Asynchronous data transfer

Load forecasting is essential for the operation and planning of a utility company. Recent large-scale smart meter deployments enabled the collection of fine-grained load data and created opportunities for sensor-based load forecasting. has achieved great successes in load forecasting; however, conventional Introduction According to the United States Energy Information Administration (EIA), global electricity consumption continues to outpace global population growth [1]. At the same time, countries are setting aggressive greenhouse gas emissions targets; European Union, for example, aims to reduce emissions for 40% and increase energy efficiency for 27% by year 2030 [2]. Renewable energy resources are expanding quickly; however, their intermittent nature and variability pose challenges for balancing supply and demand. In a such challenging environment, load forecasting has a critical role in balancing supply and demand, providing information for generation scheduling, grid operation, and infrastructure planning, as well as for transitioning towards a smarter grid. Today, electricity consumption data gathered by widely adopted smart meters provide tremendous information about historical consumption patterns on building and even household level, consequently creating new opportunities for fine-grained load forecasting [3]. Machine learning (ML) approaches have been widely used in load forecasting tasks [4] as they discover relations within data and identify patterns. ML-based...

• Beal, Vangie (September 1, 1996). . Retrieved 2011-04-27. The term asynchronous is usually used to describe communications in which data can be transmitted intermittently rather than in a steady stream. • Hargrave, Frank (2001). Hargrave's Communication Dictionary. Wiley. • Calladine, Richard (2006). "A taxonomy of learning technologies: simplifying online learning for learners, professors, and designers". In Khosrowpour, Mehdi (ed.). Emerging Trends and Challenges in Information Technology Management: 2006 Information Resources Management Association International Conference, Washington, DC, USA, May 21-24, 2006. Vol.1. Idea Group Inc (IGI). p.249. 9781599040196 . Retrieved 2014-09-03. Email and Internet Chat provide a good example of the difference between synchronous and asynchronous technologies. Email is generally responded to at the discretion of the user and hence is described as asynchronous. However, when in a Chat session each participant knows that the others are waiting for their responses. The resulting "conversations" are synchronous [...]

In our CUDA Streams A stream in CUDA is a sequence of operations that execute on the device in the order in which they are issued by the host code. While operations within a stream are guaranteed to execute in the prescribed order, operations in different streams can be interleaved and, when possible, they can even run concurrently. The default stream All device operations (kernels and data transfers) in CUDA run in a stream. When no stream is specified, the default stream (also called the “null stream”) is used. The default stream is different from other streams because it is a synchronizing stream with respect to operations on the device: no operation in the default stream will beginuntil all previously issued operations in any stream on the device have completed, and an operation in the default stream must complete before any other operation (in any stream on the device) will begin. Please note that CUDA 7, released in 2015, introduced a new option to use a separate default stream per host thread, and to treat per-thread default streams as regular streams (i.e. they don’t synchronize with operations in other streams). Read more about this new behavior in the post Let’s look at some simple code examples that use the default stream, and discuss how operations progress from the perspective of the host as well as the device. cudaMemcpy(d_a, a, numBytes, cudaMemcpyHostToDevice); increment>>(d_a) cudaMemcpy(a, d_a, numBytes, cudaMemcpyDeviceToHost); In the code above, from th...

By • Technical Features Writer In general, asynchronous -- pronounceday-SIHN-kro-nuhs, from Greek asyn-, meaning "not with," and chronos, meaning "time" -- is an adjective describing objects or events that are not coordinated in time. What does asynchronous mean? More specifically, asynchronous describes the relationship between two or more events/objects that do interact within the same system but do not occur at predetermined intervals and do not necessarily rely on each other's existence to function. They are not coordinated with each other, meaning they could occur simultaneously or not because they have their own separate agenda. Uses The term itself is broad and has many applications. In information technology (IT) alone, the term has several different applications. In most IT contexts, asynchronous refers to a style of communication that can occur both between people and between technological components. Since the term casts such a wide net, even within IT, it can be difficult to reconcile its general definition when comparing its various applied uses. One simple way to understand asynchronous is through the lens of personal communication. Sending an email or text is an example of an asynchronous form of communication because they do not necessarily have to be coordinated in time. Each event or sent message can happen at irregular intervals because the information is stored for whenever the recipient chooses to access it. Events happen independently of each other an...

This post walks through how to use the asynchronous copy feature, and how to set up your algorithms to overlap asynchronous copies with computations. Applications stage data through shared memory Applications with large data and computational intensity on that data are accelerated by copying data from global to shared memory, performing computations on data in shared memory, and copying results back to global memory. • Linear algebra algorithms copy a submatrix of a global matrix to shared memory, factorize the submatrix while in shared memory, and then copy the updated submatrix back into the global matrix. • Finite difference algorithms copy a subgrid of a global grid into shared memory, compute with that subgrid, and then copy the subgrid back into the global grid. Asynchronous data movement You’ve long had the ability to asynchronously copy data between CPU memory and GPU global memory using cudaMemcpyAsync. For more information, see the Developer Blog post from 2012, cuda::memcpy_async and not tie up threads to shepherd data movement. These asynchronous data movement features enable you to overlap computations with data movement and reduce total execution time. With cudaMemcpyAsync, data movement between CPU memory and GPU global memory can be overlapped with kernel execution. With cuda::memcpy_async, data movement from GPU global memory to shared memory can be overlapped with thread execution. A better journey through the memory hierarchy Prior to cuda::memcpy_async,...

What Does Asynchronous Data Mean? Asynchronous data is data that is not synchronized when it is sent or received. In this type of transmission, signals are sent between the computers and external systems or vice versa in an asynchronous manner. This usually refers to data that is transmitted at intermittent intervals rather than in a steady stream, which means that the first parts of the complete file might not always be the first to be sent and arrive at the destination. Different parts of the complete data are sent in different intervals, sometimes simultaneously, but follow different paths toward the destination. The transfer of asynchronous data doesn’t require the coordination or timing of bits between the two endpoints. Techopedia Explains Asynchronous Data The transmission of asynchronous data is not prompted by a clock signal when sending the data to the receiver, unlike in synchronous methods, where sending data is measured against a time reference. Compared to synchronous transmission, asynchronous communication has a few advantages: • It is more flexible and devices can exchange information at their own pace. Individual data characters can complete themselves so that even if one packet is corrupted, its predecessors and successors will not be affected. • It does not require complex processes by the receiving device. This means that an inconsistency in the transmission of data does not result in a big crisis, since the device can keep up with the data stream. Thi...

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