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In 1968 Shirley Chisholm became the first Black woman elected to the United States Congress. In 1972 she launched a campaign for the Democratic presidential nomination, breaking another political barrier.
MORE →Reflects the personal views, recollections, and perspective of the author, Mike Davis.
This is a personal recollection on the Move fire on May 13, 1985
In parallel computing, an embarrassingly parallel workload or problem (also called embarrassingly parallelizable, perfectly parallel, delightfully parallel or pleasingly parallel) is one where little or no effort is needed to split the problem into a number of parallel tasks.[1] This is due to minimal or no dependency upon communication between the parallel tasks, or for results between them.[2]
These differ from distributed computing problems, which need communication between tasks, especially communication of intermediate results. They are easier to perform on server farms which lack the special infrastructure used in a true supercomputer cluster. They are well-suited to large, Internet-based volunteer computing platforms such as BOINC, and suffer less from parallel slowdown. The opposite of embarrassingly parallel problems are inherently serial problems, which cannot be parallelized at all.
A common example of an embarrassingly parallel problem is 3D video rendering handled by a graphics processing unit, where each frame (forward method) or pixel (ray tracing method) can be handled with no interdependency.[3] Some forms of password cracking are another embarrassingly parallel task that is easily distributed on central processing units, CPU cores, or clusters.
"Embarrassingly" is used here to refer to parallelization problems which are "embarrassingly easy".[4] The term may imply embarrassment on the part of developers or compilers: "Because so many important problems remain unsolved mainly due to their intrinsic computational complexity, it would be embarrassing not to develop parallel implementations of polynomial homotopy continuation methods."[5] The term is first found in the literature in a 1986 book on multiprocessors by MATLAB's creator Cleve Moler,[6] who claims to have invented the term.[7]
An alternative term, pleasingly parallel, has gained some use, perhaps to avoid the negative connotations of embarrassment in favor of a positive reflection on the parallelizability of the problems: "Of course, there is nothing embarrassing about these programs at all."[8]
A trivial example involves serving static data. It would take very little effort to have many processing units produce the same set of bits. Indeed, the famous Hello World problem could easily be parallelized with few programming considerations or computational costs.
Some examples of embarrassingly parallel problems include:
Some computational problems are "embarrassingly parallel": they can easily be divided into components that can be executed concurrently.
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Source: Wikipedia. Article content is retrieved live through the MediaWiki API.
In parallel computing, an embarrassingly parallel workload or problem (also called embarrassingly parallelizable, perfectly parallel, delightfully parallel or pleasingly parallel) is one where little or no effort is needed to split the problem into a number of parallel tasks. This is due to minimal or no dependency upon communication between the parallel tasks, or for results between them. These differ from distributed computing problems, which need communication between tasks, especially communication of intermediate results. They are easier to perform on server farms which lack the special infrastructure used in a true supercomputer cluster. They are well-suited to large, Internet-based volunteer computing platforms such as BOINC, and suffer less from parallel slowdown. The opposite of embarrassingly parallel problems are inherently serial problems, which cannot be parallelized at all. A common example of an embarrassingly parallel problem is 3D video rendering handled by a graphics processing unit, where each frame (forward method) or pixel (ray tracing method) can be handled with no interdependency. Some forms of password cracking are another embarrassingly parallel task that is easily distributed on central processing units, CPU cores, or clusters.
Map is an idiom in parallel computing where a simple operation is applied to all elements of a sequence, potentially in parallel. It is used to solve embarrassingly parallel problems: those problems that can be decomposed into independent subtasks, requiring no communication/synchronization between the subtasks except a join or barrier at the end. When applying the map pattern, one formulates an elemental function that captures the operation to be performed on a data item that represents a part of the problem, then applies this elemental function in one or more threads of execution, hyperthreads, SIMD lanes or on multiple computers. Some parallel programming systems, such as OpenMP and Cilk, have language support for the map pattern in the form of a parallel for loop; languages such as OpenCL and CUDA support elemental functions (as "kernels") at the language level. The map pattern is typically combined with other parallel design patterns. For example, map combined with category reduction gives the MapReduce pattern.
In computer graphics, a shader is a programmable operation which is applied to data as it moves through the rendering pipeline. Shaders act on data such as vertices and primitives, generate or morph geometries and fragments, and calculate the colors in a rendered image. Shaders can execute a wide variety of operations. In modern real-time computer graphics, shaders are run on graphics processing units (GPUs) - dedicated hardware which provide parallel execution of programs. Because image rendering is an embarrassingly parallel problem (having an Amdahl's index of one), shaders benefit greatly from parallel computing, such as SIMD hardware. The drive for faster rendering has produced highly parallel processors, like GPUs, which are now used for general-purpose computing. Shaders used for general purposes are commonly called compute shaders.
A multi-core processor (MCP) is a microprocessor on a single integrated circuit (IC) with two or more separate central processing units (CPUs), called cores to emphasize their multiplicity (for example, dual-core or quad-core). Each core reads and executes program instructions, specifically ordinary CPU instructions (such as add, move data, and branch). However, the MCP can run instructions on separate cores at the same time, increasing overall speed for programs that support multithreading or other parallel computing techniques. Manufacturers typically integrate the cores onto a single IC die, known as a chip multiprocessor (CMP), or onto multiple dies in a single chip package. As of 2024, the microprocessors used in almost all new personal computers are multi-core. A multi-core processor implements multiprocessing in a single physical package. Designers may couple cores in a multi-core device tightly or loosely. For example, cores may or may not share caches, and they may implement message passing or shared-memory inter-core communication methods. Common network topologies used to interconnect cores include bus, ring, two-dimensional mesh, and crossbar. Homogeneous multi-core systems include only identical cores; heterogeneous multi-core systems have cores that are not identical (e.g. big.LITTLE have heterogeneous cores that share the same instruction set, while AMD Accelerated Processing Units have cores that do not share the same instruction set). Just as with single-processor systems, cores in multi-core systems may implement architectures such as VLIW, superscalar, vector, or multithreading. Multi-core processors are widely used across many application domains, including general-purpose, embedded, network, digital signal processing (DSP), and graphics (GPU). Core count goes up to even dozens, and for specialized chips over 10,000, and in supercomputers (i.e. clusters of chips) the count can go over 10 million (and in one case up to 20 million processing elements total in addition to host processors). The improvement in performance gained by the use of a multi-core processor depends very much on the software algorithms used and their implementation. In particular, possible gains are limited by the fraction of the software that can run in parallel simultaneously on multiple cores; this effect is described by Amdahl's law. In the best case, so-called embarrassingly parallel problems may realize speedup factors near the number of cores, or even more if the problem is split up enough to fit within each core's cache(s), avoiding use of much slower main-system memory. Most applications, however, are not accelerated as much unless programmers invest effort in refactoring. The parallelization of software is a significant ongoing topic of research. Cointegration of multiprocessor applications provides flexibility in network architecture design. Adaptability within parallel models is an additional feature of systems utilizing these protocols. In the consumer market, dual-core processors (that is, microprocessors with two units) started becoming commonplace on personal computers in the late 2000s. In the early 2010s, quad-core processors were also being adopted in that era for higher-end systems before becoming standard by the mid 2010s. In the late 2010s, hexa-core (six cores) started entering the mainstream and since the early 2020s has overtaken quad-core in many spaces.
Before the 1921 destruction of Tulsa’s Greenwood District, Black residents had created a remarkable center of business and community life. The district included stores, professional offices, entertainment venues and homes owned by Black citizens. Understanding Greenwood means learning what was built—not only what was burned.
MORE →Joe Louis.
