implements are privatization, reduction variables recognition and induction variable substitution. A new graphic user interface (GUI) was added in Feb 2013. Speedup calculations and graph display were added in May 2013. A Cetus remote server in a client-server model was added in May 2013 and users can optionally transform C Code through the server.
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In computer science, all-pairs testing or pairwise testing is a combinatorial method of software testing that, for each pair of input parameters to a system (typically, a software algorithm), tests all possible discrete combinations of those parameters. Using carefully chosen test vectors, this can be done much faster than an exhaustive search of all combinations of all parameters, by "parallelizing" the tests of parameter pairs.
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For simple loops, where each iteration is independent of the others, loop-level parallelism can be embarrassingly parallel, as parallelizing only requires assigning a process to handle each iteration. However, many algorithms are designed to run sequentially, and fail when parallel processes race due to dependence within the code. Sequential algorithms are sometimes applicable to parallel contexts with slight modification. Usually, though, they require process synchronization.
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In contrast, a throughput oriented processor architecture is designed to maximize the amount of 'useful work' done in a significant window of time. Useful work refers to large calculations on a significant amount of data. They do this by parallelizing the work load so that many calculations can be performed simultaneously. The calculations may belong to a single task or a limited number of multiple tasks.
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The OPS5 forward chaining process makes it extremely parallelizeable during the matching phase, and several automatic parallelizing compilers were created. OPS4 was an early version, while OPS83 came later. The first implementation of OPS5 was written in Lisp, and later rewritten in BLISS for speed. DEC OPS5 is an extended implementation of the OPS5 language definition, developed for use with the VMS, RISC ULTRIX, and DEC OSF/1 operating systems.
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Rajesh K. Gupta (born 1961) is a computer scientist and engineer, currently the Qualcomm Professor in Embedded Microsystems at University of California, San Diego. His research concerns design and optimization of Cyber-physical systems (CPS). He is a Principal Investigator in the NSF MetroInsight project and serves as Associate Director of the Qualcomm Institute (also known as California Institute for Telecommunications and Information Technology). His research contributions include SystemC and SPARK Parallelizing High-level Synthesis.
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Futures and promises originated in functional programming and related paradigms (such as logic programming) to decouple a value (a future) from how it was computed (a promise), allowing the computation to be done more flexibly, notably by parallelizing it. Later, it found use in distributed computing, in reducing the latency from communication round trips. Later still, it gained more use by allowing writing asynchronous programs in direct style, rather than in continuation-passing style.
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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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The sieve compiler can split code within a sieve block into chunks either implicitly or explicitly though a 'splithere' statement. For instance, the following example shows parallelizing a loop: sieve { for (iterator i(0); i The compiler will implicitly add a splitpoint above the for loop construct body, as an entry point. Similarly one will be added after as an exit point. In the Sieve System, only local variables to the sieve block scope may have dependencies.
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A cursor is a construct available in most implementations of SQL that allows the programmer to handle data in a row-by-row manner rather than as a group. Parallelizing row-by-row processing is much more complex than serial processing, which is another reason to make use of non-procedural SQL wherever possible. Database vendors typically handle parallel processing without requiring special handling by application developers. Parallel processing can be orders of magnitude faster than serial processing.
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Compute-intensive is used to describe application programs that are compute bound. Such applications devote most of their execution time to computational requirements as opposed to I/O, and typically require small volumes of data. Parallel processing of compute- intensive applications typically involves parallelizing individual algorithms within an application process, and decomposing the overall application process into separate tasks, which can then be executed in parallel on an appropriate computing platform to achieve overall higher performance than serial processing.
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A pipelined multi-threading parallelizing compiler tries to break up the sequence of operations inside a loop into a series of code blocks, such that each code block can be executed on separate processors concurrently. There are many pleasingly parallel problems that have such relatively independent code blocks, in particular systems using pipes and filters. For example, when producing live broadcast television, the following tasks must be performed many times a second: #Read a frame of raw pixel data from the image sensor, #Do MPEG motion compensation on the raw data, #Entropy compress the motion vectors and other data, #Break up the compressed data into packets, #Add the appropriate error correction and do a FFT to convert the data packets into COFDM signals, and #Send the COFDM signals out the TV antenna. A pipelined multi-threading parallelizing compiler could assign each of these 6 operations to a different processor, perhaps arranged in a systolic array, inserting the appropriate code to forward the output of one processor to the next processor.
