On a geopolitical front, China controls the majority of rare earth metal deposits, which are integral to the production of technology, and has cut exports by 72 per cent in recent years, raising economic and geopolitical tensions.
There are also numerous human rights issues through the entire life cycle of these devices from resource extraction to assembly and finally disposal. Vast amounts of information are constantly at our fingertips, yet it seems we still lack the wisdom to healthily limit our dependence on technology — not just for our own sake, but for the health of our society and environment as well. Sasha Boutilier is a second-year student at St. Perhaps most importantly, however, is the frequency with which we use online platforms like Facebook and Twitter to socialize.
An article by The Atlantic even went so far as to suggest that social media makes us lonely, leading to mental and physical illnesses. Students are indeed dependent on technology — I, for one, check my email and social media before I get out of bed everyday. However, it is misguided and reductive to assume this use of technology is inherently detrimental. Social media is just another method of communication that we will inevitably incorporate into our lives, much in the way that tools like the telephone and email have been assimilated.
I have admittedly wasted hours calculating social capital via Facebook likes or Twitter re-tweets. Yet, social media only reflects, rather than creates or exacerbates, the unavoidable popularity contests of our teenage years. One only has to think of Mean Girls to realize that toxic social relations between students were a pervasive problem long before the widespread use of social media.
So when incessant social competition becomes less important to us, we can use social media in more productive ways. The mass communication with college or course mates through Facebook groups is also incredibly useful for resolving various personal issues that may be out of scope for a registrar or professor. Being a commuter student, Skype and Google Hangouts also provide me with an ideal replacement for in-person meetings.
Perhaps most underrated is the role of social media in providing news and spurring debate. Most notably, conversations concerning sexism, racism, or other types of oppression provide a powerful alternative to the narratives we learn through mainstream media and schools. See, for instance, YesAllWomen, which highlights the magnitude of misogyny and harassment directed at women across the continent. We need to stop condemning social media as merely a facilitator of procrastination or anti-social tendencies. It actually holds enormous potential for improving our daily lives, from strengthening relationships to exposing us to alternative viewpoints.
Students and youth are at the forefront of using social media for these positive gains and deserve credit, not criticism, for doing so. One of the first consistency models was Leslie Lamport 's sequential consistency model. Sequential consistency is the property of a parallel program that its parallel execution produces the same results as a sequential program. Specifically, a program is sequentially consistent if "the results of any execution is the same as if the operations of all the processors were executed in some sequential order, and the operations of each individual processor appear in this sequence in the order specified by its program".
Software transactional memory is a common type of consistency model. Software transactional memory borrows from database theory the concept of atomic transactions and applies them to memory accesses. Mathematically, these models can be represented in several ways. Introduced in , Petri nets were an early attempt to codify the rules of consistency models.
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Dataflow theory later built upon these, and Dataflow architectures were created to physically implement the ideas of dataflow theory. Beginning in the late s, process calculi such as Calculus of Communicating Systems and Communicating Sequential Processes were developed to permit algebraic reasoning about systems composed of interacting components. Michael J. Flynn created one of the earliest classification systems for parallel and sequential computers and programs, now known as Flynn's taxonomy. Flynn classified programs and computers by whether they were operating using a single set or multiple sets of instructions, and whether or not those instructions were using a single set or multiple sets of data.
The single-instruction-single-data SISD classification is equivalent to an entirely sequential program. The single-instruction-multiple-data SIMD classification is analogous to doing the same operation repeatedly over a large data set.
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This is commonly done in signal processing applications. Multiple-instruction-single-data MISD is a rarely used classification. While computer architectures to deal with this were devised such as systolic arrays , few applications that fit this class materialized. Multiple-instruction-multiple-data MIMD programs are by far the most common type of parallel programs. According to David A. Patterson and John L.
Hennessy , "Some machines are hybrids of these categories, of course, but this classic model has survived because it is simple, easy to understand, and gives a good first approximation. It is also—perhaps because of its understandability—the most widely used scheme. From the advent of very-large-scale integration VLSI computer-chip fabrication technology in the s until about , speed-up in computer architecture was driven by doubling computer word size —the amount of information the processor can manipulate per cycle.
Historically, 4-bit microprocessors were replaced with 8-bit, then bit, then bit microprocessors. This trend generally came to an end with the introduction of bit processors, which has been a standard in general-purpose computing for two decades. Not until the early s, with the advent of x architectures, did bit processors become commonplace. A computer program is, in essence, a stream of instructions executed by a processor.
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These processors are known as subscalar processors. These instructions can be re-ordered and combined into groups which are then executed in parallel without changing the result of the program. This is known as instruction-level parallelism. Advances in instruction-level parallelism dominated computer architecture from the mids until the mids. All modern processors have multi-stage instruction pipelines. These processors are known as scalar processors. The Pentium 4 processor had a stage pipeline. Most modern processors also have multiple execution units.
These processors are known as superscalar processors. Instructions can be grouped together only if there is no data dependency between them. Scoreboarding and the Tomasulo algorithm which is similar to scoreboarding but makes use of register renaming are two of the most common techniques for implementing out-of-order execution and instruction-level parallelism.
Task parallelisms is the characteristic of a parallel program that "entirely different calculations can be performed on either the same or different sets of data". Task parallelism involves the decomposition of a task into sub-tasks and then allocating each sub-task to a processor for execution. The processors would then execute these sub-tasks concurrently and often cooperatively. Task parallelism does not usually scale with the size of a problem. Main memory in a parallel computer is either shared memory shared between all processing elements in a single address space , or distributed memory in which each processing element has its own local address space.
Distributed shared memory and memory virtualization combine the two approaches, where the processing element has its own local memory and access to the memory on non-local processors. Accesses to local memory are typically faster than accesses to non-local memory. On the supercomputers , distributed shared memory space can be implemented using the programming model such as PGAS. This model allows processes on one compute node to transparently access the remote memory of another compute node.
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Computer architectures in which each element of main memory can be accessed with equal latency and bandwidth are known as uniform memory access UMA systems. Typically, that can be achieved only by a shared memory system, in which the memory is not physically distributed. A system that does not have this property is known as a non-uniform memory access NUMA architecture.
Distributed memory systems have non-uniform memory access. Computer systems make use of caches —small and fast memories located close to the processor which store temporary copies of memory values nearby in both the physical and logical sense. Parallel computer systems have difficulties with caches that may store the same value in more than one location, with the possibility of incorrect program execution.
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These computers require a cache coherency system, which keeps track of cached values and strategically purges them, thus ensuring correct program execution. Bus snooping is one of the most common methods for keeping track of which values are being accessed and thus should be purged. Designing large, high-performance cache coherence systems is a very difficult problem in computer architecture.
As a result, shared memory computer architectures do not scale as well as distributed memory systems do. Processor—processor and processor—memory communication can be implemented in hardware in several ways, including via shared either multiported or multiplexed memory, a crossbar switch , a shared bus or an interconnect network of a myriad of topologies including star , ring , tree , hypercube , fat hypercube a hypercube with more than one processor at a node , or n-dimensional mesh.
Parallel computers based on interconnected networks need to have some kind of routing to enable the passing of messages between nodes that are not directly connected. The medium used for communication between the processors is likely to be hierarchical in large multiprocessor machines. Parallel computers can be roughly classified according to the level at which the hardware supports parallelism.
This classification is broadly analogous to the distance between basic computing nodes. These are not mutually exclusive; for example, clusters of symmetric multiprocessors are relatively common. A multi-core processor is a processor that includes multiple processing units called "cores" on the same chip.