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Explore the people, places, events, achievements, struggles and stories that shaped our journey.
Movements, leaders, victories and the continuing fight for equality.
Innovation, patents, science, technology and world-changing contributions.
Pioneers, champions, Negro Leagues, records, activism and excellence.
Meet the people whose lives, choices and achievements shaped the journey.
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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
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In computer programming, explicit parallelism is the representation of concurrent computations using primitives in the form of operators, function calls or special-purpose directives.[1] Most parallel primitives are related to process synchronization, communication and process partitioning.[2] As they rarely contribute to actually carry out the intended computation of the program but, rather, structure it, their computational cost is often considered as overhead.
The advantage of explicit parallel programming is increased programmer control over the computation. A skilled parallel programmer may take advantage of explicit parallelism to produce efficient code for a given target computation environment. However, programming with explicit parallelism is often difficult, especially for non-computing specialists, because of the extra work and skill involved in developing it.
In some instances, explicit parallelism may be avoided with the use of an optimizing compiler or runtime that automatically deduces the parallelism inherent to computations, known as implicit parallelism.
Some of the programming languages that support explicit parallelism are:
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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. As power consumption (and consequently heat generation) by computers has become a concern in recent years, parallel computing has become the dominant paradigm in computer architecture, mainly in the form of multi-core processors. In computer science, parallelism and concurrency are two different things: a parallel program uses multiple CPU cores, each core performing a task independently. On the other hand, concurrency enables a program to deal with multiple tasks even on a single CPU core; the core switches between tasks (i.e. threads) without necessarily completing each one. A program can have both, neither or a combination of parallelism and concurrency characteristics. Parallel computers can be roughly classified according to the level at which the hardware supports parallelism, with multi-core and multi-processor computers having multiple processing elements within a single machine, while clusters, MPPs, and grids use multiple computers to work on the same task. Specialized parallel computer architectures are sometimes used alongside traditional processors, for accelerating specific tasks. In some cases parallelism is transparent to the programmer, such as in bit-level or instruction-level parallelism, but explicitly parallel algorithms, particularly those that use concurrency, are more difficult to write than sequential ones, because concurrency introduces several new classes of potential software bugs, of which race conditions are the most common. Communication and synchronization between the different subtasks are typically some of the greatest obstacles to getting optimal parallel program performance. A theoretical upper bound on the speed-up of a single program as a result of parallelization is given by Amdahl's law, which states that it is limited by the fraction of time for which the parallelization can be utilised.
Data parallelism is parallelization across multiple processors in parallel computing environments. It focuses on distributing the data across different nodes, which operate on the data in parallel. It can be applied on regular data structures like arrays and matrices by working on each element in parallel. It contrasts to task parallelism as another form of parallelism. A data parallel job on an array of n elements can be divided equally among all the processors. Let us assume we want to sum all the elements of the given array and the time for a single addition operation is Ta time units. In the case of sequential execution, the time taken by the process will be n×Ta time units as it sums up all the elements of an array. On the other hand, if we execute this job as a data parallel job on 4 processors the time taken would reduce to (n/4)×Ta + merging overhead time units. Parallel execution results in a speedup of 4 over sequential execution. The locality of data references plays an important part in evaluating the performance of a data parallel programming model. Locality of data depends on the memory accesses performed by the program as well as the size of the cache.
Parallelism may refer to: Angle of parallelism, in hyperbolic geometry, the angle at one vertex of a right hyperbolic triangle that has two hyperparallel sides Axial parallelism, a type of motion characteristic of a gyroscope and astronomical bodies Conscious parallelism or also tacit parallelism, price-fixing between competitors that occurs without any communication between the parties Parallel computing, the simultaneous execution on multiple processors of different parts of a program In the analysis of parallel algorithms, the maximum possible speedup of a computation Parallel evolution, the independent emergence of a similar trait in different unrelated species Parallel (geometry), the property of parallel lines Parallelism (grammar), a balance of two or more similar words, phrases, or clauses Parallelism (rhetoric), the use of similar ideas or phrases for rhetorical effect Psychophysical parallelism, the theory that the conscious and nervous processes vary concomitantly Parallel harmony/doubling, or harmonic parallelism, in music
In computer programming, explicit parallelism is the representation of concurrent computations using primitives in the form of operators, function calls or special-purpose directives. Most parallel primitives are related to process synchronization, communication and process partitioning. As they rarely contribute to actually carry out the intended computation of the program but, rather, structure it, their computational cost is often considered as overhead. The advantage of explicit parallel programming is increased programmer control over the computation. A skilled parallel programmer may take advantage of explicit parallelism to produce efficient code for a given target computation environment. However, programming with explicit parallelism is often difficult, especially for non-computing specialists, because of the extra work and skill involved in developing it. In some instances, explicit parallelism may be avoided with the use of an optimizing compiler or runtime that automatically deduces the parallelism inherent to computations, known as implicit parallelism.
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 →Brown v. Board of Education in 1954.