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The next module after the input module in a route, can be either a processor module or an output module. Actually an input or output module can also process data through built-in code or using the NXLog language execution framework. The only difference is that processor modules are run in another worker thread, thus parallelizing log processing even more. Considering that processor modules can also be chained, this can efficiently distribute work among multiple CPUs or CPU cores in the system.
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Some of the steps present in the two-pass algorithm can be merged for efficiency, allowing for a single sweep through the image. Multi-pass algorithms also exist, some of which run in linear time relative to the number of image pixels. In the early 1990s, there was considerable interest in parallelizing connected-component algorithms in image analysis applications, due to the bottleneck of sequentially processing each pixel. The interest to the algorithm arises again with an extensive use of CUDA.
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A parallelizing FORTRAN compiler can produce high performance for some codes with little manual intervention. Where manual porting is required, the simple and fine-grained synchronization model often allows programmers to write code the "obvious" way yet achieve good performance. A further goal is that programs for the MTA will be scalable that is, when run on an MTA with twice as many CPUs, the same program will have nearly twice the performance. Both of these are challenges for many other high-performance computer systems.
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The ROSE compiler framework, developed at Lawrence Livermore National Laboratory (LLNL), is an open-source software compiler infrastructure to generate source-to-source analyzers and translators for multiple source languages including C (C89, C98, Unified Parallel C (UPC)), C++ (C++98, C++11), Fortran (77, 95, 2003), OpenMP, Java, Python, and PHP. It also supports certain binary files, and auto-parallelizing compilers by generating source code annotated with OpenMP directives. Unlike most other research compilers, ROSE is aimed at enabling non-experts to leverage compiler technologies to build their own custom software analyzers and optimizers.
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A specific application of linear maps is for geometric transformations, such as those performed in computer graphics, where the translation, rotation and scaling of 2D or 3D objects is performed by the use of a transformation matrix. Linear mappings also are used as a mechanism for describing change: for example in calculus correspond to derivatives; or in relativity, used as a device to keep track of the local transformations of reference frames. Another application of these transformations is in compiler optimizations of nested- loop code, and in parallelizing compiler techniques.
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When compared with Reduction, privatization requires one task instead of two separate tasks in the case of privatization. This task, in an abstract form, is basically analyzing the code to identify the privatizable variables. On the other hand, the two tasks required by Reduction are: identifying the reduction variable, and then parallelizing the reduction operator. By observing each of the two techniques, it's easy tell what type of overhead each one adds to the parallel program; reduction increases the computation overhead while privatization increases the memory consumed by the program.
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Before the release of ONTAP 8, individual aggregate sizes were limited to a maximum of 2TB for FAS250 models and 16TB for all other models. The limitation on aggregate size, coupled with increasing density of disk drives, served to limit the performance of the overall system. NetApp, like most storage vendors, increases overall system performance by parallelizing disk writes to many different spindles (disk drives). Large capacity drives, therefore limit the number of spindles that can be added to a single aggregate, and therefore limit the aggregate performance.
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DAVinci Project is a proposed software framework that seeks to explore the possibilities of parallelizing some of the robotics algorithms as Map/Reduce tasks in Hadoop. The project aims to build a cloud computing environment capable of providing a compute cluster built with commodity hardware exposing a suite of robotic algorithms as a SaaS and share data co-operatively across the robotic ecosystem. This initiative is not available publicly. C2RO (C2RO Cloud Robotics) is a platform that processes real-time applications such as collision avoidance and object recognition in the cloud.
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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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General-purpose CPUs do commonly have multiple cores, but each one is fast enough that many programs are fast enough without parallelizing single tasks. (Threads are commonly used to deal with asynchronous inputs or outputs, especially in a GUI.) General-purpose CPUs are technically MIMD devices, but usually only hardware designed from the ground up for MIMD programming is referred to as MIMD. Many widely used programming languages such as C, C++ and Java have ceased to be strictly von Neumann by adding support for parallel processing, in the form of threads. However, most of the categorically non-von Neumann languages are also functional languages and have not achieved widespread use.
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Federated learning aims at training a machine learning algorithm, for instance deep neural networks, on multiple local datasets contained in local nodes without explicitly exchanging data samples. The general principle consists in training local models on local data samples and exchanging parameters (e.g. the weights and biases of a deep neural network) between these local nodes at some frequency to generate a global model shared by all nodes. The main difference between federated learning and distributed learning lies in the assumptions made on the properties of the local datasets, as distributed learning originally aims at parallelizing computing power where federated learning originally aims at training on heterogeneous datasets.
