Halide (programming Language)
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Halide (programming Language)
Halide is a computer programming language designed for writing digital image processing code that takes advantage of memory locality, vectorized computation and multi-core CPUs and GPUs. Halide is implemented as an internal domain-specific language (DSL) in C++. Language The main innovation Halide brings is the separation of the algorithm being implemented from its execution schedule, i.e. code specifying the loop nesting, parallelization, loop unrolling and vector instruction. These two are usually interleaved together and experimenting with changing the schedule requires the programmer to rewrite large portions of the algorithm with every change. With Halide, changing the schedule does not require any changes to the algorithm and this allows the programmer to experiment with scheduling and finding the most efficient one. Sample source code The following function defines and sets the schedule for a 3×3 box filter defined as a series of two 3×1 passes: Func blur_3x3( ...
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Functional Programming
In computer science, functional programming is a programming paradigm where programs are constructed by Function application, applying and Function composition (computer science), composing Function (computer science), functions. It is a declarative programming paradigm in which function definitions are Tree (data structure), trees of Expression (computer science), expressions that map Value (computer science), values to other values, rather than a sequence of Imperative programming, imperative Statement (computer science), statements which update the State (computer science), running state of the program. In functional programming, functions are treated as first-class citizens, meaning that they can be bound to names (including local Identifier (computer languages), identifiers), passed as Parameter (computer programming), arguments, and Return value, returned from other functions, just as any other data type can. This allows programs to be written in a Declarative programming, ...
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Loop (computing)
In computer science, control flow (or flow of control) is the order in which individual statements, instructions or function calls of an imperative program are executed or evaluated. The emphasis on explicit control flow distinguishes an ''imperative programming'' language from a ''declarative programming'' language. Within an imperative programming language, a ''control flow statement'' is a statement that results in a choice being made as to which of two or more paths to follow. For non-strict functional languages, functions and language constructs exist to achieve the same result, but they are usually not termed control flow statements. A set of statements is in turn generally structured as a block, which in addition to grouping, also defines a lexical scope. Interrupts and signals are low-level mechanisms that can alter the flow of control in a way similar to a subroutine, but usually occur as a response to some external stimulus or event (that can occur asynchronously), ...
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Image Processing
An image is a visual representation of something. It can be two-dimensional, three-dimensional, or somehow otherwise feed into the visual system to convey information. An image can be an artifact, such as a photograph or other two-dimensional picture, that resembles a subject. In the context of signal processing, an image is a distributed amplitude of color(s). In optics, the term “image” may refer specifically to a 2D image. An image does not have to use the entire visual system to be a visual representation. A popular example of this is of a greyscale image, which uses the visual system's sensitivity to brightness across all wavelengths, without taking into account different colors. A black and white visual representation of something is still an image, even though it does not make full use of the visual system's capabilities. Images are typically still, but in some cases can be moving or animated. Characteristics Images may be two or three-dimensional, such as a pho ...
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Algorithmic Skeleton
In computing, algorithmic skeletons, or parallelism patterns, are a high-level parallel programming model for parallel and distributed computing. Algorithmic skeletons take advantage of common programming patterns to hide the complexity of parallel and distributed applications. Starting from a basic set of patterns (skeletons), more complex patterns can be built by combining the basic ones. Overview The most outstanding feature of algorithmic skeletons, which differentiates them from other high-level parallel programming models, is that orchestration and synchronization of the parallel activities is implicitly defined by the skeleton patterns. Programmers do not have to specify the synchronizations between the application's sequential parts. This yields two implications. First, as the communication/data access patterns are known in advance, cost models can be applied to schedule skeletons programs. Second, that algorithmic skeleton programming reduces the number of errors when com ...
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Cuneiform (programming Language)
Cuneiform is an open-source workflow language for large-scale scientific data analysis. It is a statically typed functional programming language promoting parallel computing. It features a versatile foreign function interface allowing users to integrate software from many external programming languages. At the organizational level Cuneiform provides facilities like conditional branching and general recursion making it Turing-complete. In this, Cuneiform is the attempt to close the gap between scientific workflow systems like Taverna, KNIME, or Galaxy and large-scale data analysis programming models like MapReduce or Pig Latin while offering the generality of a functional programming language. Cuneiform is implemented in distributed Erlang. If run in distributed mode it drives a POSIX-compliant distributed file system like Gluster or Ceph (or a FUSE integration of some other file system, e.g., HDFS). Alternatively, Cuneiform scripts can be executed on top of HTCondor or Hadoop ...
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The Register
''The Register'' is a British technology news website co-founded in 1994 by Mike Magee, John Lettice and Ross Alderson. The online newspaper's masthead sublogo is "''Biting the hand that feeds IT''." Their primary focus is information technology news and opinions. Situation Publishing Ltd is listed as the site's publisher. Drew Cullen is an owner and Linus Birtles is the managing director. Andrew Orlowski was the executive editor before leaving the website in May 2019. History ''The Register'' was founded in London as an email newsletter called ''Chip Connection''. In 1998 ''The Register'' became a daily online news source. Magee left in 2001 to start competing publications ''The Inquirer'', and later the ''IT Examiner'' and ''TechEye''.Walsh, Bob (2007). ''Clear Blogging: How People Blogging Are Changing the World and How You Can Join Them.'' Apress, In 2002, ''The Register'' expanded to have a presence in London and San Francisco, creating ''The Register USA'' at ther ...
