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1 tems in approximately one second on a single graphics processing unit.
2 ut losing calculation precision on an NVIDIA graphics processing unit.
3 method, we parallelized the computation on a graphics processing unit.
4 -time data processing being accelerated by a graphics-processing unit.
5 fits regularized regression across multiple Graphics Processing Units.
7 mpute Unified Device Architecture-compatible graphics processing units and deep learning techniques s
9 to parallel computing architectures, such as graphics processing units by illustrating its utility fo
11 we demonstrate that parallel computation on graphics processing units can reduce the processing time
13 es at their disposal, and recent advances in graphics processing unit (GPU) computing have added a pr
18 multicore architecture of a modern consumer graphics processing unit (GPU), we report a 92x increase
19 arallelism present in modern traditional and graphics processing unit (GPU)-accelerated machines, fro
21 cence lifetimes, Stokes shifts, and extended graphics processing unit (GPU)-based quantum mechanics/m
23 it is now possible to exploit the power of a graphics-processing unit (GPU) from a browser without an
24 hics hardware, a crucial feature that allows graphics-processing-unit (GPU)-based processing for inte
27 new software tool STOCHSIMGPU that exploits graphics processing units (GPUs) for parallel stochastic
30 r arbitrary molecular evolutionary models on graphics processing units (GPUs), making use of the larg
33 ning read-outs of molecular states that uses graphics processing units made from molecular circuits.
34 oth in the central processing unit (CPU) and Graphics processing unit processor markets, enabling mas
36 Implementation of these algorithms for the graphics processing unit results in dramatic speedup of
37 ness the computational power of NVIDIA GPUs (Graphics Processing Units) to greatly reduce processing
38 tic model: the Bayesian network algorithm, a graphics processing unit version of the Bayesian network
39 rallel computational power of a programmable graphics processing unit with the flexibility of the dyn
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