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Movements, leaders, victories and the continuing fight for equality.
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Katherine Johnson’s mathematical calculations helped guide some of America’s most important early space missions while she confronted the racial and gender barriers faced by Black women in twentieth-century America.
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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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.
In computer science, implicit parallelism is a characteristic of a programming language that allows a compiler or interpreter to automatically exploit the parallelism inherent to the computations expressed by some of the language's constructs. A pure implicitly parallel language does not need special directives, operators or functions to enable parallel execution, in contrast to explicit parallelism. Programming languages with implicit parallelism include Axum, Binary Modular Dataflow Machine (BMDFM), High Performance Fortran (HPF), Id, LabVIEW, MATLAB M-code, NESL, Single Assignment C (SAC), Streams and Iteration in a Single Assignment Language (SISAL), Z-level programming language (ZPL), and pH.
Explicit data graph execution, or EDGE, is a type of instruction set architecture (ISA) which intends to improve computing performance compared to common processors like the Intel x86 line. EDGE combines many individual instructions into a larger group known as a "hyperblock". Hyperblocks are designed to be able to easily run in parallel. Parallelism of modern CPU designs generally starts to plateau at about eight internal units and from one to four "cores", EDGE designs intend to support hundreds of internal units and offer processing speeds hundreds of times greater than existing designs. Major development of the EDGE concept had been led by the University of Texas at Austin under DARPA's Polymorphous Computing Architectures program, with the stated goal of producing a single-chip CPU design with 1 TFLOPS performance by 2012, which has yet to be realized as of 2018.
Software exhibits scalable parallelism if it can make use of added processors to solve larger problems, i.e., this term refers to software for which Gustafson's law holds. Consider a program which execution time is dominated by one or more loops, each of which updates every element of an array. For example, the following finite difference heat equation stencil calculation: for t := 0 to T do for i := 1 to N-1 do new(i) := (A(i-1) + A(i) + A(i) + A(i+1)) * .25 // explicit forward-difference with R = 0.25 end for i := 1 to N-1 do A(i) := new(i) end end In the above source code, all iterations of each "i" loop can be executed concurrently, i.e., turn each into a parallel loop. In such cases, it is often possible to make effective use of twice as many processors for a problem of array size 2N as for a problem of array size N. As in this example, scalable parallelism is typically a form of data parallelism. This form of parallelism is often the target of automatic parallelization of loops. Distributed computing systems and non-uniform memory access architectures are typically the most easily scaled to large numbers of processors, and thus would seem a natural target for software that exhibits scalable parallelism. However, applications with scalable parallelism may not have parallelism of sufficiently coarse grain to run effectively on such systems (unless the software is embarrassingly parallel). In the example above, the second "i" loop is embarrassingly parallel, but in the first loop, each iteration requires results produced in several prior iterations. Thus, for the first loop, parallelization may involve extensive communicating or synchronizing among processors, and thus only result in a net speedup if such interactions have very low overhead, or if the code can be transformed to resolve this issue (i.e., by combined scalable locality or scalable parallelism optimization).
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 →The Greenwood District.