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Pixel Visual Core
The Pixel Visual Core (PVC) is a series of ARM-based system in package (SiP) image processors designed by Google. The PVC is a fully programmable image, vision and AI multi-core domain-specific architecture (DSA) for mobile devices and in future for IoT. It first appeared in the Google Pixel 2 and 2 XL which were introduced on October 19, 2017. It has also appeared in the Google Pixel 3 and 3 XL. Starting with the Pixel 4, this chip was replaced with the Pixel Neural Core. History Google previously used Qualcomm Snapdragon's CPU, GPU, IPU, and DSP to handle its image processing for their Google Nexus and Google Pixel devices. With the increasing importance of computational photography techniques, Google developed the Pixel Visual Core (PVC). Google claims the PVC uses less power than using CPU and GPU while still being fully programmable, unlike their tensor processing unit (TPU) application-specific integrated circuit (ASIC). Indeed, classical mobile devices eq ...
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TensorFlow
TensorFlow is a free and open-source software library for machine learning and artificial intelligence. It can be used across a range of tasks but has a particular focus on training and inference of deep neural networks. "It is machine learning software being used for various kinds of perceptual and language understanding tasks" – Jeffrey Dean, minute 0:47 / 2:17 from YouTube clip TensorFlow was developed by the Google Brain team for internal Google use in research and production. The initial version was released under the Apache License 2.0 in 2015. Google released the updated version of TensorFlow, named TensorFlow 2.0, in September 2019. TensorFlow can be used in a wide variety of programming languages, including Python, JavaScript, C++, and Java. This flexibility lends itself to a range of applications in many different sectors. History DistBelief Starting in 2011, Google Brain built DistBelief as a proprietary machine learning system based on deep learning neural n ...
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Box Blur
A box blur (also known as a box linear filter) is a spatial domain linear filter in which each pixel in the resulting image has a value equal to the average value of its neighboring pixels in the input image. It is a form of low-pass ("blurring") filter. A 3 by 3 box blur ("radius 1") can be written as matrix :\frac\begin 1 & 1 & 1 \\ 1 & 1 & 1 \\ 1 & 1 & 1 \end. Due to its property of using equal weights, it can be implemented using a much simpler accumulation algorithm, which is significantly faster than using a sliding-window algorithm. Box blurs are frequently used to approximate a Gaussian blur. By the central limit theorem, repeated application of a box blur will approximate a Gaussian blur.code doc In the frequency domain, a box blur has zeros and negative components. That is, a sine wave with a period equal to the size of the box will be blurred away entirely, and wavelengths shorter than the size of the box may be phase-reversed, as seen when two bokeh circles touch to f ...
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Vector Processor
In computing, a vector processor or array processor is a central processing unit (CPU) that implements an instruction set where its instructions are designed to operate efficiently and effectively on large one-dimensional arrays of data called ''vectors''. This is in contrast to scalar processors, whose instructions operate on single data items only, and in contrast to some of those same scalar processors having additional single instruction, multiple data (SIMD) or SWAR Arithmetic Units. Vector processors can greatly improve performance on certain workloads, notably numerical simulation and similar tasks. Vector processing techniques also operate in video-game console hardware and in graphics accelerators. Vector machines appeared in the early 1970s and dominated supercomputer design through the 1970s into the 1990s, notably the various Cray platforms. The rapid fall in the price-to-performance ratio of conventional microprocessor designs led to a decline in vector supercom ...
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Loop Unrolling
Loop unrolling, also known as loop unwinding, is a loop transformation technique that attempts to optimize a program's execution speed at the expense of its binary size, which is an approach known as space–time tradeoff. The transformation can be undertaken manually by the programmer or by an optimizing compiler. On modern processors, loop unrolling is often counterproductive, as the increased code size can cause more cache misses; ''cf.'' Duff's device. The goal of loop unwinding is to increase a program's speed by reducing or eliminating instructions that control the loop, such as pointer arithmetic and "end of loop" tests on each iteration; reducing branch penalties; as well as hiding latencies, including the delay in reading data from memory. To eliminate this computational overhead, loops can be re-written as a repeated sequence of similar independent statements. Loop unrolling is also part of certain formal verification techniques, in particular bounded model checking. ...
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Parallelization
Parallel computing is a type of computation in which many calculations or processes are carried out simultaneously. Large problems can often be divided into smaller ones, which can then be solved at the same time. There are several different forms of parallel computing: bit-level, instruction-level, data, and task parallelism. Parallelism has long been employed in high-performance computing, but has gained broader interest due to the physical constraints preventing frequency scaling.S.V. Adve ''et al.'' (November 2008)"Parallel Computing Research at Illinois: The UPCRC Agenda" (PDF). Parallel@Illinois, University of Illinois at Urbana-Champaign. "The main techniques for these performance benefits—increased clock frequency and smarter but increasingly complex architectures—are now hitting the so-called power wall. The computer industry has accepted that future performance increases must largely come from increasing the number of processors (or cores) on a die, rather than m ...
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