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Lincoln received a bachelor of science in electrical engineering and computer science from the Massachusetts Institute of Technology in 1986, with the thesis "DisCoRd distributed combinator reduction, automatic parallelizing compiler" under thesis advisor Rishiyur Nikhil. While pursuing that degree, he held a position in ETA Systems' Software Division from 1982 to 1983; one at Los Alamos National Laboratory, Division C-10 from 1984 to 1985. After graduation, he held a position with MCC from 1986 to 1988 in their Software Technology and Advanced Computer Architecture departments. Lincoln then attended Stanford University, from 1988 to 1992, earning a Ph.D. in computer science under advisor John Mitchell.
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The Tera-Scale processors also show potential in real-time analysis in fields such as finance which requires a processor that is capable of analyzing immense amounts of data. From Intel's past evolution from single core to multi-core processors, Intel has learned that parallelization is the key to the greater processing power in the future. The Intel Tera-Scale research program is not only focused on creating the multi-cored processors, but also the parallelizing applications of today and in the future. To show their dedication to all aspects of parallel computing, Intel set aside $20 million to establish centers that will research and develop new methods utilize parallel computing in many more applications.
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PVM is a software system that enables a collection of heterogeneous computers to be used as a coherent and flexible concurrent computational resource, or a "parallel virtual machine". The individual computers may be shared-memory or local-memory multiprocessors, vector supercomputers, specialized graphics engines, or scalar workstations and PCs, that may be interconnected by a variety of networks, such as Ethernet or FDDI. PVM consists of a run-time environment and library for message-passing, task and resource management, and fault notification. While PVM will not automatically make a commercial software package run faster, it does provide a powerful set of functions for manually parallelizing an existing source program, or for writing new parallel/distributed programs.
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The large data sets from the project are freely available for other researchers to use upon request and some can be accessed from the Folding@home website. The Pande lab has collaborated with other molecular dynamics systems such as the Blue Gene supercomputer, and they share Folding@home's key software with other researchers, so that the algorithms which benefited Folding@home may aid other scientific areas. In 2011, they released the open-source Copernicus software, which is based on Folding@home's MSM and other parallelizing methods and aims to improve the efficiency and scaling of molecular simulations on large computer clusters or supercomputers. Summaries of all scientific findings from Folding@home are posted on the Folding@home website after publication.
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The January 2013 round of the program, renamed to the Free and Open Source Software Outreach Program for Women, expanded to provide 25 internships with 10 organizations (Deltacloud, Fedora, GNOME, JBoss, Mozilla, Open Technology Institute, OpenITP, OpenStack, Subversion, and Wikimedia), with GNOME Foundation Executive Director Karen Sandler joining Zhurakhinskaya in organizing the program. The June 2013 internships included seven participants contributing to the Linux kernel, for example working on parallelizing the x86 boot process. Led by kernel contributor Sage Sharp, who found mentors and projects for the interns, they made significant contributions to the 3.11 kernel release. This round had 37 interns working with 16 organizations, and the next round starting in December 2013 had 30 interns working with 8 organizations.
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Two parallelizing strategies are specially focused on population- based algorithms: (1) Parallelization of computations, in which the operations commonly applied to each of the individuals are performed in parallel, and (2) Parallelization of population, in which the population is split in different parts that can be simply exchanged or evolved separately, and then joined later. In the beginning of the parallelization history of these algorithms, the well-known master-slave (also known as global parallelization or farming) method was used. In this approach, a central processor performs the selection operations while the associated slave processors (workers) run the variation operator and the evaluation of the fitness function. This algorithm has the same behavior as the sequential one, although its computational efficiency is improved, especially for time-consuming objective functions.
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A source-to-source translator, source-to-source compiler (S2S compiler), transcompiler, or transpiler is a type of translator that takes the source code of a program written in a programming language as its input and produces an equivalent source code in the same or a different programming language. A source-to-source translator converts between programming languages that operate at approximately the same level of abstraction, while a traditional compiler translates from a higher level programming language to a lower level programming language. For example, a source-to-source compiler may perform a translation of a program from Python to JavaScript, while a traditional compiler translates from a language like C to assembler or Java to bytecode. An automatic parallelizing compiler will frequently take in a high level language program as an input and then transform the code and annotate it with parallel code annotations (e.g.
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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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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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