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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 science, distributed memory refers to a multiprocessor computer system in which each processor has its own private memory.[1] Computational tasks can only operate on local data, and if remote data are required, the computational task must communicate with one or more remote processors. In contrast, a shared memory multiprocessor offers a single memory space used by all processors. Processors do not have to be aware where data resides, except that there may be performance penalties, and that race conditions are to be avoided.
In a distributed memory system there is typically a processor, a memory, and some form of interconnection that allows programs on each processor to interact with each other. The interconnect can be organised with point to point links or separate hardware can provide a switching network. The network topology is a key factor in determining how the multiprocessor machine scales. The links between nodes can be implemented using some standard network protocol (for example Ethernet), using bespoke network links (used in for example the transputer), or using dual-ported memories.
The key issue in programming distributed memory systems is how to distribute the data over the memories. Depending on the problem solved, the data can be distributed statically, or it can be moved through the nodes. Data can be moved on demand, or data can be pushed to the new nodes in advance.
As an example, if a problem can be described as a pipeline where data x is processed subsequently through functions f, g, h, etc. (the result is h(g(f(x)))), then this can be expressed as a distributed memory problem where the data is transmitted first to the node that performs f that passes the result onto the second node that computes g, and finally to the third node that computes h. This is also known as systolic computation.
Data can be kept statically in nodes if most computations happen locally, and only changes on edges have to be reported to other nodes. An example of this is simulation where data is modeled using a grid, and each node simulates a small part of the larger grid. On every iteration, nodes inform all neighboring nodes of the new edge data.
Similarly, in distributed shared memory each node of a cluster has access to a large shared memory in addition to each node's limited non-shared private memory.
Distributed shared memory hides the mechanism of communication, it does not hide the latency of communication.
Distributed memory refers to a computing system in which each processor has its memory. Computational tasks efficiently operate with local data, but when remote data is required, the task must communicate (using explicit messages) with remote processors to transfer data. This type of parallel computing is standard on supercomputers equipped with many thousands of computing nodes.
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Source: Wikipedia. Article content is retrieved live through the MediaWiki API.
In computer science, distributed memory refers to a multiprocessor computer system in which each processor has its own private memory. Computational tasks can only operate on local data, and if remote data are required, the computational task must communicate with one or more remote processors. In contrast, a shared memory multiprocessor offers a single memory space used by all processors. Processors do not have to be aware where data resides, except that there may be performance penalties, and that race conditions are to be avoided. In a distributed memory system there is typically a processor, a memory, and some form of interconnection that allows programs on each processor to interact with each other. The interconnect can be organised with point to point links or separate hardware can provide a switching network. The network topology is a key factor in determining how the multiprocessor machine scales. The links between nodes can be implemented using some standard network protocol (for example Ethernet), using bespoke network links (used in for example the transputer), or using dual-ported memories.
Sparse distributed memory (SDM) is a mathematical model of human long-term memory introduced by Pentti Kanerva in 1988 while he was at NASA Ames Research Center. This memory exhibits behaviors, both in theory and in experiment, that resemble those previously unapproached by machines – e.g., rapid recognition of faces or odors, discovery of new connections between seemingly unrelated ideas, etc. Sparse distributed memory is used for storing and retrieving large amounts ( 2 1000 {\displaystyle 2^{1000}} bits) of information without focusing on the accuracy but on similarity of information. There are some recent applications in robot navigation and experience-based robot manipulation.
In computer science, distributed shared memory (DSM) is a form of memory architecture where physically separated memories can be addressed as a single shared address space. The term "shared" does not mean that there is a single centralized memory, but that the address space is shared—i.e., the same physical address on two processors refers to the same location in memory. Distributed global address space (DGAS), is a similar term for a wide class of software and hardware implementations, in which each node of a cluster has access to shared memory in addition to each node's private (i.e., not shared) memory.
In computer science, memory virtualization decouples volatile random access memory (RAM) resources from individual systems in the data center, and then aggregates those resources into a virtualized memory pool available to any computer in the cluster. The memory pool is accessed by the operating system or applications running on top of the operating system. The distributed memory pool can then be utilized as a high-speed cache, a messaging layer, or a large, shared memory resource for a CPU or a GPU application.
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.
