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In this paper, we study computation offloading by using edge computing, which is a new paradigm to deliver computation to the edge of pervasive networks nearby mobile users.
Our experiments using kernels from a popular phylogenetic inference application suite show that we can deliver computation throughput over 1011 operations per second, consuming ∼0.5 nJ per operation, while ensuring that on-chip temperature variation is within 26 °C.
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At present, a promising solution to address this challenge is cloud computing [ 3- 5], which exploits the full potential of multiple computers and delivers computation and storage as dynamically allocated virtual resources via the Internet [ 6].
"This huge explosion of data is driving a movement to design systems around workloads because it is the only way to deliver the computation needed, and it's far more energy-efficient," said Kunle Olukotun, a computer scientist at Stanford.
These new applications are challenging the industry to deliver much more computation capability at an improved affordability level.
With increasing workloads, these many-core platforms need to deliver high overall computation throughput while also being energy-efficient.
CFD results implementing a multi-fluid model with k ε turbulence and special momentum exchange terms for direct gas solid interactions show that, even on standard PC workstations, this kind of computations can deliver qualitatively reasonable agreement with measurements.
Although high complexity dense disparity calculation algorithms deliver more accurate disparity results, speed of computation is desirable in many settings, e.g., on real-time 3D videos.
This paper identifies the impact of computation delivered by authentic learning experiences in the form of anchored instruction on students' self-beliefs and their capacity to leverage computation to acquire disciplinary concepts in subsequent computationally-based engineering coursework.
The performance-per-watt is defined by the rate of computation delivered by the processor under test for every watt of power consumed.
The main motivation behind this study was to develop a self-adaptive model management strategy for surrogate-assisted algorithms in such a way that the same quality results, as those obtained by the corresponding unassisted (typical) algorithm, are delivered with less computation.
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